two version of R2R are hereHEADmaster

27 files changed, 5742 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/__init__.py b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/__init__.py
new file mode 100644
index 00000000..b022f129
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/__init__.py
@@ -0,0 +1,57 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from opentelemetry.sdk.metrics._internal import Meter, MeterProvider
+from opentelemetry.sdk.metrics._internal.exceptions import MetricsTimeoutError
+from opentelemetry.sdk.metrics._internal.exemplar import (
+    AlignedHistogramBucketExemplarReservoir,
+    AlwaysOffExemplarFilter,
+    AlwaysOnExemplarFilter,
+    Exemplar,
+    ExemplarFilter,
+    ExemplarReservoir,
+    SimpleFixedSizeExemplarReservoir,
+    TraceBasedExemplarFilter,
+)
+from opentelemetry.sdk.metrics._internal.instrument import (
+    Counter,
+    Histogram,
+    ObservableCounter,
+    ObservableGauge,
+    ObservableUpDownCounter,
+    UpDownCounter,
+)
+from opentelemetry.sdk.metrics._internal.instrument import Gauge as _Gauge
+
+__all__ = [
+    "AlignedHistogramBucketExemplarReservoir",
+    "AlwaysOnExemplarFilter",
+    "AlwaysOffExemplarFilter",
+    "Exemplar",
+    "ExemplarFilter",
+    "ExemplarReservoir",
+    "Meter",
+    "MeterProvider",
+    "MetricsTimeoutError",
+    "Counter",
+    "Histogram",
+    "_Gauge",
+    "ObservableCounter",
+    "ObservableGauge",
+    "ObservableUpDownCounter",
+    "SimpleFixedSizeExemplarReservoir",
+    "UpDownCounter",
+    "TraceBasedExemplarFilter",
+]
diff --git a/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/__init__.py b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/__init__.py
new file mode 100644
index 00000000..faa0959f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/__init__.py
@@ -0,0 +1,582 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import weakref
+from atexit import register, unregister
+from logging import getLogger
+from os import environ
+from threading import Lock
+from time import time_ns
+from typing import Optional, Sequence
+
+# This kind of import is needed to avoid Sphinx errors.
+import opentelemetry.sdk.metrics
+from opentelemetry.metrics import Counter as APICounter
+from opentelemetry.metrics import Histogram as APIHistogram
+from opentelemetry.metrics import Meter as APIMeter
+from opentelemetry.metrics import MeterProvider as APIMeterProvider
+from opentelemetry.metrics import NoOpMeter
+from opentelemetry.metrics import ObservableCounter as APIObservableCounter
+from opentelemetry.metrics import ObservableGauge as APIObservableGauge
+from opentelemetry.metrics import (
+    ObservableUpDownCounter as APIObservableUpDownCounter,
+)
+from opentelemetry.metrics import UpDownCounter as APIUpDownCounter
+from opentelemetry.metrics import _Gauge as APIGauge
+from opentelemetry.sdk.environment_variables import (
+    OTEL_METRICS_EXEMPLAR_FILTER,
+    OTEL_SDK_DISABLED,
+)
+from opentelemetry.sdk.metrics._internal.exceptions import MetricsTimeoutError
+from opentelemetry.sdk.metrics._internal.exemplar import (
+    AlwaysOffExemplarFilter,
+    AlwaysOnExemplarFilter,
+    ExemplarFilter,
+    TraceBasedExemplarFilter,
+)
+from opentelemetry.sdk.metrics._internal.instrument import (
+    _Counter,
+    _Gauge,
+    _Histogram,
+    _ObservableCounter,
+    _ObservableGauge,
+    _ObservableUpDownCounter,
+    _UpDownCounter,
+)
+from opentelemetry.sdk.metrics._internal.measurement_consumer import (
+    MeasurementConsumer,
+    SynchronousMeasurementConsumer,
+)
+from opentelemetry.sdk.metrics._internal.sdk_configuration import (
+    SdkConfiguration,
+)
+from opentelemetry.sdk.resources import Resource
+from opentelemetry.sdk.util.instrumentation import InstrumentationScope
+from opentelemetry.util._once import Once
+from opentelemetry.util.types import (
+    Attributes,
+)
+
+_logger = getLogger(__name__)
+
+
+class Meter(APIMeter):
+    """See `opentelemetry.metrics.Meter`."""
+
+    def __init__(
+        self,
+        instrumentation_scope: InstrumentationScope,
+        measurement_consumer: MeasurementConsumer,
+    ):
+        super().__init__(
+            name=instrumentation_scope.name,
+            version=instrumentation_scope.version,
+            schema_url=instrumentation_scope.schema_url,
+        )
+        self._instrumentation_scope = instrumentation_scope
+        self._measurement_consumer = measurement_consumer
+        self._instrument_id_instrument = {}
+        self._instrument_id_instrument_lock = Lock()
+
+    def create_counter(self, name, unit="", description="") -> APICounter:
+        status = self._register_instrument(name, _Counter, unit, description)
+
+        if status.conflict:
+            # FIXME #2558 go through all views here and check if this
+            # instrument registration conflict can be fixed. If it can be, do
+            # not log the following warning.
+            self._log_instrument_registration_conflict(
+                name,
+                APICounter.__name__,
+                unit,
+                description,
+                status,
+            )
+        if status.already_registered:
+            with self._instrument_id_instrument_lock:
+                return self._instrument_id_instrument[status.instrument_id]
+
+        instrument = _Counter(
+            name,
+            self._instrumentation_scope,
+            self._measurement_consumer,
+            unit,
+            description,
+        )
+
+        with self._instrument_id_instrument_lock:
+            self._instrument_id_instrument[status.instrument_id] = instrument
+            return instrument
+
+    def create_up_down_counter(
+        self, name, unit="", description=""
+    ) -> APIUpDownCounter:
+        status = self._register_instrument(
+            name, _UpDownCounter, unit, description
+        )
+
+        if status.conflict:
+            # FIXME #2558 go through all views here and check if this
+            # instrument registration conflict can be fixed. If it can be, do
+            # not log the following warning.
+            self._log_instrument_registration_conflict(
+                name,
+                APIUpDownCounter.__name__,
+                unit,
+                description,
+                status,
+            )
+        if status.already_registered:
+            with self._instrument_id_instrument_lock:
+                return self._instrument_id_instrument[status.instrument_id]
+
+        instrument = _UpDownCounter(
+            name,
+            self._instrumentation_scope,
+            self._measurement_consumer,
+            unit,
+            description,
+        )
+
+        with self._instrument_id_instrument_lock:
+            self._instrument_id_instrument[status.instrument_id] = instrument
+            return instrument
+
+    def create_observable_counter(
+        self,
+        name,
+        callbacks=None,
+        unit="",
+        description="",
+    ) -> APIObservableCounter:
+        status = self._register_instrument(
+            name, _ObservableCounter, unit, description
+        )
+
+        if status.conflict:
+            # FIXME #2558 go through all views here and check if this
+            # instrument registration conflict can be fixed. If it can be, do
+            # not log the following warning.
+            self._log_instrument_registration_conflict(
+                name,
+                APIObservableCounter.__name__,
+                unit,
+                description,
+                status,
+            )
+        if status.already_registered:
+            with self._instrument_id_instrument_lock:
+                return self._instrument_id_instrument[status.instrument_id]
+
+        instrument = _ObservableCounter(
+            name,
+            self._instrumentation_scope,
+            self._measurement_consumer,
+            callbacks,
+            unit,
+            description,
+        )
+
+        self._measurement_consumer.register_asynchronous_instrument(instrument)
+
+        with self._instrument_id_instrument_lock:
+            self._instrument_id_instrument[status.instrument_id] = instrument
+            return instrument
+
+    def create_histogram(
+        self,
+        name: str,
+        unit: str = "",
+        description: str = "",
+        *,
+        explicit_bucket_boundaries_advisory: Optional[Sequence[float]] = None,
+    ) -> APIHistogram:
+        if explicit_bucket_boundaries_advisory is not None:
+            invalid_advisory = False
+            if isinstance(explicit_bucket_boundaries_advisory, Sequence):
+                try:
+                    invalid_advisory = not (
+                        all(
+                            isinstance(e, (float, int))
+                            for e in explicit_bucket_boundaries_advisory
+                        )
+                    )
+                except (KeyError, TypeError):
+                    invalid_advisory = True
+            else:
+                invalid_advisory = True
+
+            if invalid_advisory:
+                explicit_bucket_boundaries_advisory = None
+                _logger.warning(
+                    "explicit_bucket_boundaries_advisory must be a sequence of numbers"
+                )
+
+        status = self._register_instrument(
+            name,
+            _Histogram,
+            unit,
+            description,
+            explicit_bucket_boundaries_advisory,
+        )
+
+        if status.conflict:
+            # FIXME #2558 go through all views here and check if this
+            # instrument registration conflict can be fixed. If it can be, do
+            # not log the following warning.
+            self._log_instrument_registration_conflict(
+                name,
+                APIHistogram.__name__,
+                unit,
+                description,
+                status,
+            )
+        if status.already_registered:
+            with self._instrument_id_instrument_lock:
+                return self._instrument_id_instrument[status.instrument_id]
+
+        instrument = _Histogram(
+            name,
+            self._instrumentation_scope,
+            self._measurement_consumer,
+            unit,
+            description,
+            explicit_bucket_boundaries_advisory,
+        )
+        with self._instrument_id_instrument_lock:
+            self._instrument_id_instrument[status.instrument_id] = instrument
+            return instrument
+
+    def create_gauge(self, name, unit="", description="") -> APIGauge:
+        status = self._register_instrument(name, _Gauge, unit, description)
+
+        if status.conflict:
+            # FIXME #2558 go through all views here and check if this
+            # instrument registration conflict can be fixed. If it can be, do
+            # not log the following warning.
+            self._log_instrument_registration_conflict(
+                name,
+                APIGauge.__name__,
+                unit,
+                description,
+                status,
+            )
+        if status.already_registered:
+            with self._instrument_id_instrument_lock:
+                return self._instrument_id_instrument[status.instrument_id]
+
+        instrument = _Gauge(
+            name,
+            self._instrumentation_scope,
+            self._measurement_consumer,
+            unit,
+            description,
+        )
+
+        with self._instrument_id_instrument_lock:
+            self._instrument_id_instrument[status.instrument_id] = instrument
+            return instrument
+
+    def create_observable_gauge(
+        self, name, callbacks=None, unit="", description=""
+    ) -> APIObservableGauge:
+        status = self._register_instrument(
+            name, _ObservableGauge, unit, description
+        )
+
+        if status.conflict:
+            # FIXME #2558 go through all views here and check if this
+            # instrument registration conflict can be fixed. If it can be, do
+            # not log the following warning.
+            self._log_instrument_registration_conflict(
+                name,
+                APIObservableGauge.__name__,
+                unit,
+                description,
+                status,
+            )
+        if status.already_registered:
+            with self._instrument_id_instrument_lock:
+                return self._instrument_id_instrument[status.instrument_id]
+
+        instrument = _ObservableGauge(
+            name,
+            self._instrumentation_scope,
+            self._measurement_consumer,
+            callbacks,
+            unit,
+            description,
+        )
+
+        self._measurement_consumer.register_asynchronous_instrument(instrument)
+
+        with self._instrument_id_instrument_lock:
+            self._instrument_id_instrument[status.instrument_id] = instrument
+            return instrument
+
+    def create_observable_up_down_counter(
+        self, name, callbacks=None, unit="", description=""
+    ) -> APIObservableUpDownCounter:
+        status = self._register_instrument(
+            name, _ObservableUpDownCounter, unit, description
+        )
+
+        if status.conflict:
+            # FIXME #2558 go through all views here and check if this
+            # instrument registration conflict can be fixed. If it can be, do
+            # not log the following warning.
+            self._log_instrument_registration_conflict(
+                name,
+                APIObservableUpDownCounter.__name__,
+                unit,
+                description,
+                status,
+            )
+        if status.already_registered:
+            with self._instrument_id_instrument_lock:
+                return self._instrument_id_instrument[status.instrument_id]
+
+        instrument = _ObservableUpDownCounter(
+            name,
+            self._instrumentation_scope,
+            self._measurement_consumer,
+            callbacks,
+            unit,
+            description,
+        )
+
+        self._measurement_consumer.register_asynchronous_instrument(instrument)
+
+        with self._instrument_id_instrument_lock:
+            self._instrument_id_instrument[status.instrument_id] = instrument
+            return instrument
+
+
+def _get_exemplar_filter(exemplar_filter: str) -> ExemplarFilter:
+    if exemplar_filter == "trace_based":
+        return TraceBasedExemplarFilter()
+    if exemplar_filter == "always_on":
+        return AlwaysOnExemplarFilter()
+    if exemplar_filter == "always_off":
+        return AlwaysOffExemplarFilter()
+    msg = f"Unknown exemplar filter '{exemplar_filter}'."
+    raise ValueError(msg)
+
+
+class MeterProvider(APIMeterProvider):
+    r"""See `opentelemetry.metrics.MeterProvider`.
+
+    Args:
+        metric_readers: Register metric readers to collect metrics from the SDK
+            on demand. Each :class:`opentelemetry.sdk.metrics.export.MetricReader` is
+            completely independent and will collect separate streams of
+            metrics. TODO: reference ``PeriodicExportingMetricReader`` usage with push
+            exporters here.
+        resource: The resource representing what the metrics emitted from the SDK pertain to.
+        shutdown_on_exit: If true, registers an `atexit` handler to call
+            `MeterProvider.shutdown`
+        views: The views to configure the metric output the SDK
+
+    By default, instruments which do not match any :class:`opentelemetry.sdk.metrics.view.View` (or if no :class:`opentelemetry.sdk.metrics.view.View`\ s
+    are provided) will report metrics with the default aggregation for the
+    instrument's kind. To disable instruments by default, configure a match-all
+    :class:`opentelemetry.sdk.metrics.view.View` with `DropAggregation` and then create :class:`opentelemetry.sdk.metrics.view.View`\ s to re-enable
+    individual instruments:
+
+    .. code-block:: python
+        :caption: Disable default views
+
+        MeterProvider(
+            views=[
+                View(instrument_name="*", aggregation=DropAggregation()),
+                View(instrument_name="mycounter"),
+            ],
+            # ...
+        )
+    """
+
+    _all_metric_readers_lock = Lock()
+    _all_metric_readers = weakref.WeakSet()
+
+    def __init__(
+        self,
+        metric_readers: Sequence[
+            "opentelemetry.sdk.metrics.export.MetricReader"
+        ] = (),
+        resource: Optional[Resource] = None,
+        exemplar_filter: Optional[ExemplarFilter] = None,
+        shutdown_on_exit: bool = True,
+        views: Sequence["opentelemetry.sdk.metrics.view.View"] = (),
+    ):
+        self._lock = Lock()
+        self._meter_lock = Lock()
+        self._atexit_handler = None
+        if resource is None:
+            resource = Resource.create({})
+        self._sdk_config = SdkConfiguration(
+            exemplar_filter=(
+                exemplar_filter
+                or _get_exemplar_filter(
+                    environ.get(OTEL_METRICS_EXEMPLAR_FILTER, "trace_based")
+                )
+            ),
+            resource=resource,
+            metric_readers=metric_readers,
+            views=views,
+        )
+        self._measurement_consumer = SynchronousMeasurementConsumer(
+            sdk_config=self._sdk_config
+        )
+        disabled = environ.get(OTEL_SDK_DISABLED, "")
+        self._disabled = disabled.lower().strip() == "true"
+
+        if shutdown_on_exit:
+            self._atexit_handler = register(self.shutdown)
+
+        self._meters = {}
+        self._shutdown_once = Once()
+        self._shutdown = False
+
+        for metric_reader in self._sdk_config.metric_readers:
+            with self._all_metric_readers_lock:
+                if metric_reader in self._all_metric_readers:
+                    # pylint: disable=broad-exception-raised
+                    raise Exception(
+                        f"MetricReader {metric_reader} has been registered "
+                        "already in other MeterProvider instance"
+                    )
+
+                self._all_metric_readers.add(metric_reader)
+
+            metric_reader._set_collect_callback(
+                self._measurement_consumer.collect
+            )
+
+    def force_flush(self, timeout_millis: float = 10_000) -> bool:
+        deadline_ns = time_ns() + timeout_millis * 10**6
+
+        metric_reader_error = {}
+
+        for metric_reader in self._sdk_config.metric_readers:
+            current_ts = time_ns()
+            try:
+                if current_ts >= deadline_ns:
+                    raise MetricsTimeoutError(
+                        "Timed out while flushing metric readers"
+                    )
+                metric_reader.force_flush(
+                    timeout_millis=(deadline_ns - current_ts) / 10**6
+                )
+
+            # pylint: disable=broad-exception-caught
+            except Exception as error:
+                metric_reader_error[metric_reader] = error
+
+        if metric_reader_error:
+            metric_reader_error_string = "\n".join(
+                [
+                    f"{metric_reader.__class__.__name__}: {repr(error)}"
+                    for metric_reader, error in metric_reader_error.items()
+                ]
+            )
+
+            # pylint: disable=broad-exception-raised
+            raise Exception(
+                "MeterProvider.force_flush failed because the following "
+                "metric readers failed during collect:\n"
+                f"{metric_reader_error_string}"
+            )
+        return True
+
+    def shutdown(self, timeout_millis: float = 30_000):
+        deadline_ns = time_ns() + timeout_millis * 10**6
+
+        def _shutdown():
+            self._shutdown = True
+
+        did_shutdown = self._shutdown_once.do_once(_shutdown)
+
+        if not did_shutdown:
+            _logger.warning("shutdown can only be called once")
+            return
+
+        metric_reader_error = {}
+
+        for metric_reader in self._sdk_config.metric_readers:
+            current_ts = time_ns()
+            try:
+                if current_ts >= deadline_ns:
+                    # pylint: disable=broad-exception-raised
+                    raise Exception(
+                        "Didn't get to execute, deadline already exceeded"
+                    )
+                metric_reader.shutdown(
+                    timeout_millis=(deadline_ns - current_ts) / 10**6
+                )
+
+            # pylint: disable=broad-exception-caught
+            except Exception as error:
+                metric_reader_error[metric_reader] = error
+
+        if self._atexit_handler is not None:
+            unregister(self._atexit_handler)
+            self._atexit_handler = None
+
+        if metric_reader_error:
+            metric_reader_error_string = "\n".join(
+                [
+                    f"{metric_reader.__class__.__name__}: {repr(error)}"
+                    for metric_reader, error in metric_reader_error.items()
+                ]
+            )
+
+            # pylint: disable=broad-exception-raised
+            raise Exception(
+                (
+                    "MeterProvider.shutdown failed because the following "
+                    "metric readers failed during shutdown:\n"
+                    f"{metric_reader_error_string}"
+                )
+            )
+
+    def get_meter(
+        self,
+        name: str,
+        version: Optional[str] = None,
+        schema_url: Optional[str] = None,
+        attributes: Optional[Attributes] = None,
+    ) -> Meter:
+        if self._disabled:
+            return NoOpMeter(name, version=version, schema_url=schema_url)
+
+        if self._shutdown:
+            _logger.warning(
+                "A shutdown `MeterProvider` can not provide a `Meter`"
+            )
+            return NoOpMeter(name, version=version, schema_url=schema_url)
+
+        if not name:
+            _logger.warning("Meter name cannot be None or empty.")
+            return NoOpMeter(name, version=version, schema_url=schema_url)
+
+        info = InstrumentationScope(name, version, schema_url, attributes)
+        with self._meter_lock:
+            if not self._meters.get(info):
+                # FIXME #2558 pass SDKConfig object to meter so that the meter
+                # has access to views.
+                self._meters[info] = Meter(
+                    info,
+                    self._measurement_consumer,
+                )
+            return self._meters[info]
diff --git a/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/_view_instrument_match.py b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/_view_instrument_match.py
new file mode 100644
index 00000000..be81d70e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/_view_instrument_match.py
@@ -0,0 +1,153 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from logging import getLogger
+from threading import Lock
+from time import time_ns
+from typing import Dict, List, Optional, Sequence
+
+from opentelemetry.metrics import Instrument
+from opentelemetry.sdk.metrics._internal.aggregation import (
+    Aggregation,
+    DefaultAggregation,
+    _Aggregation,
+    _SumAggregation,
+)
+from opentelemetry.sdk.metrics._internal.export import AggregationTemporality
+from opentelemetry.sdk.metrics._internal.measurement import Measurement
+from opentelemetry.sdk.metrics._internal.point import DataPointT
+from opentelemetry.sdk.metrics._internal.view import View
+
+_logger = getLogger(__name__)
+
+
+class _ViewInstrumentMatch:
+    def __init__(
+        self,
+        view: View,
+        instrument: Instrument,
+        instrument_class_aggregation: Dict[type, Aggregation],
+    ):
+        self._view = view
+        self._instrument = instrument
+        self._attributes_aggregation: Dict[frozenset, _Aggregation] = {}
+        self._lock = Lock()
+        self._instrument_class_aggregation = instrument_class_aggregation
+        self._name = self._view._name or self._instrument.name
+        self._description = (
+            self._view._description or self._instrument.description
+        )
+        if not isinstance(self._view._aggregation, DefaultAggregation):
+            self._aggregation = self._view._aggregation._create_aggregation(
+                self._instrument,
+                None,
+                self._view._exemplar_reservoir_factory,
+                0,
+            )
+        else:
+            self._aggregation = self._instrument_class_aggregation[
+                self._instrument.__class__
+            ]._create_aggregation(
+                self._instrument,
+                None,
+                self._view._exemplar_reservoir_factory,
+                0,
+            )
+
+    def conflicts(self, other: "_ViewInstrumentMatch") -> bool:
+        # pylint: disable=protected-access
+
+        result = (
+            self._name == other._name
+            and self._instrument.unit == other._instrument.unit
+            # The aggregation class is being used here instead of data point
+            # type since they are functionally equivalent.
+            and self._aggregation.__class__ == other._aggregation.__class__
+        )
+        if isinstance(self._aggregation, _SumAggregation):
+            result = (
+                result
+                and self._aggregation._instrument_is_monotonic
+                == other._aggregation._instrument_is_monotonic
+                and self._aggregation._instrument_aggregation_temporality
+                == other._aggregation._instrument_aggregation_temporality
+            )
+
+        return result
+
+    # pylint: disable=protected-access
+    def consume_measurement(
+        self, measurement: Measurement, should_sample_exemplar: bool = True
+    ) -> None:
+        if self._view._attribute_keys is not None:
+            attributes = {}
+
+            for key, value in (measurement.attributes or {}).items():
+                if key in self._view._attribute_keys:
+                    attributes[key] = value
+        elif measurement.attributes is not None:
+            attributes = measurement.attributes
+        else:
+            attributes = {}
+
+        aggr_key = frozenset(attributes.items())
+
+        if aggr_key not in self._attributes_aggregation:
+            with self._lock:
+                if aggr_key not in self._attributes_aggregation:
+                    if not isinstance(
+                        self._view._aggregation, DefaultAggregation
+                    ):
+                        aggregation = (
+                            self._view._aggregation._create_aggregation(
+                                self._instrument,
+                                attributes,
+                                self._view._exemplar_reservoir_factory,
+                                time_ns(),
+                            )
+                        )
+                    else:
+                        aggregation = self._instrument_class_aggregation[
+                            self._instrument.__class__
+                        ]._create_aggregation(
+                            self._instrument,
+                            attributes,
+                            self._view._exemplar_reservoir_factory,
+                            time_ns(),
+                        )
+                    self._attributes_aggregation[aggr_key] = aggregation
+
+        self._attributes_aggregation[aggr_key].aggregate(
+            measurement, should_sample_exemplar
+        )
+
+    def collect(
+        self,
+        collection_aggregation_temporality: AggregationTemporality,
+        collection_start_nanos: int,
+    ) -> Optional[Sequence[DataPointT]]:
+        data_points: List[DataPointT] = []
+        with self._lock:
+            for aggregation in self._attributes_aggregation.values():
+                data_point = aggregation.collect(
+                    collection_aggregation_temporality, collection_start_nanos
+                )
+                if data_point is not None:
+                    data_points.append(data_point)
+
+        # Returning here None instead of an empty list because the caller
+        # does not consume a sequence and to be consistent with the rest of
+        # collect methods that also return None.
+        return data_points or None
diff --git a/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/aggregation.py b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/aggregation.py
new file mode 100644
index 00000000..8443d951
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/aggregation.py
@@ -0,0 +1,1475 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# pylint: disable=too-many-lines
+
+from abc import ABC, abstractmethod
+from bisect import bisect_left
+from enum import IntEnum
+from functools import partial
+from logging import getLogger
+from math import inf
+from threading import Lock
+from typing import (
+    Callable,
+    Generic,
+    List,
+    Optional,
+    Sequence,
+    Type,
+    TypeVar,
+)
+
+from opentelemetry.metrics import (
+    Asynchronous,
+    Counter,
+    Histogram,
+    Instrument,
+    ObservableCounter,
+    ObservableGauge,
+    ObservableUpDownCounter,
+    Synchronous,
+    UpDownCounter,
+    _Gauge,
+)
+from opentelemetry.sdk.metrics._internal.exemplar import (
+    Exemplar,
+    ExemplarReservoirBuilder,
+)
+from opentelemetry.sdk.metrics._internal.exponential_histogram.buckets import (
+    Buckets,
+)
+from opentelemetry.sdk.metrics._internal.exponential_histogram.mapping import (
+    Mapping,
+)
+from opentelemetry.sdk.metrics._internal.exponential_histogram.mapping.exponent_mapping import (
+    ExponentMapping,
+)
+from opentelemetry.sdk.metrics._internal.exponential_histogram.mapping.logarithm_mapping import (
+    LogarithmMapping,
+)
+from opentelemetry.sdk.metrics._internal.measurement import Measurement
+from opentelemetry.sdk.metrics._internal.point import Buckets as BucketsPoint
+from opentelemetry.sdk.metrics._internal.point import (
+    ExponentialHistogramDataPoint,
+    HistogramDataPoint,
+    NumberDataPoint,
+    Sum,
+)
+from opentelemetry.sdk.metrics._internal.point import Gauge as GaugePoint
+from opentelemetry.sdk.metrics._internal.point import (
+    Histogram as HistogramPoint,
+)
+from opentelemetry.util.types import Attributes
+
+_DataPointVarT = TypeVar("_DataPointVarT", NumberDataPoint, HistogramDataPoint)
+
+_logger = getLogger(__name__)
+
+
+class AggregationTemporality(IntEnum):
+    """
+    The temporality to use when aggregating data.
+
+    Can be one of the following values:
+    """
+
+    UNSPECIFIED = 0
+    DELTA = 1
+    CUMULATIVE = 2
+
+
+class _Aggregation(ABC, Generic[_DataPointVarT]):
+    def __init__(
+        self,
+        attributes: Attributes,
+        reservoir_builder: ExemplarReservoirBuilder,
+    ):
+        self._lock = Lock()
+        self._attributes = attributes
+        self._reservoir = reservoir_builder()
+        self._previous_point = None
+
+    @abstractmethod
+    def aggregate(
+        self, measurement: Measurement, should_sample_exemplar: bool = True
+    ) -> None:
+        """Aggregate a measurement.
+
+        Args:
+            measurement: Measurement to aggregate
+            should_sample_exemplar: Whether the measurement should be sampled by the exemplars reservoir or not.
+        """
+
+    @abstractmethod
+    def collect(
+        self,
+        collection_aggregation_temporality: AggregationTemporality,
+        collection_start_nano: int,
+    ) -> Optional[_DataPointVarT]:
+        pass
+
+    def _collect_exemplars(self) -> Sequence[Exemplar]:
+        """Returns the collected exemplars.
+
+        Returns:
+            The exemplars collected by the reservoir
+        """
+        return self._reservoir.collect(self._attributes)
+
+    def _sample_exemplar(
+        self, measurement: Measurement, should_sample_exemplar: bool
+    ) -> None:
+        """Offer the measurement to the exemplar reservoir for sampling.
+
+        It should be called within the each :ref:`aggregate` call.
+
+        Args:
+            measurement: The new measurement
+            should_sample_exemplar: Whether the measurement should be sampled by the exemplars reservoir or not.
+        """
+        if should_sample_exemplar:
+            self._reservoir.offer(
+                measurement.value,
+                measurement.time_unix_nano,
+                measurement.attributes,
+                measurement.context,
+            )
+
+
+class _DropAggregation(_Aggregation):
+    def aggregate(
+        self, measurement: Measurement, should_sample_exemplar: bool = True
+    ) -> None:
+        pass
+
+    def collect(
+        self,
+        collection_aggregation_temporality: AggregationTemporality,
+        collection_start_nano: int,
+    ) -> Optional[_DataPointVarT]:
+        pass
+
+
+class _SumAggregation(_Aggregation[Sum]):
+    def __init__(
+        self,
+        attributes: Attributes,
+        instrument_is_monotonic: bool,
+        instrument_aggregation_temporality: AggregationTemporality,
+        start_time_unix_nano: int,
+        reservoir_builder: ExemplarReservoirBuilder,
+    ):
+        super().__init__(attributes, reservoir_builder)
+
+        self._start_time_unix_nano = start_time_unix_nano
+        self._instrument_aggregation_temporality = (
+            instrument_aggregation_temporality
+        )
+        self._instrument_is_monotonic = instrument_is_monotonic
+
+        self._value = None
+
+        self._previous_collection_start_nano = self._start_time_unix_nano
+        self._previous_value = 0
+
+    def aggregate(
+        self, measurement: Measurement, should_sample_exemplar: bool = True
+    ) -> None:
+        with self._lock:
+            if self._value is None:
+                self._value = 0
+
+            self._value = self._value + measurement.value
+
+        self._sample_exemplar(measurement, should_sample_exemplar)
+
+    def collect(
+        self,
+        collection_aggregation_temporality: AggregationTemporality,
+        collection_start_nano: int,
+    ) -> Optional[NumberDataPoint]:
+        """
+        Atomically return a point for the current value of the metric and
+        reset the aggregation value.
+
+        Synchronous instruments have a method which is called directly with
+        increments for a given quantity:
+
+        For example, an instrument that counts the amount of passengers in
+        every vehicle that crosses a certain point in a highway:
+
+        synchronous_instrument.add(2)
+        collect(...)  # 2 passengers are counted
+        synchronous_instrument.add(3)
+        collect(...)  # 3 passengers are counted
+        synchronous_instrument.add(1)
+        collect(...)  # 1 passenger is counted
+
+        In this case the instrument aggregation temporality is DELTA because
+        every value represents an increment to the count,
+
+        Asynchronous instruments have a callback which returns the total value
+        of a given quantity:
+
+        For example, an instrument that measures the amount of bytes written to
+        a certain hard drive:
+
+        callback() -> 1352
+        collect(...) # 1352 bytes have been written so far
+        callback() -> 2324
+        collect(...) # 2324 bytes have been written so far
+        callback() -> 4542
+        collect(...) # 4542 bytes have been written so far
+
+        In this case the instrument aggregation temporality is CUMULATIVE
+        because every value represents the total of the measurement.
+
+        There is also the collection aggregation temporality, which is passed
+        to this method. The collection aggregation temporality defines the
+        nature of the returned value by this aggregation.
+
+        When the collection aggregation temporality matches the
+        instrument aggregation temporality, then this method returns the
+        current value directly:
+
+        synchronous_instrument.add(2)
+        collect(DELTA) -> 2
+        synchronous_instrument.add(3)
+        collect(DELTA) -> 3
+        synchronous_instrument.add(1)
+        collect(DELTA) -> 1
+
+        callback() -> 1352
+        collect(CUMULATIVE) -> 1352
+        callback() -> 2324
+        collect(CUMULATIVE) -> 2324
+        callback() -> 4542
+        collect(CUMULATIVE) -> 4542
+
+        When the collection aggregation temporality does not match the
+        instrument aggregation temporality, then a conversion is made. For this
+        purpose, this aggregation keeps a private attribute,
+        self._previous_value.
+
+        When the instrument is synchronous:
+
+        self._previous_value is the sum of every previously
+        collected (delta) value. In this case, the returned (cumulative) value
+        will be:
+
+        self._previous_value + value
+
+        synchronous_instrument.add(2)
+        collect(CUMULATIVE) -> 2
+        synchronous_instrument.add(3)
+        collect(CUMULATIVE) -> 5
+        synchronous_instrument.add(1)
+        collect(CUMULATIVE) -> 6
+
+        Also, as a diagram:
+
+        time ->
+
+        self._previous_value
+        |-------------|
+
+        value (delta)
+                      |----|
+
+        returned value (cumulative)
+        |------------------|
+
+        When the instrument is asynchronous:
+
+        self._previous_value is the value of the previously
+        collected (cumulative) value. In this case, the returned (delta) value
+        will be:
+
+        value - self._previous_value
+
+        callback() -> 1352
+        collect(DELTA) -> 1352
+        callback() -> 2324
+        collect(DELTA) -> 972
+        callback() -> 4542
+        collect(DELTA) -> 2218
+
+        Also, as a diagram:
+
+        time ->
+
+        self._previous_value
+        |-------------|
+
+        value (cumulative)
+        |------------------|
+
+        returned value (delta)
+                      |----|
+        """
+
+        with self._lock:
+            value = self._value
+            self._value = None
+
+            if (
+                self._instrument_aggregation_temporality
+                is AggregationTemporality.DELTA
+            ):
+                # This happens when the corresponding instrument for this
+                # aggregation is synchronous.
+                if (
+                    collection_aggregation_temporality
+                    is AggregationTemporality.DELTA
+                ):
+                    previous_collection_start_nano = (
+                        self._previous_collection_start_nano
+                    )
+                    self._previous_collection_start_nano = (
+                        collection_start_nano
+                    )
+
+                    if value is None:
+                        return None
+
+                    return NumberDataPoint(
+                        attributes=self._attributes,
+                        exemplars=self._collect_exemplars(),
+                        start_time_unix_nano=previous_collection_start_nano,
+                        time_unix_nano=collection_start_nano,
+                        value=value,
+                    )
+
+                if value is None:
+                    value = 0
+
+                self._previous_value = value + self._previous_value
+
+                return NumberDataPoint(
+                    attributes=self._attributes,
+                    exemplars=self._collect_exemplars(),
+                    start_time_unix_nano=self._start_time_unix_nano,
+                    time_unix_nano=collection_start_nano,
+                    value=self._previous_value,
+                )
+
+            # This happens when the corresponding instrument for this
+            # aggregation is asynchronous.
+
+            if value is None:
+                # This happens when the corresponding instrument callback
+                # does not produce measurements.
+                return None
+
+            if (
+                collection_aggregation_temporality
+                is AggregationTemporality.DELTA
+            ):
+                result_value = value - self._previous_value
+
+                self._previous_value = value
+
+                previous_collection_start_nano = (
+                    self._previous_collection_start_nano
+                )
+                self._previous_collection_start_nano = collection_start_nano
+
+                return NumberDataPoint(
+                    attributes=self._attributes,
+                    exemplars=self._collect_exemplars(),
+                    start_time_unix_nano=previous_collection_start_nano,
+                    time_unix_nano=collection_start_nano,
+                    value=result_value,
+                )
+
+            return NumberDataPoint(
+                attributes=self._attributes,
+                exemplars=self._collect_exemplars(),
+                start_time_unix_nano=self._start_time_unix_nano,
+                time_unix_nano=collection_start_nano,
+                value=value,
+            )
+
+
+class _LastValueAggregation(_Aggregation[GaugePoint]):
+    def __init__(
+        self,
+        attributes: Attributes,
+        reservoir_builder: ExemplarReservoirBuilder,
+    ):
+        super().__init__(attributes, reservoir_builder)
+        self._value = None
+
+    def aggregate(
+        self, measurement: Measurement, should_sample_exemplar: bool = True
+    ):
+        with self._lock:
+            self._value = measurement.value
+
+        self._sample_exemplar(measurement, should_sample_exemplar)
+
+    def collect(
+        self,
+        collection_aggregation_temporality: AggregationTemporality,
+        collection_start_nano: int,
+    ) -> Optional[_DataPointVarT]:
+        """
+        Atomically return a point for the current value of the metric.
+        """
+        with self._lock:
+            if self._value is None:
+                return None
+            value = self._value
+            self._value = None
+
+        exemplars = self._collect_exemplars()
+
+        return NumberDataPoint(
+            attributes=self._attributes,
+            exemplars=exemplars,
+            start_time_unix_nano=None,
+            time_unix_nano=collection_start_nano,
+            value=value,
+        )
+
+
+_DEFAULT_EXPLICIT_BUCKET_HISTOGRAM_AGGREGATION_BOUNDARIES: Sequence[float] = (
+    0.0,
+    5.0,
+    10.0,
+    25.0,
+    50.0,
+    75.0,
+    100.0,
+    250.0,
+    500.0,
+    750.0,
+    1000.0,
+    2500.0,
+    5000.0,
+    7500.0,
+    10000.0,
+)
+
+
+class _ExplicitBucketHistogramAggregation(_Aggregation[HistogramPoint]):
+    def __init__(
+        self,
+        attributes: Attributes,
+        instrument_aggregation_temporality: AggregationTemporality,
+        start_time_unix_nano: int,
+        reservoir_builder: ExemplarReservoirBuilder,
+        boundaries: Optional[Sequence[float]] = None,
+        record_min_max: bool = True,
+    ):
+        if boundaries is None:
+            boundaries = (
+                _DEFAULT_EXPLICIT_BUCKET_HISTOGRAM_AGGREGATION_BOUNDARIES
+            )
+        super().__init__(
+            attributes,
+            reservoir_builder=partial(
+                reservoir_builder, boundaries=boundaries
+            ),
+        )
+
+        self._instrument_aggregation_temporality = (
+            instrument_aggregation_temporality
+        )
+        self._start_time_unix_nano = start_time_unix_nano
+        self._boundaries = tuple(boundaries)
+        self._record_min_max = record_min_max
+
+        self._value = None
+        self._min = inf
+        self._max = -inf
+        self._sum = 0
+
+        self._previous_value = None
+        self._previous_min = inf
+        self._previous_max = -inf
+        self._previous_sum = 0
+
+        self._previous_collection_start_nano = self._start_time_unix_nano
+
+    def _get_empty_bucket_counts(self) -> List[int]:
+        return [0] * (len(self._boundaries) + 1)
+
+    def aggregate(
+        self, measurement: Measurement, should_sample_exemplar: bool = True
+    ) -> None:
+        with self._lock:
+            if self._value is None:
+                self._value = self._get_empty_bucket_counts()
+
+            measurement_value = measurement.value
+
+            self._sum += measurement_value
+
+            if self._record_min_max:
+                self._min = min(self._min, measurement_value)
+                self._max = max(self._max, measurement_value)
+
+            self._value[bisect_left(self._boundaries, measurement_value)] += 1
+
+        self._sample_exemplar(measurement, should_sample_exemplar)
+
+    def collect(
+        self,
+        collection_aggregation_temporality: AggregationTemporality,
+        collection_start_nano: int,
+    ) -> Optional[_DataPointVarT]:
+        """
+        Atomically return a point for the current value of the metric.
+        """
+
+        with self._lock:
+            value = self._value
+            sum_ = self._sum
+            min_ = self._min
+            max_ = self._max
+
+            self._value = None
+            self._sum = 0
+            self._min = inf
+            self._max = -inf
+
+            if (
+                self._instrument_aggregation_temporality
+                is AggregationTemporality.DELTA
+            ):
+                # This happens when the corresponding instrument for this
+                # aggregation is synchronous.
+                if (
+                    collection_aggregation_temporality
+                    is AggregationTemporality.DELTA
+                ):
+                    previous_collection_start_nano = (
+                        self._previous_collection_start_nano
+                    )
+                    self._previous_collection_start_nano = (
+                        collection_start_nano
+                    )
+
+                    if value is None:
+                        return None
+
+                    return HistogramDataPoint(
+                        attributes=self._attributes,
+                        exemplars=self._collect_exemplars(),
+                        start_time_unix_nano=previous_collection_start_nano,
+                        time_unix_nano=collection_start_nano,
+                        count=sum(value),
+                        sum=sum_,
+                        bucket_counts=tuple(value),
+                        explicit_bounds=self._boundaries,
+                        min=min_,
+                        max=max_,
+                    )
+
+                if value is None:
+                    value = self._get_empty_bucket_counts()
+
+                if self._previous_value is None:
+                    self._previous_value = self._get_empty_bucket_counts()
+
+                self._previous_value = [
+                    value_element + previous_value_element
+                    for (
+                        value_element,
+                        previous_value_element,
+                    ) in zip(value, self._previous_value)
+                ]
+                self._previous_min = min(min_, self._previous_min)
+                self._previous_max = max(max_, self._previous_max)
+                self._previous_sum = sum_ + self._previous_sum
+
+                return HistogramDataPoint(
+                    attributes=self._attributes,
+                    exemplars=self._collect_exemplars(),
+                    start_time_unix_nano=self._start_time_unix_nano,
+                    time_unix_nano=collection_start_nano,
+                    count=sum(self._previous_value),
+                    sum=self._previous_sum,
+                    bucket_counts=tuple(self._previous_value),
+                    explicit_bounds=self._boundaries,
+                    min=self._previous_min,
+                    max=self._previous_max,
+                )
+
+            return None
+
+
+# pylint: disable=protected-access
+class _ExponentialBucketHistogramAggregation(_Aggregation[HistogramPoint]):
+    # _min_max_size and _max_max_size are the smallest and largest values
+    # the max_size parameter may have, respectively.
+
+    # _min_max_size is is the smallest reasonable value which is small enough
+    # to contain the entire normal floating point range at the minimum scale.
+    _min_max_size = 2
+
+    # _max_max_size is an arbitrary limit meant to limit accidental creation of
+    # giant exponential bucket histograms.
+    _max_max_size = 16384
+
+    def __init__(
+        self,
+        attributes: Attributes,
+        reservoir_builder: ExemplarReservoirBuilder,
+        instrument_aggregation_temporality: AggregationTemporality,
+        start_time_unix_nano: int,
+        # This is the default maximum number of buckets per positive or
+        # negative number range.  The value 160 is specified by OpenTelemetry.
+        # See the derivation here:
+        # https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/metrics/sdk.md#exponential-bucket-histogram-aggregation)
+        max_size: int = 160,
+        max_scale: int = 20,
+    ):
+        # max_size is the maximum capacity of the positive and negative
+        # buckets.
+        # _sum is the sum of all the values aggregated by this aggregator.
+        # _count is the count of all calls to aggregate.
+        # _zero_count is the count of all the calls to aggregate when the value
+        # to be aggregated is exactly 0.
+        # _min is the smallest value aggregated by this aggregator.
+        # _max is the smallest value aggregated by this aggregator.
+        # _positive holds the positive values.
+        # _negative holds the negative values by their absolute value.
+        if max_size < self._min_max_size:
+            raise ValueError(
+                f"Buckets max size {max_size} is smaller than "
+                "minimum max size {self._min_max_size}"
+            )
+
+        if max_size > self._max_max_size:
+            raise ValueError(
+                f"Buckets max size {max_size} is larger than "
+                "maximum max size {self._max_max_size}"
+            )
+        if max_scale > 20:
+            _logger.warning(
+                "max_scale is set to %s which is "
+                "larger than the recommended value of 20",
+                max_scale,
+            )
+
+        # This aggregation is analogous to _ExplicitBucketHistogramAggregation,
+        # the only difference is that with every call to aggregate, the size
+        # and amount of buckets can change (in
+        # _ExplicitBucketHistogramAggregation both size and amount of buckets
+        # remain constant once it is instantiated).
+
+        super().__init__(
+            attributes,
+            reservoir_builder=partial(
+                reservoir_builder, size=min(20, max_size)
+            ),
+        )
+
+        self._instrument_aggregation_temporality = (
+            instrument_aggregation_temporality
+        )
+        self._start_time_unix_nano = start_time_unix_nano
+        self._max_size = max_size
+        self._max_scale = max_scale
+
+        self._value_positive = None
+        self._value_negative = None
+        self._min = inf
+        self._max = -inf
+        self._sum = 0
+        self._count = 0
+        self._zero_count = 0
+        self._scale = None
+
+        self._previous_value_positive = None
+        self._previous_value_negative = None
+        self._previous_min = inf
+        self._previous_max = -inf
+        self._previous_sum = 0
+        self._previous_count = 0
+        self._previous_zero_count = 0
+        self._previous_scale = None
+
+        self._previous_collection_start_nano = self._start_time_unix_nano
+
+        self._mapping = self._new_mapping(self._max_scale)
+
+    def aggregate(
+        self, measurement: Measurement, should_sample_exemplar: bool = True
+    ) -> None:
+        # pylint: disable=too-many-branches,too-many-statements, too-many-locals
+
+        with self._lock:
+            if self._value_positive is None:
+                self._value_positive = Buckets()
+            if self._value_negative is None:
+                self._value_negative = Buckets()
+
+            measurement_value = measurement.value
+
+            self._sum += measurement_value
+
+            self._min = min(self._min, measurement_value)
+            self._max = max(self._max, measurement_value)
+
+            self._count += 1
+
+            if measurement_value == 0:
+                self._zero_count += 1
+
+                if self._count == self._zero_count:
+                    self._scale = 0
+
+                return
+
+            if measurement_value > 0:
+                value = self._value_positive
+
+            else:
+                measurement_value = -measurement_value
+                value = self._value_negative
+
+            # The following code finds out if it is necessary to change the
+            # buckets to hold the incoming measurement_value, changes them if
+            # necessary. This process does not exist in
+            # _ExplicitBucketHistogram aggregation because the buckets there
+            # are constant in size and amount.
+            index = self._mapping.map_to_index(measurement_value)
+
+            is_rescaling_needed = False
+            low, high = 0, 0
+
+            if len(value) == 0:
+                value.index_start = index
+                value.index_end = index
+                value.index_base = index
+
+            elif (
+                index < value.index_start
+                and (value.index_end - index) >= self._max_size
+            ):
+                is_rescaling_needed = True
+                low = index
+                high = value.index_end
+
+            elif (
+                index > value.index_end
+                and (index - value.index_start) >= self._max_size
+            ):
+                is_rescaling_needed = True
+                low = value.index_start
+                high = index
+
+            if is_rescaling_needed:
+                scale_change = self._get_scale_change(low, high)
+                self._downscale(
+                    scale_change,
+                    self._value_positive,
+                    self._value_negative,
+                )
+                self._mapping = self._new_mapping(
+                    self._mapping.scale - scale_change
+                )
+
+                index = self._mapping.map_to_index(measurement_value)
+
+            self._scale = self._mapping.scale
+
+            if index < value.index_start:
+                span = value.index_end - index
+
+                if span >= len(value.counts):
+                    value.grow(span + 1, self._max_size)
+
+                value.index_start = index
+
+            elif index > value.index_end:
+                span = index - value.index_start
+
+                if span >= len(value.counts):
+                    value.grow(span + 1, self._max_size)
+
+                value.index_end = index
+
+            bucket_index = index - value.index_base
+
+            if bucket_index < 0:
+                bucket_index += len(value.counts)
+
+            # Now the buckets have been changed if needed and bucket_index will
+            # be used to increment the counter of the bucket that needs to be
+            # incremented.
+
+            # This is analogous to
+            # self._value[bisect_left(self._boundaries, measurement_value)] += 1
+            # in _ExplicitBucketHistogramAggregation.aggregate
+            value.increment_bucket(bucket_index)
+
+        self._sample_exemplar(measurement, should_sample_exemplar)
+
+    def collect(
+        self,
+        collection_aggregation_temporality: AggregationTemporality,
+        collection_start_nano: int,
+    ) -> Optional[_DataPointVarT]:
+        """
+        Atomically return a point for the current value of the metric.
+        """
+
+        # pylint: disable=too-many-statements, too-many-locals
+        with self._lock:
+            value_positive = self._value_positive
+            value_negative = self._value_negative
+            sum_ = self._sum
+            min_ = self._min
+            max_ = self._max
+            count = self._count
+            zero_count = self._zero_count
+            scale = self._scale
+
+            self._value_positive = None
+            self._value_negative = None
+            self._sum = 0
+            self._min = inf
+            self._max = -inf
+            self._count = 0
+            self._zero_count = 0
+            self._scale = None
+
+            if (
+                self._instrument_aggregation_temporality
+                is AggregationTemporality.DELTA
+            ):
+                # This happens when the corresponding instrument for this
+                # aggregation is synchronous.
+                if (
+                    collection_aggregation_temporality
+                    is AggregationTemporality.DELTA
+                ):
+                    previous_collection_start_nano = (
+                        self._previous_collection_start_nano
+                    )
+                    self._previous_collection_start_nano = (
+                        collection_start_nano
+                    )
+
+                    if value_positive is None and value_negative is None:
+                        return None
+
+                    return ExponentialHistogramDataPoint(
+                        attributes=self._attributes,
+                        exemplars=self._collect_exemplars(),
+                        start_time_unix_nano=previous_collection_start_nano,
+                        time_unix_nano=collection_start_nano,
+                        count=count,
+                        sum=sum_,
+                        scale=scale,
+                        zero_count=zero_count,
+                        positive=BucketsPoint(
+                            offset=value_positive.offset,
+                            bucket_counts=(value_positive.get_offset_counts()),
+                        ),
+                        negative=BucketsPoint(
+                            offset=value_negative.offset,
+                            bucket_counts=(value_negative.get_offset_counts()),
+                        ),
+                        # FIXME: Find the right value for flags
+                        flags=0,
+                        min=min_,
+                        max=max_,
+                    )
+
+                # Here collection_temporality is CUMULATIVE.
+                # instrument_temporality is always DELTA for the time being.
+                # Here we need to handle the case where:
+                # collect is called after at least one other call to collect
+                # (there is data in previous buckets, a call to merge is needed
+                # to handle possible differences in bucket sizes).
+                # collect is called without another call previous call to
+                # collect was made (there is no previous buckets, previous,
+                # empty buckets that are the same scale of the current buckets
+                # need to be made so that they can be cumulatively aggregated
+                # to the current buckets).
+
+                if (
+                    value_positive is None
+                    and self._previous_value_positive is None
+                ):
+                    # This happens if collect is called for the first time
+                    # and aggregate has not yet been called.
+                    value_positive = Buckets()
+                    self._previous_value_positive = value_positive.copy_empty()
+                if (
+                    value_negative is None
+                    and self._previous_value_negative is None
+                ):
+                    value_negative = Buckets()
+                    self._previous_value_negative = value_negative.copy_empty()
+                if scale is None and self._previous_scale is None:
+                    scale = self._mapping.scale
+                    self._previous_scale = scale
+
+                if (
+                    value_positive is not None
+                    and self._previous_value_positive is None
+                ):
+                    # This happens when collect is called the very first time
+                    # and aggregate has been called before.
+
+                    # We need previous buckets to add them to the current ones.
+                    # When collect is called for the first time, there are no
+                    # previous buckets, so we need to create empty buckets to
+                    # add them to the current ones. The addition of empty
+                    # buckets to the current ones will result in the current
+                    # ones unchanged.
+
+                    # The way the previous buckets are generated here is
+                    # different from the explicit bucket histogram where
+                    # the size and amount of the buckets does not change once
+                    # they are instantiated. Here, the size and amount of the
+                    # buckets can change with every call to aggregate. In order
+                    # to get empty buckets that can be added to the current
+                    # ones resulting in the current ones unchanged we need to
+                    # generate empty buckets that have the same size and amount
+                    # as the current ones, this is what copy_empty does.
+                    self._previous_value_positive = value_positive.copy_empty()
+                if (
+                    value_negative is not None
+                    and self._previous_value_negative is None
+                ):
+                    self._previous_value_negative = value_negative.copy_empty()
+                if scale is not None and self._previous_scale is None:
+                    self._previous_scale = scale
+
+                if (
+                    value_positive is None
+                    and self._previous_value_positive is not None
+                ):
+                    value_positive = self._previous_value_positive.copy_empty()
+                if (
+                    value_negative is None
+                    and self._previous_value_negative is not None
+                ):
+                    value_negative = self._previous_value_negative.copy_empty()
+                if scale is None and self._previous_scale is not None:
+                    scale = self._previous_scale
+
+                min_scale = min(self._previous_scale, scale)
+
+                low_positive, high_positive = (
+                    self._get_low_high_previous_current(
+                        self._previous_value_positive,
+                        value_positive,
+                        scale,
+                        min_scale,
+                    )
+                )
+                low_negative, high_negative = (
+                    self._get_low_high_previous_current(
+                        self._previous_value_negative,
+                        value_negative,
+                        scale,
+                        min_scale,
+                    )
+                )
+
+                min_scale = min(
+                    min_scale
+                    - self._get_scale_change(low_positive, high_positive),
+                    min_scale
+                    - self._get_scale_change(low_negative, high_negative),
+                )
+
+                self._downscale(
+                    self._previous_scale - min_scale,
+                    self._previous_value_positive,
+                    self._previous_value_negative,
+                )
+
+                # self._merge adds the values from value to
+                # self._previous_value, this is analogous to
+                # self._previous_value = [
+                #     value_element + previous_value_element
+                #     for (
+                #         value_element,
+                #         previous_value_element,
+                #     ) in zip(value, self._previous_value)
+                # ]
+                # in _ExplicitBucketHistogramAggregation.collect.
+                self._merge(
+                    self._previous_value_positive,
+                    value_positive,
+                    scale,
+                    min_scale,
+                    collection_aggregation_temporality,
+                )
+                self._merge(
+                    self._previous_value_negative,
+                    value_negative,
+                    scale,
+                    min_scale,
+                    collection_aggregation_temporality,
+                )
+
+                self._previous_min = min(min_, self._previous_min)
+                self._previous_max = max(max_, self._previous_max)
+                self._previous_sum = sum_ + self._previous_sum
+                self._previous_count = count + self._previous_count
+                self._previous_zero_count = (
+                    zero_count + self._previous_zero_count
+                )
+                self._previous_scale = min_scale
+
+                return ExponentialHistogramDataPoint(
+                    attributes=self._attributes,
+                    exemplars=self._collect_exemplars(),
+                    start_time_unix_nano=self._start_time_unix_nano,
+                    time_unix_nano=collection_start_nano,
+                    count=self._previous_count,
+                    sum=self._previous_sum,
+                    scale=self._previous_scale,
+                    zero_count=self._previous_zero_count,
+                    positive=BucketsPoint(
+                        offset=self._previous_value_positive.offset,
+                        bucket_counts=(
+                            self._previous_value_positive.get_offset_counts()
+                        ),
+                    ),
+                    negative=BucketsPoint(
+                        offset=self._previous_value_negative.offset,
+                        bucket_counts=(
+                            self._previous_value_negative.get_offset_counts()
+                        ),
+                    ),
+                    # FIXME: Find the right value for flags
+                    flags=0,
+                    min=self._previous_min,
+                    max=self._previous_max,
+                )
+
+            return None
+
+    def _get_low_high_previous_current(
+        self,
+        previous_point_buckets,
+        current_point_buckets,
+        current_scale,
+        min_scale,
+    ):
+        (previous_point_low, previous_point_high) = self._get_low_high(
+            previous_point_buckets, self._previous_scale, min_scale
+        )
+        (current_point_low, current_point_high) = self._get_low_high(
+            current_point_buckets, current_scale, min_scale
+        )
+
+        if current_point_low > current_point_high:
+            low = previous_point_low
+            high = previous_point_high
+
+        elif previous_point_low > previous_point_high:
+            low = current_point_low
+            high = current_point_high
+
+        else:
+            low = min(previous_point_low, current_point_low)
+            high = max(previous_point_high, current_point_high)
+
+        return low, high
+
+    @staticmethod
+    def _get_low_high(buckets, scale, min_scale):
+        if buckets.counts == [0]:
+            return 0, -1
+
+        shift = scale - min_scale
+
+        return buckets.index_start >> shift, buckets.index_end >> shift
+
+    @staticmethod
+    def _new_mapping(scale: int) -> Mapping:
+        if scale <= 0:
+            return ExponentMapping(scale)
+        return LogarithmMapping(scale)
+
+    def _get_scale_change(self, low, high):
+        change = 0
+
+        while high - low >= self._max_size:
+            high = high >> 1
+            low = low >> 1
+
+            change += 1
+
+        return change
+
+    @staticmethod
+    def _downscale(change: int, positive, negative):
+        if change == 0:
+            return
+
+        if change < 0:
+            # pylint: disable=broad-exception-raised
+            raise Exception("Invalid change of scale")
+
+        positive.downscale(change)
+        negative.downscale(change)
+
+    def _merge(
+        self,
+        previous_buckets: Buckets,
+        current_buckets: Buckets,
+        current_scale,
+        min_scale,
+        aggregation_temporality,
+    ):
+        current_change = current_scale - min_scale
+
+        for current_bucket_index, current_bucket in enumerate(
+            current_buckets.counts
+        ):
+            if current_bucket == 0:
+                continue
+
+            # Not considering the case where len(previous_buckets) == 0. This
+            # would not happen because self._previous_point is only assigned to
+            # an ExponentialHistogramDataPoint object if self._count != 0.
+
+            current_index = current_buckets.index_base + current_bucket_index
+            if current_index > current_buckets.index_end:
+                current_index -= len(current_buckets.counts)
+
+            index = current_index >> current_change
+
+            if index < previous_buckets.index_start:
+                span = previous_buckets.index_end - index
+
+                if span >= self._max_size:
+                    # pylint: disable=broad-exception-raised
+                    raise Exception("Incorrect merge scale")
+
+                if span >= len(previous_buckets.counts):
+                    previous_buckets.grow(span + 1, self._max_size)
+
+                previous_buckets.index_start = index
+
+            if index > previous_buckets.index_end:
+                span = index - previous_buckets.index_start
+
+                if span >= self._max_size:
+                    # pylint: disable=broad-exception-raised
+                    raise Exception("Incorrect merge scale")
+
+                if span >= len(previous_buckets.counts):
+                    previous_buckets.grow(span + 1, self._max_size)
+
+                previous_buckets.index_end = index
+
+            bucket_index = index - previous_buckets.index_base
+
+            if bucket_index < 0:
+                bucket_index += len(previous_buckets.counts)
+
+            if aggregation_temporality is AggregationTemporality.DELTA:
+                current_bucket = -current_bucket
+
+            previous_buckets.increment_bucket(
+                bucket_index, increment=current_bucket
+            )
+
+
+class Aggregation(ABC):
+    """
+    Base class for all aggregation types.
+    """
+
+    @abstractmethod
+    def _create_aggregation(
+        self,
+        instrument: Instrument,
+        attributes: Attributes,
+        reservoir_factory: Callable[
+            [Type[_Aggregation]], ExemplarReservoirBuilder
+        ],
+        start_time_unix_nano: int,
+    ) -> _Aggregation:
+        """Creates an aggregation"""
+
+
+class DefaultAggregation(Aggregation):
+    """
+    The default aggregation to be used in a `View`.
+
+    This aggregation will create an actual aggregation depending on the
+    instrument type, as specified next:
+
+    ==================================================== ====================================
+    Instrument                                           Aggregation
+    ==================================================== ====================================
+    `opentelemetry.sdk.metrics.Counter`                  `SumAggregation`
+    `opentelemetry.sdk.metrics.UpDownCounter`            `SumAggregation`
+    `opentelemetry.sdk.metrics.ObservableCounter`        `SumAggregation`
+    `opentelemetry.sdk.metrics.ObservableUpDownCounter`  `SumAggregation`
+    `opentelemetry.sdk.metrics.Histogram`                `ExplicitBucketHistogramAggregation`
+    `opentelemetry.sdk.metrics.ObservableGauge`          `LastValueAggregation`
+    ==================================================== ====================================
+    """
+
+    def _create_aggregation(
+        self,
+        instrument: Instrument,
+        attributes: Attributes,
+        reservoir_factory: Callable[
+            [Type[_Aggregation]], ExemplarReservoirBuilder
+        ],
+        start_time_unix_nano: int,
+    ) -> _Aggregation:
+        # pylint: disable=too-many-return-statements
+        if isinstance(instrument, Counter):
+            return _SumAggregation(
+                attributes,
+                reservoir_builder=reservoir_factory(_SumAggregation),
+                instrument_is_monotonic=True,
+                instrument_aggregation_temporality=(
+                    AggregationTemporality.DELTA
+                ),
+                start_time_unix_nano=start_time_unix_nano,
+            )
+        if isinstance(instrument, UpDownCounter):
+            return _SumAggregation(
+                attributes,
+                reservoir_builder=reservoir_factory(_SumAggregation),
+                instrument_is_monotonic=False,
+                instrument_aggregation_temporality=(
+                    AggregationTemporality.DELTA
+                ),
+                start_time_unix_nano=start_time_unix_nano,
+            )
+
+        if isinstance(instrument, ObservableCounter):
+            return _SumAggregation(
+                attributes,
+                reservoir_builder=reservoir_factory(_SumAggregation),
+                instrument_is_monotonic=True,
+                instrument_aggregation_temporality=(
+                    AggregationTemporality.CUMULATIVE
+                ),
+                start_time_unix_nano=start_time_unix_nano,
+            )
+
+        if isinstance(instrument, ObservableUpDownCounter):
+            return _SumAggregation(
+                attributes,
+                reservoir_builder=reservoir_factory(_SumAggregation),
+                instrument_is_monotonic=False,
+                instrument_aggregation_temporality=(
+                    AggregationTemporality.CUMULATIVE
+                ),
+                start_time_unix_nano=start_time_unix_nano,
+            )
+
+        if isinstance(instrument, Histogram):
+            boundaries = instrument._advisory.explicit_bucket_boundaries
+            return _ExplicitBucketHistogramAggregation(
+                attributes,
+                reservoir_builder=reservoir_factory(
+                    _ExplicitBucketHistogramAggregation
+                ),
+                instrument_aggregation_temporality=(
+                    AggregationTemporality.DELTA
+                ),
+                boundaries=boundaries,
+                start_time_unix_nano=start_time_unix_nano,
+            )
+
+        if isinstance(instrument, ObservableGauge):
+            return _LastValueAggregation(
+                attributes,
+                reservoir_builder=reservoir_factory(_LastValueAggregation),
+            )
+
+        if isinstance(instrument, _Gauge):
+            return _LastValueAggregation(
+                attributes,
+                reservoir_builder=reservoir_factory(_LastValueAggregation),
+            )
+
+        # pylint: disable=broad-exception-raised
+        raise Exception(f"Invalid instrument type {type(instrument)} found")
+
+
+class ExponentialBucketHistogramAggregation(Aggregation):
+    def __init__(
+        self,
+        max_size: int = 160,
+        max_scale: int = 20,
+    ):
+        self._max_size = max_size
+        self._max_scale = max_scale
+
+    def _create_aggregation(
+        self,
+        instrument: Instrument,
+        attributes: Attributes,
+        reservoir_factory: Callable[
+            [Type[_Aggregation]], ExemplarReservoirBuilder
+        ],
+        start_time_unix_nano: int,
+    ) -> _Aggregation:
+        instrument_aggregation_temporality = AggregationTemporality.UNSPECIFIED
+        if isinstance(instrument, Synchronous):
+            instrument_aggregation_temporality = AggregationTemporality.DELTA
+        elif isinstance(instrument, Asynchronous):
+            instrument_aggregation_temporality = (
+                AggregationTemporality.CUMULATIVE
+            )
+
+        return _ExponentialBucketHistogramAggregation(
+            attributes,
+            reservoir_factory(_ExponentialBucketHistogramAggregation),
+            instrument_aggregation_temporality,
+            start_time_unix_nano,
+            max_size=self._max_size,
+            max_scale=self._max_scale,
+        )
+
+
+class ExplicitBucketHistogramAggregation(Aggregation):
+    """This aggregation informs the SDK to collect:
+
+    - Count of Measurement values falling within explicit bucket boundaries.
+    - Arithmetic sum of Measurement values in population. This SHOULD NOT be collected when used with instruments that record negative measurements, e.g. UpDownCounter or ObservableGauge.
+    - Min (optional) Measurement value in population.
+    - Max (optional) Measurement value in population.
+
+
+    Args:
+        boundaries: Array of increasing values representing explicit bucket boundary values.
+        record_min_max: Whether to record min and max.
+    """
+
+    def __init__(
+        self,
+        boundaries: Optional[Sequence[float]] = None,
+        record_min_max: bool = True,
+    ) -> None:
+        self._boundaries = boundaries
+        self._record_min_max = record_min_max
+
+    def _create_aggregation(
+        self,
+        instrument: Instrument,
+        attributes: Attributes,
+        reservoir_factory: Callable[
+            [Type[_Aggregation]], ExemplarReservoirBuilder
+        ],
+        start_time_unix_nano: int,
+    ) -> _Aggregation:
+        instrument_aggregation_temporality = AggregationTemporality.UNSPECIFIED
+        if isinstance(instrument, Synchronous):
+            instrument_aggregation_temporality = AggregationTemporality.DELTA
+        elif isinstance(instrument, Asynchronous):
+            instrument_aggregation_temporality = (
+                AggregationTemporality.CUMULATIVE
+            )
+
+        if self._boundaries is None:
+            self._boundaries = (
+                instrument._advisory.explicit_bucket_boundaries
+                or _DEFAULT_EXPLICIT_BUCKET_HISTOGRAM_AGGREGATION_BOUNDARIES
+            )
+
+        return _ExplicitBucketHistogramAggregation(
+            attributes,
+            instrument_aggregation_temporality,
+            start_time_unix_nano,
+            reservoir_factory(_ExplicitBucketHistogramAggregation),
+            self._boundaries,
+            self._record_min_max,
+        )
+
+
+class SumAggregation(Aggregation):
+    """This aggregation informs the SDK to collect:
+
+    - The arithmetic sum of Measurement values.
+    """
+
+    def _create_aggregation(
+        self,
+        instrument: Instrument,
+        attributes: Attributes,
+        reservoir_factory: Callable[
+            [Type[_Aggregation]], ExemplarReservoirBuilder
+        ],
+        start_time_unix_nano: int,
+    ) -> _Aggregation:
+        instrument_aggregation_temporality = AggregationTemporality.UNSPECIFIED
+        if isinstance(instrument, Synchronous):
+            instrument_aggregation_temporality = AggregationTemporality.DELTA
+        elif isinstance(instrument, Asynchronous):
+            instrument_aggregation_temporality = (
+                AggregationTemporality.CUMULATIVE
+            )
+
+        return _SumAggregation(
+            attributes,
+            isinstance(instrument, (Counter, ObservableCounter)),
+            instrument_aggregation_temporality,
+            start_time_unix_nano,
+            reservoir_factory(_SumAggregation),
+        )
+
+
+class LastValueAggregation(Aggregation):
+    """
+    This aggregation informs the SDK to collect:
+
+    - The last Measurement.
+    - The timestamp of the last Measurement.
+    """
+
+    def _create_aggregation(
+        self,
+        instrument: Instrument,
+        attributes: Attributes,
+        reservoir_factory: Callable[
+            [Type[_Aggregation]], ExemplarReservoirBuilder
+        ],
+        start_time_unix_nano: int,
+    ) -> _Aggregation:
+        return _LastValueAggregation(
+            attributes,
+            reservoir_builder=reservoir_factory(_LastValueAggregation),
+        )
+
+
+class DropAggregation(Aggregation):
+    """Using this aggregation will make all measurements be ignored."""
+
+    def _create_aggregation(
+        self,
+        instrument: Instrument,
+        attributes: Attributes,
+        reservoir_factory: Callable[
+            [Type[_Aggregation]], ExemplarReservoirBuilder
+        ],
+        start_time_unix_nano: int,
+    ) -> _Aggregation:
+        return _DropAggregation(
+            attributes, reservoir_factory(_DropAggregation)
+        )
diff --git a/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exceptions.py b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exceptions.py
new file mode 100644
index 00000000..0f8c3a75
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exceptions.py
@@ -0,0 +1,17 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+class MetricsTimeoutError(Exception):
+    """Raised when a metrics function times out"""
diff --git a/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exemplar/__init__.py b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exemplar/__init__.py
new file mode 100644
index 00000000..ee93dd18
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exemplar/__init__.py
@@ -0,0 +1,39 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .exemplar import Exemplar
+from .exemplar_filter import (
+    AlwaysOffExemplarFilter,
+    AlwaysOnExemplarFilter,
+    ExemplarFilter,
+    TraceBasedExemplarFilter,
+)
+from .exemplar_reservoir import (
+    AlignedHistogramBucketExemplarReservoir,
+    ExemplarReservoir,
+    ExemplarReservoirBuilder,
+    SimpleFixedSizeExemplarReservoir,
+)
+
+__all__ = [
+    "Exemplar",
+    "ExemplarFilter",
+    "AlwaysOffExemplarFilter",
+    "AlwaysOnExemplarFilter",
+    "TraceBasedExemplarFilter",
+    "AlignedHistogramBucketExemplarReservoir",
+    "ExemplarReservoir",
+    "ExemplarReservoirBuilder",
+    "SimpleFixedSizeExemplarReservoir",
+]
diff --git a/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exemplar/exemplar.py b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exemplar/exemplar.py
new file mode 100644
index 00000000..95582e16
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exemplar/exemplar.py
@@ -0,0 +1,50 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import dataclasses
+from typing import Optional, Union
+
+from opentelemetry.util.types import Attributes
+
+
+@dataclasses.dataclass(frozen=True)
+class Exemplar:
+    """A representation of an exemplar, which is a sample input measurement.
+
+    Exemplars also hold information about the environment when the measurement
+    was recorded, for example the span and trace ID of the active span when the
+    exemplar was recorded.
+
+    Attributes
+        trace_id: (optional) The trace associated with a recording
+        span_id: (optional) The span associated with a recording
+        time_unix_nano: The time of the observation
+        value: The recorded value
+        filtered_attributes: A set of filtered attributes which provide additional insight into the Context when the observation was made.
+
+    References:
+        https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/metrics/data-model.md#exemplars
+        https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/metrics/sdk.md#exemplar
+    """
+
+    # TODO Fix doc - if using valid Google `Attributes:` key, the attributes are duplicated
+    # one will come from napoleon extension and the other from autodoc extension. This
+    # will raise an sphinx error of duplicated object description
+    # See https://github.com/sphinx-doc/sphinx/issues/8664
+
+    filtered_attributes: Attributes
+    value: Union[int, float]
+    time_unix_nano: int
+    span_id: Optional[int] = None
+    trace_id: Optional[int] = None
diff --git a/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exemplar/exemplar_filter.py b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exemplar/exemplar_filter.py
new file mode 100644
index 00000000..8961d101
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exemplar/exemplar_filter.py
@@ -0,0 +1,134 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from abc import ABC, abstractmethod
+from typing import Union
+
+from opentelemetry import trace
+from opentelemetry.context import Context
+from opentelemetry.trace.span import INVALID_SPAN
+from opentelemetry.util.types import Attributes
+
+
+class ExemplarFilter(ABC):
+    """``ExemplarFilter`` determines which measurements are eligible for becoming an
+    ``Exemplar``.
+
+    Exemplar filters are used to filter measurements before attempting to store them
+    in a reservoir.
+
+    Reference:
+        https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/metrics/sdk.md#exemplarfilter
+    """
+
+    @abstractmethod
+    def should_sample(
+        self,
+        value: Union[int, float],
+        time_unix_nano: int,
+        attributes: Attributes,
+        context: Context,
+    ) -> bool:
+        """Returns whether or not a reservoir should attempt to filter a measurement.
+
+        Args:
+            value: The value of the measurement
+            timestamp: A timestamp that best represents when the measurement was taken
+            attributes: The complete set of measurement attributes
+            context: The Context of the measurement
+        """
+        raise NotImplementedError(
+            "ExemplarFilter.should_sample is not implemented"
+        )
+
+
+class AlwaysOnExemplarFilter(ExemplarFilter):
+    """An ExemplarFilter which makes all measurements eligible for being an Exemplar.
+
+    Reference:
+        https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/metrics/sdk.md#alwayson
+    """
+
+    def should_sample(
+        self,
+        value: Union[int, float],
+        time_unix_nano: int,
+        attributes: Attributes,
+        context: Context,
+    ) -> bool:
+        """Returns whether or not a reservoir should attempt to filter a measurement.
+
+        Args:
+            value: The value of the measurement
+            timestamp: A timestamp that best represents when the measurement was taken
+            attributes: The complete set of measurement attributes
+            context: The Context of the measurement
+        """
+        return True
+
+
+class AlwaysOffExemplarFilter(ExemplarFilter):
+    """An ExemplarFilter which makes no measurements eligible for being an Exemplar.
+
+    Using this ExemplarFilter is as good as disabling Exemplar feature.
+
+    Reference:
+        https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/metrics/sdk.md#alwaysoff
+    """
+
+    def should_sample(
+        self,
+        value: Union[int, float],
+        time_unix_nano: int,
+        attributes: Attributes,
+        context: Context,
+    ) -> bool:
+        """Returns whether or not a reservoir should attempt to filter a measurement.
+
+        Args:
+            value: The value of the measurement
+            timestamp: A timestamp that best represents when the measurement was taken
+            attributes: The complete set of measurement attributes
+            context: The Context of the measurement
+        """
+        return False
+
+
+class TraceBasedExemplarFilter(ExemplarFilter):
+    """An ExemplarFilter which makes those measurements eligible for being an Exemplar,
+    which are recorded in the context of a sampled parent span.
+
+    Reference:
+        https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/metrics/sdk.md#tracebased
+    """
+
+    def should_sample(
+        self,
+        value: Union[int, float],
+        time_unix_nano: int,
+        attributes: Attributes,
+        context: Context,
+    ) -> bool:
+        """Returns whether or not a reservoir should attempt to filter a measurement.
+
+        Args:
+            value: The value of the measurement
+            timestamp: A timestamp that best represents when the measurement was taken
+            attributes: The complete set of measurement attributes
+            context: The Context of the measurement
+        """
+        span = trace.get_current_span(context)
+        if span == INVALID_SPAN:
+            return False
+        return span.get_span_context().trace_flags.sampled
diff --git a/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exemplar/exemplar_reservoir.py b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exemplar/exemplar_reservoir.py
new file mode 100644
index 00000000..22d1ee9f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exemplar/exemplar_reservoir.py
@@ -0,0 +1,332 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from abc import ABC, abstractmethod
+from collections import defaultdict
+from random import randrange
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    List,
+    Mapping,
+    Optional,
+    Sequence,
+    Union,
+)
+
+from opentelemetry import trace
+from opentelemetry.context import Context
+from opentelemetry.trace.span import INVALID_SPAN
+from opentelemetry.util.types import Attributes
+
+from .exemplar import Exemplar
+
+
+class ExemplarReservoir(ABC):
+    """ExemplarReservoir provide a method to offer measurements to the reservoir
+    and another to collect accumulated Exemplars.
+
+    Note:
+        The constructor MUST accept ``**kwargs`` that may be set from aggregation
+        parameters.
+
+    Reference:
+        https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/metrics/sdk.md#exemplarreservoir
+    """
+
+    @abstractmethod
+    def offer(
+        self,
+        value: Union[int, float],
+        time_unix_nano: int,
+        attributes: Attributes,
+        context: Context,
+    ) -> None:
+        """Offers a measurement to be sampled.
+
+        Args:
+            value: Measured value
+            time_unix_nano: Measurement instant
+            attributes: Measurement attributes
+            context: Measurement context
+        """
+        raise NotImplementedError("ExemplarReservoir.offer is not implemented")
+
+    @abstractmethod
+    def collect(self, point_attributes: Attributes) -> List[Exemplar]:
+        """Returns accumulated Exemplars and also resets the reservoir for the next
+        sampling period
+
+        Args:
+            point_attributes: The attributes associated with metric point.
+
+        Returns:
+            a list of ``opentelemetry.sdk.metrics._internal.exemplar.exemplar.Exemplar`` s. Returned
+            exemplars contain the attributes that were filtered out by the aggregator,
+            but recorded alongside the original measurement.
+        """
+        raise NotImplementedError(
+            "ExemplarReservoir.collect is not implemented"
+        )
+
+
+class ExemplarBucket:
+    def __init__(self) -> None:
+        self.__value: Union[int, float] = 0
+        self.__attributes: Attributes = None
+        self.__time_unix_nano: int = 0
+        self.__span_id: Optional[int] = None
+        self.__trace_id: Optional[int] = None
+        self.__offered: bool = False
+
+    def offer(
+        self,
+        value: Union[int, float],
+        time_unix_nano: int,
+        attributes: Attributes,
+        context: Context,
+    ) -> None:
+        """Offers a measurement to be sampled.
+
+        Args:
+            value: Measured value
+            time_unix_nano: Measurement instant
+            attributes: Measurement attributes
+            context: Measurement context
+        """
+        self.__value = value
+        self.__time_unix_nano = time_unix_nano
+        self.__attributes = attributes
+        span = trace.get_current_span(context)
+        if span != INVALID_SPAN:
+            span_context = span.get_span_context()
+            self.__span_id = span_context.span_id
+            self.__trace_id = span_context.trace_id
+
+        self.__offered = True
+
+    def collect(self, point_attributes: Attributes) -> Optional[Exemplar]:
+        """May return an Exemplar and resets the bucket for the next sampling period."""
+        if not self.__offered:
+            return None
+
+        # filters out attributes from the measurement that are already included in the metric data point
+        # See the specification for more details:
+        # https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/metrics/sdk.md#exemplar
+        filtered_attributes = (
+            {
+                k: v
+                for k, v in self.__attributes.items()
+                if k not in point_attributes
+            }
+            if self.__attributes
+            else None
+        )
+
+        exemplar = Exemplar(
+            filtered_attributes,
+            self.__value,
+            self.__time_unix_nano,
+            self.__span_id,
+            self.__trace_id,
+        )
+        self.__reset()
+        return exemplar
+
+    def __reset(self) -> None:
+        """Reset the bucket state after a collection cycle."""
+        self.__value = 0
+        self.__attributes = {}
+        self.__time_unix_nano = 0
+        self.__span_id = None
+        self.__trace_id = None
+        self.__offered = False
+
+
+class BucketIndexError(ValueError):
+    """An exception raised when the bucket index cannot be found."""
+
+
+class FixedSizeExemplarReservoirABC(ExemplarReservoir):
+    """Abstract class for a reservoir with fixed size."""
+
+    def __init__(self, size: int, **kwargs) -> None:
+        super().__init__(**kwargs)
+        self._size: int = size
+        self._reservoir_storage: Mapping[int, ExemplarBucket] = defaultdict(
+            ExemplarBucket
+        )
+
+    def collect(self, point_attributes: Attributes) -> List[Exemplar]:
+        """Returns accumulated Exemplars and also resets the reservoir for the next
+        sampling period
+
+        Args:
+            point_attributes: The attributes associated with metric point.
+
+        Returns:
+            a list of ``opentelemetry.sdk.metrics._internal.exemplar.exemplar.Exemplar`` s. Returned
+            exemplars contain the attributes that were filtered out by the aggregator,
+            but recorded alongside the original measurement.
+        """
+        exemplars = [
+            e
+            for e in (
+                bucket.collect(point_attributes)
+                for _, bucket in sorted(self._reservoir_storage.items())
+            )
+            if e is not None
+        ]
+        self._reset()
+        return exemplars
+
+    def offer(
+        self,
+        value: Union[int, float],
+        time_unix_nano: int,
+        attributes: Attributes,
+        context: Context,
+    ) -> None:
+        """Offers a measurement to be sampled.
+
+        Args:
+            value: Measured value
+            time_unix_nano: Measurement instant
+            attributes: Measurement attributes
+            context: Measurement context
+        """
+        try:
+            index = self._find_bucket_index(
+                value, time_unix_nano, attributes, context
+            )
+
+            self._reservoir_storage[index].offer(
+                value, time_unix_nano, attributes, context
+            )
+        except BucketIndexError:
+            # Ignore invalid bucket index
+            pass
+
+    @abstractmethod
+    def _find_bucket_index(
+        self,
+        value: Union[int, float],
+        time_unix_nano: int,
+        attributes: Attributes,
+        context: Context,
+    ) -> int:
+        """Determines the bucket index for the given measurement.
+
+        It should be implemented by subclasses based on specific strategies.
+
+        Args:
+            value: Measured value
+            time_unix_nano: Measurement instant
+            attributes: Measurement attributes
+            context: Measurement context
+
+        Returns:
+            The bucket index
+
+        Raises:
+            BucketIndexError: If no bucket index can be found.
+        """
+
+    def _reset(self) -> None:
+        """Reset the reservoir by resetting any stateful logic after a collection cycle."""
+
+
+class SimpleFixedSizeExemplarReservoir(FixedSizeExemplarReservoirABC):
+    """This reservoir uses an uniformly-weighted sampling algorithm based on the number
+    of samples the reservoir has seen so far to determine if the offered measurements
+    should be sampled.
+
+    Reference:
+        https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/metrics/sdk.md#simplefixedsizeexemplarreservoir
+    """
+
+    def __init__(self, size: int = 1, **kwargs) -> None:
+        super().__init__(size, **kwargs)
+        self._measurements_seen: int = 0
+
+    def _reset(self) -> None:
+        super()._reset()
+        self._measurements_seen = 0
+
+    def _find_bucket_index(
+        self,
+        value: Union[int, float],
+        time_unix_nano: int,
+        attributes: Attributes,
+        context: Context,
+    ) -> int:
+        self._measurements_seen += 1
+        if self._measurements_seen < self._size:
+            return self._measurements_seen - 1
+
+        index = randrange(0, self._measurements_seen)
+        if index < self._size:
+            return index
+
+        raise BucketIndexError("Unable to find the bucket index.")
+
+
+class AlignedHistogramBucketExemplarReservoir(FixedSizeExemplarReservoirABC):
+    """This Exemplar reservoir takes a configuration parameter that is the
+    configuration of a Histogram. This implementation keeps the last seen measurement
+    that falls within a histogram bucket.
+
+    Reference:
+        https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/metrics/sdk.md#alignedhistogrambucketexemplarreservoir
+    """
+
+    def __init__(self, boundaries: Sequence[float], **kwargs) -> None:
+        super().__init__(len(boundaries) + 1, **kwargs)
+        self._boundaries: Sequence[float] = boundaries
+
+    def offer(
+        self,
+        value: Union[int, float],
+        time_unix_nano: int,
+        attributes: Attributes,
+        context: Context,
+    ) -> None:
+        """Offers a measurement to be sampled."""
+        index = self._find_bucket_index(
+            value, time_unix_nano, attributes, context
+        )
+        self._reservoir_storage[index].offer(
+            value, time_unix_nano, attributes, context
+        )
+
+    def _find_bucket_index(
+        self,
+        value: Union[int, float],
+        time_unix_nano: int,
+        attributes: Attributes,
+        context: Context,
+    ) -> int:
+        for index, boundary in enumerate(self._boundaries):
+            if value <= boundary:
+                return index
+        return len(self._boundaries)
+
+
+ExemplarReservoirBuilder = Callable[[Dict[str, Any]], ExemplarReservoir]
+ExemplarReservoirBuilder.__doc__ = """ExemplarReservoir builder.
+
+It may receive the Aggregation parameters it is bounded to; e.g.
+the _ExplicitBucketHistogramAggregation will provide the boundaries.
+"""
diff --git a/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exponential_histogram/__init__.py b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exponential_histogram/__init__.py
new file mode 100644
index 00000000..e69de29b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exponential_histogram/__init__.py
diff --git a/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exponential_histogram/buckets.py b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exponential_histogram/buckets.py
new file mode 100644
index 00000000..e8a93326
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exponential_histogram/buckets.py
@@ -0,0 +1,190 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from math import ceil, log2
+
+
+class Buckets:
+    # No method of this class is protected by locks because instances of this
+    # class are only used in methods that are protected by locks themselves.
+
+    def __init__(self):
+        self._counts = [0]
+
+        # The term index refers to the number of the exponential histogram bucket
+        # used to determine its boundaries. The lower boundary of a bucket is
+        # determined by base ** index and the upper boundary of a bucket is
+        # determined by base ** (index + 1). index values are signedto account
+        # for values less than or equal to 1.
+
+        # self._index_* will all have values equal to a certain index that is
+        # determined by the corresponding mapping _map_to_index function and
+        # the value of the index depends on the value passed to _map_to_index.
+
+        # Index of the 0th position in self._counts: self._counts[0] is the
+        # count in the bucket with index self.__index_base.
+        self.__index_base = 0
+
+        # self.__index_start is the smallest index value represented in
+        # self._counts.
+        self.__index_start = 0
+
+        # self.__index_start is the largest index value represented in
+        # self._counts.
+        self.__index_end = 0
+
+    @property
+    def index_start(self) -> int:
+        return self.__index_start
+
+    @index_start.setter
+    def index_start(self, value: int) -> None:
+        self.__index_start = value
+
+    @property
+    def index_end(self) -> int:
+        return self.__index_end
+
+    @index_end.setter
+    def index_end(self, value: int) -> None:
+        self.__index_end = value
+
+    @property
+    def index_base(self) -> int:
+        return self.__index_base
+
+    @index_base.setter
+    def index_base(self, value: int) -> None:
+        self.__index_base = value
+
+    @property
+    def counts(self):
+        return self._counts
+
+    def get_offset_counts(self):
+        bias = self.__index_base - self.__index_start
+        return self._counts[-bias:] + self._counts[:-bias]
+
+    def grow(self, needed: int, max_size: int) -> None:
+        size = len(self._counts)
+        bias = self.__index_base - self.__index_start
+        old_positive_limit = size - bias
+
+        # 2 ** ceil(log2(needed)) finds the smallest power of two that is larger
+        # or equal than needed:
+        # 2 ** ceil(log2(1)) == 1
+        # 2 ** ceil(log2(2)) == 2
+        # 2 ** ceil(log2(3)) == 4
+        # 2 ** ceil(log2(4)) == 4
+        # 2 ** ceil(log2(5)) == 8
+        # 2 ** ceil(log2(6)) == 8
+        # 2 ** ceil(log2(7)) == 8
+        # 2 ** ceil(log2(8)) == 8
+        new_size = min(2 ** ceil(log2(needed)), max_size)
+
+        new_positive_limit = new_size - bias
+
+        tmp = [0] * new_size
+        tmp[new_positive_limit:] = self._counts[old_positive_limit:]
+        tmp[0:old_positive_limit] = self._counts[0:old_positive_limit]
+        self._counts = tmp
+
+    @property
+    def offset(self) -> int:
+        return self.__index_start
+
+    def __len__(self) -> int:
+        if len(self._counts) == 0:
+            return 0
+
+        if self.__index_end == self.__index_start and self[0] == 0:
+            return 0
+
+        return self.__index_end - self.__index_start + 1
+
+    def __getitem__(self, key: int) -> int:
+        bias = self.__index_base - self.__index_start
+
+        if key < bias:
+            key += len(self._counts)
+
+        key -= bias
+
+        return self._counts[key]
+
+    def downscale(self, amount: int) -> None:
+        """
+        Rotates, then collapses 2 ** amount to 1 buckets.
+        """
+
+        bias = self.__index_base - self.__index_start
+
+        if bias != 0:
+            self.__index_base = self.__index_start
+
+            # [0, 1, 2, 3, 4] Original backing array
+
+            self._counts = self._counts[::-1]
+            # [4, 3, 2, 1, 0]
+
+            self._counts = (
+                self._counts[:bias][::-1] + self._counts[bias:][::-1]
+            )
+            # [3, 4, 0, 1, 2] This is a rotation of the backing array.
+
+        size = 1 + self.__index_end - self.__index_start
+        each = 1 << amount
+        inpos = 0
+        outpos = 0
+
+        pos = self.__index_start
+
+        while pos <= self.__index_end:
+            mod = pos % each
+            if mod < 0:
+                mod += each
+
+            index = mod
+
+            while index < each and inpos < size:
+                if outpos != inpos:
+                    self._counts[outpos] += self._counts[inpos]
+                    self._counts[inpos] = 0
+
+                inpos += 1
+                pos += 1
+                index += 1
+
+            outpos += 1
+
+        self.__index_start >>= amount
+        self.__index_end >>= amount
+        self.__index_base = self.__index_start
+
+    def increment_bucket(self, bucket_index: int, increment: int = 1) -> None:
+        self._counts[bucket_index] += increment
+
+    def copy_empty(self) -> "Buckets":
+        copy = Buckets()
+
+        # pylint: disable=no-member
+        # pylint: disable=protected-access
+        # pylint: disable=attribute-defined-outside-init
+        # pylint: disable=invalid-name
+        copy._Buckets__index_base = self._Buckets__index_base
+        copy._Buckets__index_start = self._Buckets__index_start
+        copy._Buckets__index_end = self._Buckets__index_end
+        copy._counts = [0 for _ in self._counts]
+
+        return copy
diff --git a/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exponential_histogram/mapping/__init__.py b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exponential_histogram/mapping/__init__.py
new file mode 100644
index 00000000..387b1d14
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exponential_histogram/mapping/__init__.py
@@ -0,0 +1,98 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from abc import ABC, abstractmethod
+
+
+class Mapping(ABC):
+    """
+    Parent class for `LogarithmMapping` and `ExponentialMapping`.
+    """
+
+    # pylint: disable=no-member
+    def __new__(cls, scale: int):
+        with cls._mappings_lock:
+            # cls._mappings and cls._mappings_lock are implemented in each of
+            # the child classes as a dictionary and a lock, respectively. They
+            # are not instantiated here because that would lead to both child
+            # classes having the same instance of cls._mappings and
+            # cls._mappings_lock.
+            if scale not in cls._mappings:
+                cls._mappings[scale] = super().__new__(cls)
+                cls._mappings[scale]._init(scale)
+
+        return cls._mappings[scale]
+
+    @abstractmethod
+    def _init(self, scale: int) -> None:
+        # pylint: disable=attribute-defined-outside-init
+
+        if scale > self._get_max_scale():
+            # pylint: disable=broad-exception-raised
+            raise Exception(f"scale is larger than {self._max_scale}")
+
+        if scale < self._get_min_scale():
+            # pylint: disable=broad-exception-raised
+            raise Exception(f"scale is smaller than {self._min_scale}")
+
+        # The size of the exponential histogram buckets is determined by a
+        # parameter known as scale, larger values of scale will produce smaller
+        # buckets. Bucket boundaries of the exponential histogram are located
+        # at integer powers of the base, where:
+        #
+        # base = 2 ** (2 ** (-scale))
+        # https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/metrics/data-model.md#all-scales-use-the-logarithm-function
+        self._scale = scale
+
+    @abstractmethod
+    def _get_min_scale(self) -> int:
+        """
+        Return the smallest possible value for the mapping scale
+        """
+
+    @abstractmethod
+    def _get_max_scale(self) -> int:
+        """
+        Return the largest possible value for the mapping scale
+        """
+
+    @abstractmethod
+    def map_to_index(self, value: float) -> int:
+        """
+        Maps positive floating point values to indexes corresponding to
+        `Mapping.scale`. Implementations are not expected to handle zeros,
+        +inf, NaN, or negative values.
+        """
+
+    @abstractmethod
+    def get_lower_boundary(self, index: int) -> float:
+        """
+        Returns the lower boundary of a given bucket index. The index is
+        expected to map onto a range that is at least partially inside the
+        range of normal floating point values.  If the corresponding
+        bucket's upper boundary is less than or equal to 2 ** -1022,
+        :class:`~opentelemetry.sdk.metrics.MappingUnderflowError`
+        will be raised. If the corresponding bucket's lower boundary is greater
+        than ``sys.float_info.max``,
+        :class:`~opentelemetry.sdk.metrics.MappingOverflowError`
+        will be raised.
+        """
+
+    @property
+    def scale(self) -> int:
+        """
+        Returns the parameter that controls the resolution of this mapping.
+        See: https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/metrics/datamodel.md#exponential-scale
+        """
+        return self._scale
diff --git a/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exponential_histogram/mapping/errors.py b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exponential_histogram/mapping/errors.py
new file mode 100644
index 00000000..477ed6f0
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exponential_histogram/mapping/errors.py
@@ -0,0 +1,26 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+class MappingUnderflowError(Exception):
+    """
+    Raised when computing the lower boundary of an index that maps into a
+    denormal floating point value.
+    """
+
+
+class MappingOverflowError(Exception):
+    """
+    Raised when computing the lower boundary of an index that maps into +inf.
+    """
diff --git a/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exponential_histogram/mapping/exponent_mapping.py b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exponential_histogram/mapping/exponent_mapping.py
new file mode 100644
index 00000000..297bb7a4
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exponential_histogram/mapping/exponent_mapping.py
@@ -0,0 +1,141 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from math import ldexp
+from threading import Lock
+
+from opentelemetry.sdk.metrics._internal.exponential_histogram.mapping import (
+    Mapping,
+)
+from opentelemetry.sdk.metrics._internal.exponential_histogram.mapping.errors import (
+    MappingOverflowError,
+    MappingUnderflowError,
+)
+from opentelemetry.sdk.metrics._internal.exponential_histogram.mapping.ieee_754 import (
+    MANTISSA_WIDTH,
+    MAX_NORMAL_EXPONENT,
+    MIN_NORMAL_EXPONENT,
+    MIN_NORMAL_VALUE,
+    get_ieee_754_exponent,
+    get_ieee_754_mantissa,
+)
+
+
+class ExponentMapping(Mapping):
+    # Reference implementation here:
+    # https://github.com/open-telemetry/opentelemetry-go/blob/0e6f9c29c10d6078e8131418e1d1d166c7195d61/sdk/metric/aggregator/exponential/mapping/exponent/exponent.go
+
+    _mappings = {}
+    _mappings_lock = Lock()
+
+    _min_scale = -10
+    _max_scale = 0
+
+    def _get_min_scale(self):
+        # _min_scale defines the point at which the exponential mapping
+        # function becomes useless for 64-bit floats. With scale -10, ignoring
+        # subnormal values, bucket indices range from -1 to 1.
+        return -10
+
+    def _get_max_scale(self):
+        # _max_scale is the largest scale supported by exponential mapping. Use
+        # a logarithm mapping for larger scales.
+        return 0
+
+    def _init(self, scale: int):
+        # pylint: disable=attribute-defined-outside-init
+
+        super()._init(scale)
+
+        # self._min_normal_lower_boundary_index is the largest index such that
+        # base ** index < MIN_NORMAL_VALUE and
+        # base ** (index + 1) >= MIN_NORMAL_VALUE. An exponential histogram
+        # bucket with this index covers the range
+        # (base ** index, base (index + 1)], including MIN_NORMAL_VALUE. This
+        # is the smallest valid index that contains at least one normal value.
+        index = MIN_NORMAL_EXPONENT >> -self._scale
+
+        if -self._scale < 2:
+            # For scales -1 and 0, the maximum value 2 ** -1022 is a
+            # power-of-two multiple, meaning base ** index == MIN_NORMAL_VALUE.
+            # Subtracting 1 so that base ** (index + 1) == MIN_NORMAL_VALUE.
+            index -= 1
+
+        self._min_normal_lower_boundary_index = index
+
+        # self._max_normal_lower_boundary_index is the index such that
+        # base**index equals the greatest representable lower boundary. An
+        # exponential histogram bucket with this index covers the range
+        # ((2 ** 1024) / base, 2 ** 1024], which includes opentelemetry.sdk.
+        # metrics._internal.exponential_histogram.ieee_754.MAX_NORMAL_VALUE.
+        # This bucket is incomplete, since the upper boundary cannot be
+        # represented. One greater than this index corresponds with the bucket
+        # containing values > 2 ** 1024.
+        self._max_normal_lower_boundary_index = (
+            MAX_NORMAL_EXPONENT >> -self._scale
+        )
+
+    def map_to_index(self, value: float) -> int:
+        if value < MIN_NORMAL_VALUE:
+            return self._min_normal_lower_boundary_index
+
+        exponent = get_ieee_754_exponent(value)
+
+        # Positive integers are represented in binary as having an infinite
+        # amount of leading zeroes, for example 2 is represented as ...00010.
+
+        # A negative integer -x is represented in binary as the complement of
+        # (x - 1). For example, -4 is represented as the complement of 4 - 1
+        # == 3. 3 is represented as ...00011. Its compliment is ...11100, the
+        # binary representation of -4.
+
+        # get_ieee_754_mantissa(value) gets the positive integer made up
+        # from the rightmost MANTISSA_WIDTH bits (the mantissa) of the IEEE
+        # 754 representation of value. If value is an exact power of 2, all
+        # these MANTISSA_WIDTH bits would be all zeroes, and when 1 is
+        # subtracted the resulting value is -1. The binary representation of
+        # -1 is ...111, so when these bits are right shifted MANTISSA_WIDTH
+        # places, the resulting value for correction is -1. If value is not an
+        # exact power of 2, at least one of the rightmost MANTISSA_WIDTH
+        # bits would be 1 (even for values whose decimal part is 0, like 5.0
+        # since the IEEE 754 of such number is too the product of a power of 2
+        # (defined in the exponent part of the IEEE 754 representation) and the
+        # value defined in the mantissa). Having at least one of the rightmost
+        # MANTISSA_WIDTH bit being 1 means that get_ieee_754(value) will
+        # always be greater or equal to 1, and when 1 is subtracted, the
+        # result will be greater or equal to 0, whose representation in binary
+        # will be of at most MANTISSA_WIDTH ones that have an infinite
+        # amount of leading zeroes. When those MANTISSA_WIDTH bits are
+        # shifted to the right MANTISSA_WIDTH places, the resulting value
+        # will be 0.
+
+        # In summary, correction will be -1 if value is a power of 2, 0 if not.
+
+        # FIXME Document why we can assume value will not be 0, inf, or NaN.
+        correction = (get_ieee_754_mantissa(value) - 1) >> MANTISSA_WIDTH
+
+        return (exponent + correction) >> -self._scale
+
+    def get_lower_boundary(self, index: int) -> float:
+        if index < self._min_normal_lower_boundary_index:
+            raise MappingUnderflowError()
+
+        if index > self._max_normal_lower_boundary_index:
+            raise MappingOverflowError()
+
+        return ldexp(1, index << -self._scale)
+
+    @property
+    def scale(self) -> int:
+        return self._scale
diff --git a/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exponential_histogram/mapping/ieee_754.md b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exponential_histogram/mapping/ieee_754.md
new file mode 100644
index 00000000..0cf5c8c5
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exponential_histogram/mapping/ieee_754.md
@@ -0,0 +1,175 @@
+# IEEE 754 Explained
+
+IEEE 754 is a standard that defines a way to represent certain mathematical
+objects using binary numbers.
+
+## Binary Number Fields
+
+The binary numbers used in IEEE 754 can have different lengths, the length that
+is interesting for the purposes of this project is 64 bits. These binary
+numbers are made up of 3 contiguous fields of bits, from left to right:
+
+1. 1 sign bit
+2. 11 exponent bits
+3. 52 mantissa bits
+
+Depending on the values these fields have, the represented mathematical object
+can be one of:
+
+* Floating point number
+* Zero
+* NaN
+* Infinite
+
+## Floating Point Numbers
+
+IEEE 754 represents a floating point number $f$ using an exponential
+notation with 4 components: $sign$, $mantissa$, $base$ and $exponent$:
+
+$$f = sign \times mantissa \times base ^ {exponent}$$
+
+There are two possible representations of floating point numbers:
+_normal_ and _denormal_, which have different valid values for
+their $mantissa$ and $exponent$ fields.
+
+### Binary Representation
+
+$sign$, $mantissa$, and $exponent$ are represented in binary, the
+representation of each component has certain details explained next.
+
+$base$ is always $2$ and it is not represented in binary.
+
+#### Sign
+
+$sign$ can have 2 values:
+
+1. $1$ if the `sign` bit is `0`
+2. $-1$ if the `sign` bit is `1`.
+
+#### Mantissa
+
+##### Normal Floating Point Numbers
+
+$mantissa$ is a positive fractional number whose integer part is $1$, for example
+$1.2345 \dots$. The `mantissa` bits represent only the fractional part and the
+$mantissa$ value can be calculated as:
+
+$$mantissa = 1 + \sum_{i=1}^{52} b_{i} \times 2^{-i} = 1 + \frac{b_{1}}{2^{1}} + \frac{b_{2}}{2^{2}} + \dots + \frac{b_{51}}{2^{51}} + \frac{b_{52}}{2^{52}}$$
+
+Where $b_{i}$ is:
+
+1. $0$ if the bit at the position `i - 1` is `0`.
+2. $1$ if the bit at the position `i - 1` is `1`.
+
+##### Denormal Floating Point Numbers
+
+$mantissa$ is a positive fractional number whose integer part is $0$, for example
+$0.12345 \dots$. The `mantissa` bits represent only the fractional part and the
+$mantissa$ value can be calculated as:
+
+$$mantissa = \sum_{i=1}^{52} b_{i} \times 2^{-i} = \frac{b_{1}}{2^{1}} + \frac{b_{2}}{2^{2}} + \dots + \frac{b_{51}}{2^{51}} + \frac{b_{52}}{2^{52}}$$
+
+Where $b_{i}$ is:
+
+1. $0$ if the bit at the position `i - 1` is `0`.
+2. $1$ if the bit at the position `i - 1` is `1`.
+
+#### Exponent
+
+##### Normal Floating Point Numbers
+
+Only the following bit sequences are allowed: `00000000001` to `11111111110`.
+That is, there must be at least one `0` and one `1` in the exponent bits.
+
+The actual value of the $exponent$ can be calculated as:
+
+$$exponent = v - bias$$
+
+where $v$ is the value of the binary number in the exponent bits and $bias$ is $1023$.
+Considering the restrictions above, the respective minimum and maximum values for the
+exponent are:
+
+1. `00000000001` = $1$, $1 - 1023 = -1022$
+2. `11111111110` = $2046$, $2046 - 1023 = 1023$
+
+So, $exponent$ is an integer in the range $\left[-1022, 1023\right]$.
+
+
+##### Denormal Floating Point Numbers
+
+$exponent$ is always $-1022$. Nevertheless, it is always represented as `00000000000`.
+
+### Normal and Denormal Floating Point Numbers
+
+The smallest absolute value a normal floating point number can have is calculated
+like this:
+
+$$1 \times 1.0\dots0 \times 2^{-1022} = 2.2250738585072014 \times 10^{-308}$$
+
+Since normal floating point numbers always have a $1$ as the integer part of the
+$mantissa$, then smaller values can be achieved by using the smallest possible exponent
+( $-1022$ ) and a $0$ in the integer part of the $mantissa$, but significant digits are lost.
+
+The smallest absolute value a denormal floating point number can have is calculated
+like this:
+
+$$1 \times 2^{-52} \times 2^{-1022} = 5 \times 10^{-324}$$
+
+## Zero
+
+Zero is represented like this:
+
+* Sign bit: `X`
+* Exponent bits: `00000000000`
+* Mantissa bits: `0000000000000000000000000000000000000000000000000000`
+
+where `X` means `0` or `1`.
+
+## NaN
+
+There are 2 kinds of NaNs that are represented:
+
+1. QNaNs (Quiet NaNs): represent the result of indeterminate operations.
+2. SNaNs (Signalling NaNs): represent the result of invalid operations.
+
+### QNaNs
+
+QNaNs are represented like this:
+
+* Sign bit: `X`
+* Exponent bits: `11111111111`
+* Mantissa bits: `1XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX`
+
+where `X` means `0` or `1`.
+
+### SNaNs
+
+SNaNs are represented like this:
+
+* Sign bit: `X`
+* Exponent bits: `11111111111`
+* Mantissa bits: `0XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX1`
+
+where `X` means `0` or `1`.
+
+## Infinite
+
+### Positive Infinite
+
+Positive infinite is represented like this:
+
+* Sign bit: `0`
+* Exponent bits: `11111111111`
+* Mantissa bits: `0000000000000000000000000000000000000000000000000000`
+
+where `X` means `0` or `1`.
+
+### Negative Infinite
+
+Negative infinite is represented like this:
+
+* Sign bit: `1`
+* Exponent bits: `11111111111`
+* Mantissa bits: `0000000000000000000000000000000000000000000000000000`
+
+where `X` means `0` or `1`.
diff --git a/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exponential_histogram/mapping/ieee_754.py b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exponential_histogram/mapping/ieee_754.py
new file mode 100644
index 00000000..d4b7e861
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exponential_histogram/mapping/ieee_754.py
@@ -0,0 +1,117 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from ctypes import c_double, c_uint64
+from sys import float_info
+
+# IEEE 754 64-bit floating point numbers use 11 bits for the exponent and 52
+# bits for the mantissa.
+MANTISSA_WIDTH = 52
+EXPONENT_WIDTH = 11
+
+# This mask is equivalent to 52 "1" bits (there are 13 hexadecimal 4-bit "f"s
+# in the mantissa mask, 13 * 4 == 52) or 0xfffffffffffff in hexadecimal.
+MANTISSA_MASK = (1 << MANTISSA_WIDTH) - 1
+
+# There are 11 bits for the exponent, but the exponent values 0 (11 "0"
+# bits) and 2047 (11 "1" bits) have special meanings so the exponent range is
+# from 1 to 2046. To calculate the exponent value, 1023 (the bias) is
+# subtracted from the exponent, so the exponent value range is from -1022 to
+# +1023.
+EXPONENT_BIAS = (2 ** (EXPONENT_WIDTH - 1)) - 1
+
+# All the exponent mask bits are set to 1 for the 11 exponent bits.
+EXPONENT_MASK = ((1 << EXPONENT_WIDTH) - 1) << MANTISSA_WIDTH
+
+# The sign mask has the first bit set to 1 and the rest to 0.
+SIGN_MASK = 1 << (EXPONENT_WIDTH + MANTISSA_WIDTH)
+
+# For normal floating point numbers, the exponent can have a value in the
+# range [-1022, 1023].
+MIN_NORMAL_EXPONENT = -EXPONENT_BIAS + 1
+MAX_NORMAL_EXPONENT = EXPONENT_BIAS
+
+# The smallest possible normal value is 2.2250738585072014e-308.
+# This value is the result of using the smallest possible number in the
+# mantissa, 1.0000000000000000000000000000000000000000000000000000 (52 "0"s in
+# the fractional part) and a single "1" in the exponent.
+# Finally 1 * (2 ** -1022) = 2.2250738585072014e-308.
+MIN_NORMAL_VALUE = float_info.min
+
+# Greatest possible normal value (1.7976931348623157e+308)
+# The binary representation of a float in scientific notation uses (for the
+# mantissa) one bit for the integer part (which is implicit) and 52 bits for
+# the fractional part. Consider a float binary 1.111. It is equal to 1 + 1/2 +
+# 1/4 + 1/8. The greatest possible value in the 52-bit binary mantissa would be
+# then 1.1111111111111111111111111111111111111111111111111111 (52 "1"s in the
+# fractional part) whose decimal value is 1.9999999999999998. Finally,
+# 1.9999999999999998 * (2 ** 1023) = 1.7976931348623157e+308.
+MAX_NORMAL_VALUE = float_info.max
+
+
+def get_ieee_754_exponent(value: float) -> int:
+    """
+    Gets the exponent of the IEEE 754 representation of a float.
+    """
+
+    return (
+        (
+            # This step gives the integer that corresponds to the IEEE 754
+            # representation of a float. For example, consider
+            # -MAX_NORMAL_VALUE for an example. We choose this value because
+            # of its binary representation which makes easy to understand the
+            # subsequent operations.
+            #
+            # c_uint64.from_buffer(c_double(-MAX_NORMAL_VALUE)).value == 18442240474082181119
+            # bin(18442240474082181119) == '0b1111111111101111111111111111111111111111111111111111111111111111'
+            #
+            # The first bit of the previous binary number is the sign bit: 1 (1 means negative, 0 means positive)
+            # The next 11 bits are the exponent bits: 11111111110
+            # The next 52 bits are the mantissa bits: 1111111111111111111111111111111111111111111111111111
+            #
+            # This step isolates the exponent bits, turning every bit outside
+            # of the exponent field (sign and mantissa bits) to 0.
+            c_uint64.from_buffer(c_double(value)).value & EXPONENT_MASK
+            # For the example this means:
+            # 18442240474082181119 & EXPONENT_MASK == 9214364837600034816
+            # bin(9214364837600034816) == '0b111111111100000000000000000000000000000000000000000000000000000'
+            # Notice that the previous binary representation does not include
+            # leading zeroes, so the sign bit is not included since it is a
+            # zero.
+        )
+        # This step moves the exponent bits to the right, removing the
+        # mantissa bits that were set to 0 by the previous step. This
+        # leaves the IEEE 754 exponent value, ready for the next step.
+        >> MANTISSA_WIDTH
+        # For the example this means:
+        # 9214364837600034816 >> MANTISSA_WIDTH == 2046
+        # bin(2046) == '0b11111111110'
+        # As shown above, these are the original 11 bits that correspond to the
+        # exponent.
+        # This step subtracts the exponent bias from the IEEE 754 value,
+        # leaving the actual exponent value.
+    ) - EXPONENT_BIAS
+    # For the example this means:
+    # 2046 - EXPONENT_BIAS == 1023
+    # As mentioned in a comment above, the largest value for the exponent is
+
+
+def get_ieee_754_mantissa(value: float) -> int:
+    return (
+        c_uint64.from_buffer(c_double(value)).value
+        # This step isolates the mantissa bits. There is no need to do any
+        # bit shifting as the mantissa bits are already the rightmost field
+        # in an IEEE 754 representation.
+        & MANTISSA_MASK
+    )
diff --git a/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exponential_histogram/mapping/logarithm_mapping.py b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exponential_histogram/mapping/logarithm_mapping.py
new file mode 100644
index 00000000..e73f3a81
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/exponential_histogram/mapping/logarithm_mapping.py
@@ -0,0 +1,138 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from math import exp, floor, ldexp, log
+from threading import Lock
+
+from opentelemetry.sdk.metrics._internal.exponential_histogram.mapping import (
+    Mapping,
+)
+from opentelemetry.sdk.metrics._internal.exponential_histogram.mapping.errors import (
+    MappingOverflowError,
+    MappingUnderflowError,
+)
+from opentelemetry.sdk.metrics._internal.exponential_histogram.mapping.ieee_754 import (
+    MAX_NORMAL_EXPONENT,
+    MIN_NORMAL_EXPONENT,
+    MIN_NORMAL_VALUE,
+    get_ieee_754_exponent,
+    get_ieee_754_mantissa,
+)
+
+
+class LogarithmMapping(Mapping):
+    # Reference implementation here:
+    # https://github.com/open-telemetry/opentelemetry-go/blob/0e6f9c29c10d6078e8131418e1d1d166c7195d61/sdk/metric/aggregator/exponential/mapping/logarithm/logarithm.go
+
+    _mappings = {}
+    _mappings_lock = Lock()
+
+    _min_scale = 1
+    _max_scale = 20
+
+    def _get_min_scale(self):
+        # _min_scale ensures that ExponentMapping is used for zero and negative
+        # scale values.
+        return self._min_scale
+
+    def _get_max_scale(self):
+        # FIXME The Go implementation uses a value of 20 here, find out the
+        # right value for this implementation, more information here:
+        # https://github.com/lightstep/otel-launcher-go/blob/c9ca8483be067a39ab306b09060446e7fda65f35/lightstep/sdk/metric/aggregator/histogram/structure/README.md#mapping-function
+        # https://github.com/open-telemetry/opentelemetry-go/blob/0e6f9c29c10d6078e8131418e1d1d166c7195d61/sdk/metric/aggregator/exponential/mapping/logarithm/logarithm.go#L32-L45
+        return self._max_scale
+
+    def _init(self, scale: int):
+        # pylint: disable=attribute-defined-outside-init
+
+        super()._init(scale)
+
+        # self._scale_factor is defined as a multiplier because multiplication
+        # is faster than division. self._scale_factor is defined as:
+        # index = log(value) * self._scale_factor
+        # Where:
+        # index = log(value) / log(base)
+        # index = log(value) / log(2 ** (2 ** -scale))
+        # index = log(value) / ((2 ** -scale) * log(2))
+        # index = log(value) * ((1 / log(2)) * (2 ** scale))
+        # self._scale_factor = ((1 / log(2)) * (2 ** scale))
+        # self._scale_factor = (1 /log(2)) * (2 ** scale)
+        # self._scale_factor = ldexp(1 / log(2), scale)
+        # This implementation was copied from a Java prototype. See:
+        # https://github.com/newrelic-experimental/newrelic-sketch-java/blob/1ce245713603d61ba3a4510f6df930a5479cd3f6/src/main/java/com/newrelic/nrsketch/indexer/LogIndexer.java
+        # for the equations used here.
+        self._scale_factor = ldexp(1 / log(2), scale)
+
+        # self._min_normal_lower_boundary_index is the index such that
+        # base ** index == MIN_NORMAL_VALUE. An exponential histogram bucket
+        # with this index covers the range
+        # (MIN_NORMAL_VALUE, MIN_NORMAL_VALUE * base]. One less than this index
+        # corresponds with the bucket containing values <= MIN_NORMAL_VALUE.
+        self._min_normal_lower_boundary_index = (
+            MIN_NORMAL_EXPONENT << self._scale
+        )
+
+        # self._max_normal_lower_boundary_index is the index such that
+        # base ** index equals the greatest representable lower boundary. An
+        # exponential histogram bucket with this index covers the range
+        # ((2 ** 1024) / base, 2 ** 1024], which includes opentelemetry.sdk.
+        # metrics._internal.exponential_histogram.ieee_754.MAX_NORMAL_VALUE.
+        # This bucket is incomplete, since the upper boundary cannot be
+        # represented. One greater than this index corresponds with the bucket
+        # containing values > 2 ** 1024.
+        self._max_normal_lower_boundary_index = (
+            (MAX_NORMAL_EXPONENT + 1) << self._scale
+        ) - 1
+
+    def map_to_index(self, value: float) -> int:
+        """
+        Maps positive floating point values to indexes corresponding to scale.
+        """
+
+        # value is subnormal
+        if value <= MIN_NORMAL_VALUE:
+            return self._min_normal_lower_boundary_index - 1
+
+        # value is an exact power of two.
+        if get_ieee_754_mantissa(value) == 0:
+            exponent = get_ieee_754_exponent(value)
+            return (exponent << self._scale) - 1
+
+        return min(
+            floor(log(value) * self._scale_factor),
+            self._max_normal_lower_boundary_index,
+        )
+
+    def get_lower_boundary(self, index: int) -> float:
+        if index >= self._max_normal_lower_boundary_index:
+            if index == self._max_normal_lower_boundary_index:
+                return 2 * exp(
+                    (index - (1 << self._scale)) / self._scale_factor
+                )
+            raise MappingOverflowError()
+
+        if index <= self._min_normal_lower_boundary_index:
+            if index == self._min_normal_lower_boundary_index:
+                return MIN_NORMAL_VALUE
+            if index == self._min_normal_lower_boundary_index - 1:
+                return (
+                    exp((index + (1 << self._scale)) / self._scale_factor) / 2
+                )
+            raise MappingUnderflowError()
+
+        return exp(index / self._scale_factor)
+
+    @property
+    def scale(self) -> int:
+        return self._scale
diff --git a/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/export/__init__.py b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/export/__init__.py
new file mode 100644
index 00000000..52c68334
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/export/__init__.py
@@ -0,0 +1,576 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import annotations
+
+import math
+import os
+import weakref
+from abc import ABC, abstractmethod
+from enum import Enum
+from logging import getLogger
+from os import environ, linesep
+from sys import stdout
+from threading import Event, Lock, RLock, Thread
+from time import time_ns
+from typing import IO, Callable, Iterable, Optional
+
+from typing_extensions import final
+
+# This kind of import is needed to avoid Sphinx errors.
+import opentelemetry.sdk.metrics._internal
+from opentelemetry.context import (
+    _SUPPRESS_INSTRUMENTATION_KEY,
+    attach,
+    detach,
+    set_value,
+)
+from opentelemetry.sdk.environment_variables import (
+    OTEL_METRIC_EXPORT_INTERVAL,
+    OTEL_METRIC_EXPORT_TIMEOUT,
+)
+from opentelemetry.sdk.metrics._internal.aggregation import (
+    AggregationTemporality,
+    DefaultAggregation,
+)
+from opentelemetry.sdk.metrics._internal.exceptions import MetricsTimeoutError
+from opentelemetry.sdk.metrics._internal.instrument import (
+    Counter,
+    Gauge,
+    Histogram,
+    ObservableCounter,
+    ObservableGauge,
+    ObservableUpDownCounter,
+    UpDownCounter,
+    _Counter,
+    _Gauge,
+    _Histogram,
+    _ObservableCounter,
+    _ObservableGauge,
+    _ObservableUpDownCounter,
+    _UpDownCounter,
+)
+from opentelemetry.sdk.metrics._internal.point import MetricsData
+from opentelemetry.util._once import Once
+
+_logger = getLogger(__name__)
+
+
+class MetricExportResult(Enum):
+    """Result of exporting a metric
+
+    Can be any of the following values:"""
+
+    SUCCESS = 0
+    FAILURE = 1
+
+
+class MetricExporter(ABC):
+    """Interface for exporting metrics.
+
+    Interface to be implemented by services that want to export metrics received
+    in their own format.
+
+    Args:
+        preferred_temporality: Used by `opentelemetry.sdk.metrics.export.PeriodicExportingMetricReader` to
+            configure exporter level preferred temporality. See `opentelemetry.sdk.metrics.export.MetricReader` for
+            more details on what preferred temporality is.
+        preferred_aggregation: Used by `opentelemetry.sdk.metrics.export.PeriodicExportingMetricReader` to
+            configure exporter level preferred aggregation. See `opentelemetry.sdk.metrics.export.MetricReader` for
+            more details on what preferred aggregation is.
+    """
+
+    def __init__(
+        self,
+        preferred_temporality: dict[type, AggregationTemporality]
+        | None = None,
+        preferred_aggregation: dict[
+            type, "opentelemetry.sdk.metrics.view.Aggregation"
+        ]
+        | None = None,
+    ) -> None:
+        self._preferred_temporality = preferred_temporality
+        self._preferred_aggregation = preferred_aggregation
+
+    @abstractmethod
+    def export(
+        self,
+        metrics_data: MetricsData,
+        timeout_millis: float = 10_000,
+        **kwargs,
+    ) -> MetricExportResult:
+        """Exports a batch of telemetry data.
+
+        Args:
+            metrics: The list of `opentelemetry.sdk.metrics.export.Metric` objects to be exported
+
+        Returns:
+            The result of the export
+        """
+
+    @abstractmethod
+    def force_flush(self, timeout_millis: float = 10_000) -> bool:
+        """
+        Ensure that export of any metrics currently received by the exporter
+        are completed as soon as possible.
+        """
+
+    @abstractmethod
+    def shutdown(self, timeout_millis: float = 30_000, **kwargs) -> None:
+        """Shuts down the exporter.
+
+        Called when the SDK is shut down.
+        """
+
+
+class ConsoleMetricExporter(MetricExporter):
+    """Implementation of :class:`MetricExporter` that prints metrics to the
+    console.
+
+    This class can be used for diagnostic purposes. It prints the exported
+    metrics to the console STDOUT.
+    """
+
+    def __init__(
+        self,
+        out: IO = stdout,
+        formatter: Callable[
+            ["opentelemetry.sdk.metrics.export.MetricsData"], str
+        ] = lambda metrics_data: metrics_data.to_json() + linesep,
+        preferred_temporality: dict[type, AggregationTemporality]
+        | None = None,
+        preferred_aggregation: dict[
+            type, "opentelemetry.sdk.metrics.view.Aggregation"
+        ]
+        | None = None,
+    ):
+        super().__init__(
+            preferred_temporality=preferred_temporality,
+            preferred_aggregation=preferred_aggregation,
+        )
+        self.out = out
+        self.formatter = formatter
+
+    def export(
+        self,
+        metrics_data: MetricsData,
+        timeout_millis: float = 10_000,
+        **kwargs,
+    ) -> MetricExportResult:
+        self.out.write(self.formatter(metrics_data))
+        self.out.flush()
+        return MetricExportResult.SUCCESS
+
+    def shutdown(self, timeout_millis: float = 30_000, **kwargs) -> None:
+        pass
+
+    def force_flush(self, timeout_millis: float = 10_000) -> bool:
+        return True
+
+
+class MetricReader(ABC):
+    # pylint: disable=too-many-branches,broad-exception-raised
+    """
+    Base class for all metric readers
+
+    Args:
+        preferred_temporality: A mapping between instrument classes and
+            aggregation temporality. By default uses CUMULATIVE for all instrument
+            classes. This mapping will be used to define the default aggregation
+            temporality of every instrument class. If the user wants to make a
+            change in the default aggregation temporality of an instrument class,
+            it is enough to pass here a dictionary whose keys are the instrument
+            classes and the values are the corresponding desired aggregation
+            temporalities of the classes that the user wants to change, not all of
+            them. The classes not included in the passed dictionary will retain
+            their association to their default aggregation temporalities.
+        preferred_aggregation: A mapping between instrument classes and
+            aggregation instances. By default maps all instrument classes to an
+            instance of `DefaultAggregation`. This mapping will be used to
+            define the default aggregation of every instrument class. If the
+            user wants to make a change in the default aggregation of an
+            instrument class, it is enough to pass here a dictionary whose keys
+            are the instrument classes and the values are the corresponding
+            desired aggregation for the instrument classes that the user wants
+            to change, not necessarily all of them. The classes not included in
+            the passed dictionary will retain their association to their
+            default aggregations. The aggregation defined here will be
+            overridden by an aggregation defined by a view that is not
+            `DefaultAggregation`.
+
+    .. document protected _receive_metrics which is a intended to be overridden by subclass
+    .. automethod:: _receive_metrics
+    """
+
+    def __init__(
+        self,
+        preferred_temporality: dict[type, AggregationTemporality]
+        | None = None,
+        preferred_aggregation: dict[
+            type, "opentelemetry.sdk.metrics.view.Aggregation"
+        ]
+        | None = None,
+    ) -> None:
+        self._collect: Callable[
+            [
+                "opentelemetry.sdk.metrics.export.MetricReader",
+                AggregationTemporality,
+            ],
+            Iterable["opentelemetry.sdk.metrics.export.Metric"],
+        ] = None
+
+        self._instrument_class_temporality = {
+            _Counter: AggregationTemporality.CUMULATIVE,
+            _UpDownCounter: AggregationTemporality.CUMULATIVE,
+            _Histogram: AggregationTemporality.CUMULATIVE,
+            _Gauge: AggregationTemporality.CUMULATIVE,
+            _ObservableCounter: AggregationTemporality.CUMULATIVE,
+            _ObservableUpDownCounter: AggregationTemporality.CUMULATIVE,
+            _ObservableGauge: AggregationTemporality.CUMULATIVE,
+        }
+
+        if preferred_temporality is not None:
+            for temporality in preferred_temporality.values():
+                if temporality not in (
+                    AggregationTemporality.CUMULATIVE,
+                    AggregationTemporality.DELTA,
+                ):
+                    raise Exception(
+                        f"Invalid temporality value found {temporality}"
+                    )
+
+        if preferred_temporality is not None:
+            for typ, temporality in preferred_temporality.items():
+                if typ is Counter:
+                    self._instrument_class_temporality[_Counter] = temporality
+                elif typ is UpDownCounter:
+                    self._instrument_class_temporality[_UpDownCounter] = (
+                        temporality
+                    )
+                elif typ is Histogram:
+                    self._instrument_class_temporality[_Histogram] = (
+                        temporality
+                    )
+                elif typ is Gauge:
+                    self._instrument_class_temporality[_Gauge] = temporality
+                elif typ is ObservableCounter:
+                    self._instrument_class_temporality[_ObservableCounter] = (
+                        temporality
+                    )
+                elif typ is ObservableUpDownCounter:
+                    self._instrument_class_temporality[
+                        _ObservableUpDownCounter
+                    ] = temporality
+                elif typ is ObservableGauge:
+                    self._instrument_class_temporality[_ObservableGauge] = (
+                        temporality
+                    )
+                else:
+                    raise Exception(f"Invalid instrument class found {typ}")
+
+        self._preferred_temporality = preferred_temporality
+        self._instrument_class_aggregation = {
+            _Counter: DefaultAggregation(),
+            _UpDownCounter: DefaultAggregation(),
+            _Histogram: DefaultAggregation(),
+            _Gauge: DefaultAggregation(),
+            _ObservableCounter: DefaultAggregation(),
+            _ObservableUpDownCounter: DefaultAggregation(),
+            _ObservableGauge: DefaultAggregation(),
+        }
+
+        if preferred_aggregation is not None:
+            for typ, aggregation in preferred_aggregation.items():
+                if typ is Counter:
+                    self._instrument_class_aggregation[_Counter] = aggregation
+                elif typ is UpDownCounter:
+                    self._instrument_class_aggregation[_UpDownCounter] = (
+                        aggregation
+                    )
+                elif typ is Histogram:
+                    self._instrument_class_aggregation[_Histogram] = (
+                        aggregation
+                    )
+                elif typ is Gauge:
+                    self._instrument_class_aggregation[_Gauge] = aggregation
+                elif typ is ObservableCounter:
+                    self._instrument_class_aggregation[_ObservableCounter] = (
+                        aggregation
+                    )
+                elif typ is ObservableUpDownCounter:
+                    self._instrument_class_aggregation[
+                        _ObservableUpDownCounter
+                    ] = aggregation
+                elif typ is ObservableGauge:
+                    self._instrument_class_aggregation[_ObservableGauge] = (
+                        aggregation
+                    )
+                else:
+                    raise Exception(f"Invalid instrument class found {typ}")
+
+    @final
+    def collect(self, timeout_millis: float = 10_000) -> None:
+        """Collects the metrics from the internal SDK state and
+        invokes the `_receive_metrics` with the collection.
+
+        Args:
+            timeout_millis: Amount of time in milliseconds before this function
+              raises a timeout error.
+
+        If any of the underlying ``collect`` methods called by this method
+        fails by any reason (including timeout) an exception will be raised
+        detailing the individual errors that caused this function to fail.
+        """
+        if self._collect is None:
+            _logger.warning(
+                "Cannot call collect on a MetricReader until it is registered on a MeterProvider"
+            )
+            return
+
+        metrics = self._collect(self, timeout_millis=timeout_millis)
+
+        if metrics is not None:
+            self._receive_metrics(
+                metrics,
+                timeout_millis=timeout_millis,
+            )
+
+    @final
+    def _set_collect_callback(
+        self,
+        func: Callable[
+            [
+                "opentelemetry.sdk.metrics.export.MetricReader",
+                AggregationTemporality,
+            ],
+            Iterable["opentelemetry.sdk.metrics.export.Metric"],
+        ],
+    ) -> None:
+        """This function is internal to the SDK. It should not be called or overridden by users"""
+        self._collect = func
+
+    @abstractmethod
+    def _receive_metrics(
+        self,
+        metrics_data: "opentelemetry.sdk.metrics.export.MetricsData",
+        timeout_millis: float = 10_000,
+        **kwargs,
+    ) -> None:
+        """Called by `MetricReader.collect` when it receives a batch of metrics"""
+
+    def force_flush(self, timeout_millis: float = 10_000) -> bool:
+        self.collect(timeout_millis=timeout_millis)
+        return True
+
+    @abstractmethod
+    def shutdown(self, timeout_millis: float = 30_000, **kwargs) -> None:
+        """Shuts down the MetricReader. This method provides a way
+        for the MetricReader to do any cleanup required. A metric reader can
+        only be shutdown once, any subsequent calls are ignored and return
+        failure status.
+
+        When a `MetricReader` is registered on a
+        :class:`~opentelemetry.sdk.metrics.MeterProvider`,
+        :meth:`~opentelemetry.sdk.metrics.MeterProvider.shutdown` will invoke this
+        automatically.
+        """
+
+
+class InMemoryMetricReader(MetricReader):
+    """Implementation of `MetricReader` that returns its metrics from :func:`get_metrics_data`.
+
+    This is useful for e.g. unit tests.
+    """
+
+    def __init__(
+        self,
+        preferred_temporality: dict[type, AggregationTemporality]
+        | None = None,
+        preferred_aggregation: dict[
+            type, "opentelemetry.sdk.metrics.view.Aggregation"
+        ]
+        | None = None,
+    ) -> None:
+        super().__init__(
+            preferred_temporality=preferred_temporality,
+            preferred_aggregation=preferred_aggregation,
+        )
+        self._lock = RLock()
+        self._metrics_data: "opentelemetry.sdk.metrics.export.MetricsData" = (
+            None
+        )
+
+    def get_metrics_data(
+        self,
+    ) -> Optional["opentelemetry.sdk.metrics.export.MetricsData"]:
+        """Reads and returns current metrics from the SDK"""
+        with self._lock:
+            self.collect()
+            metrics_data = self._metrics_data
+            self._metrics_data = None
+        return metrics_data
+
+    def _receive_metrics(
+        self,
+        metrics_data: "opentelemetry.sdk.metrics.export.MetricsData",
+        timeout_millis: float = 10_000,
+        **kwargs,
+    ) -> None:
+        with self._lock:
+            self._metrics_data = metrics_data
+
+    def shutdown(self, timeout_millis: float = 30_000, **kwargs) -> None:
+        pass
+
+
+class PeriodicExportingMetricReader(MetricReader):
+    """`PeriodicExportingMetricReader` is an implementation of `MetricReader`
+    that collects metrics based on a user-configurable time interval, and passes the
+    metrics to the configured exporter. If the time interval is set to `math.inf`, the
+    reader will not invoke periodic collection.
+
+    The configured exporter's :py:meth:`~MetricExporter.export` method will not be called
+    concurrently.
+    """
+
+    def __init__(
+        self,
+        exporter: MetricExporter,
+        export_interval_millis: Optional[float] = None,
+        export_timeout_millis: Optional[float] = None,
+    ) -> None:
+        # PeriodicExportingMetricReader defers to exporter for configuration
+        super().__init__(
+            preferred_temporality=exporter._preferred_temporality,
+            preferred_aggregation=exporter._preferred_aggregation,
+        )
+
+        # This lock is held whenever calling self._exporter.export() to prevent concurrent
+        # execution of MetricExporter.export()
+        # https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/metrics/sdk.md#exportbatch
+        self._export_lock = Lock()
+
+        self._exporter = exporter
+        if export_interval_millis is None:
+            try:
+                export_interval_millis = float(
+                    environ.get(OTEL_METRIC_EXPORT_INTERVAL, 60000)
+                )
+            except ValueError:
+                _logger.warning(
+                    "Found invalid value for export interval, using default"
+                )
+                export_interval_millis = 60000
+        if export_timeout_millis is None:
+            try:
+                export_timeout_millis = float(
+                    environ.get(OTEL_METRIC_EXPORT_TIMEOUT, 30000)
+                )
+            except ValueError:
+                _logger.warning(
+                    "Found invalid value for export timeout, using default"
+                )
+                export_timeout_millis = 30000
+        self._export_interval_millis = export_interval_millis
+        self._export_timeout_millis = export_timeout_millis
+        self._shutdown = False
+        self._shutdown_event = Event()
+        self._shutdown_once = Once()
+        self._daemon_thread = None
+        if (
+            self._export_interval_millis > 0
+            and self._export_interval_millis < math.inf
+        ):
+            self._daemon_thread = Thread(
+                name="OtelPeriodicExportingMetricReader",
+                target=self._ticker,
+                daemon=True,
+            )
+            self._daemon_thread.start()
+            if hasattr(os, "register_at_fork"):
+                weak_at_fork = weakref.WeakMethod(self._at_fork_reinit)
+
+                os.register_at_fork(
+                    after_in_child=lambda: weak_at_fork()()  # pylint: disable=unnecessary-lambda, protected-access
+                )
+        elif self._export_interval_millis <= 0:
+            raise ValueError(
+                f"interval value {self._export_interval_millis} is invalid \
+                and needs to be larger than zero."
+            )
+
+    def _at_fork_reinit(self):
+        self._daemon_thread = Thread(
+            name="OtelPeriodicExportingMetricReader",
+            target=self._ticker,
+            daemon=True,
+        )
+        self._daemon_thread.start()
+
+    def _ticker(self) -> None:
+        interval_secs = self._export_interval_millis / 1e3
+        while not self._shutdown_event.wait(interval_secs):
+            try:
+                self.collect(timeout_millis=self._export_timeout_millis)
+            except MetricsTimeoutError:
+                _logger.warning(
+                    "Metric collection timed out. Will try again after %s seconds",
+                    interval_secs,
+                    exc_info=True,
+                )
+        # one last collection below before shutting down completely
+        try:
+            self.collect(timeout_millis=self._export_interval_millis)
+        except MetricsTimeoutError:
+            _logger.warning(
+                "Metric collection timed out.",
+                exc_info=True,
+            )
+
+    def _receive_metrics(
+        self,
+        metrics_data: MetricsData,
+        timeout_millis: float = 10_000,
+        **kwargs,
+    ) -> None:
+        token = attach(set_value(_SUPPRESS_INSTRUMENTATION_KEY, True))
+        # pylint: disable=broad-exception-caught,invalid-name
+        try:
+            with self._export_lock:
+                self._exporter.export(
+                    metrics_data, timeout_millis=timeout_millis
+                )
+        except Exception:
+            _logger.exception("Exception while exporting metrics")
+        detach(token)
+
+    def shutdown(self, timeout_millis: float = 30_000, **kwargs) -> None:
+        deadline_ns = time_ns() + timeout_millis * 10**6
+
+        def _shutdown():
+            self._shutdown = True
+
+        did_set = self._shutdown_once.do_once(_shutdown)
+        if not did_set:
+            _logger.warning("Can't shutdown multiple times")
+            return
+
+        self._shutdown_event.set()
+        if self._daemon_thread:
+            self._daemon_thread.join(timeout=(deadline_ns - time_ns()) / 10**9)
+        self._exporter.shutdown(timeout=(deadline_ns - time_ns()) / 10**6)
+
+    def force_flush(self, timeout_millis: float = 10_000) -> bool:
+        super().force_flush(timeout_millis=timeout_millis)
+        self._exporter.force_flush(timeout_millis=timeout_millis)
+        return True
diff --git a/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/instrument.py b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/instrument.py
new file mode 100644
index 00000000..b01578f4
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/instrument.py
@@ -0,0 +1,334 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# pylint: disable=too-many-ancestors, unused-import
+from __future__ import annotations
+
+from logging import getLogger
+from time import time_ns
+from typing import Generator, Iterable, List, Sequence, Union
+
+# This kind of import is needed to avoid Sphinx errors.
+import opentelemetry.sdk.metrics
+from opentelemetry.context import Context, get_current
+from opentelemetry.metrics import CallbackT
+from opentelemetry.metrics import Counter as APICounter
+from opentelemetry.metrics import Histogram as APIHistogram
+from opentelemetry.metrics import ObservableCounter as APIObservableCounter
+from opentelemetry.metrics import ObservableGauge as APIObservableGauge
+from opentelemetry.metrics import (
+    ObservableUpDownCounter as APIObservableUpDownCounter,
+)
+from opentelemetry.metrics import UpDownCounter as APIUpDownCounter
+from opentelemetry.metrics import _Gauge as APIGauge
+from opentelemetry.metrics._internal.instrument import (
+    CallbackOptions,
+    _MetricsHistogramAdvisory,
+)
+from opentelemetry.sdk.metrics._internal.measurement import Measurement
+from opentelemetry.sdk.util.instrumentation import InstrumentationScope
+
+_logger = getLogger(__name__)
+
+
+_ERROR_MESSAGE = (
+    "Expected ASCII string of maximum length 63 characters but got {}"
+)
+
+
+class _Synchronous:
+    def __init__(
+        self,
+        name: str,
+        instrumentation_scope: InstrumentationScope,
+        measurement_consumer: "opentelemetry.sdk.metrics.MeasurementConsumer",
+        unit: str = "",
+        description: str = "",
+    ):
+        # pylint: disable=no-member
+        result = self._check_name_unit_description(name, unit, description)
+
+        if result["name"] is None:
+            # pylint: disable=broad-exception-raised
+            raise Exception(_ERROR_MESSAGE.format(name))
+
+        if result["unit"] is None:
+            # pylint: disable=broad-exception-raised
+            raise Exception(_ERROR_MESSAGE.format(unit))
+
+        name = result["name"]
+        unit = result["unit"]
+        description = result["description"]
+
+        self.name = name.lower()
+        self.unit = unit
+        self.description = description
+        self.instrumentation_scope = instrumentation_scope
+        self._measurement_consumer = measurement_consumer
+        super().__init__(name, unit=unit, description=description)
+
+
+class _Asynchronous:
+    def __init__(
+        self,
+        name: str,
+        instrumentation_scope: InstrumentationScope,
+        measurement_consumer: "opentelemetry.sdk.metrics.MeasurementConsumer",
+        callbacks: Iterable[CallbackT] | None = None,
+        unit: str = "",
+        description: str = "",
+    ):
+        # pylint: disable=no-member
+        result = self._check_name_unit_description(name, unit, description)
+
+        if result["name"] is None:
+            # pylint: disable=broad-exception-raised
+            raise Exception(_ERROR_MESSAGE.format(name))
+
+        if result["unit"] is None:
+            # pylint: disable=broad-exception-raised
+            raise Exception(_ERROR_MESSAGE.format(unit))
+
+        name = result["name"]
+        unit = result["unit"]
+        description = result["description"]
+
+        self.name = name.lower()
+        self.unit = unit
+        self.description = description
+        self.instrumentation_scope = instrumentation_scope
+        self._measurement_consumer = measurement_consumer
+        super().__init__(name, callbacks, unit=unit, description=description)
+
+        self._callbacks: List[CallbackT] = []
+
+        if callbacks is not None:
+            for callback in callbacks:
+                if isinstance(callback, Generator):
+                    # advance generator to it's first yield
+                    next(callback)
+
+                    def inner(
+                        options: CallbackOptions,
+                        callback=callback,
+                    ) -> Iterable[Measurement]:
+                        try:
+                            return callback.send(options)
+                        except StopIteration:
+                            return []
+
+                    self._callbacks.append(inner)
+                else:
+                    self._callbacks.append(callback)
+
+    def callback(
+        self, callback_options: CallbackOptions
+    ) -> Iterable[Measurement]:
+        for callback in self._callbacks:
+            try:
+                for api_measurement in callback(callback_options):
+                    yield Measurement(
+                        api_measurement.value,
+                        time_unix_nano=time_ns(),
+                        instrument=self,
+                        context=api_measurement.context or get_current(),
+                        attributes=api_measurement.attributes,
+                    )
+            except Exception:  # pylint: disable=broad-exception-caught
+                _logger.exception(
+                    "Callback failed for instrument %s.", self.name
+                )
+
+
+class Counter(_Synchronous, APICounter):
+    def __new__(cls, *args, **kwargs):
+        if cls is Counter:
+            raise TypeError("Counter must be instantiated via a meter.")
+        return super().__new__(cls)
+
+    def add(
+        self,
+        amount: Union[int, float],
+        attributes: dict[str, str] | None = None,
+        context: Context | None = None,
+    ):
+        if amount < 0:
+            _logger.warning(
+                "Add amount must be non-negative on Counter %s.", self.name
+            )
+            return
+        time_unix_nano = time_ns()
+        self._measurement_consumer.consume_measurement(
+            Measurement(
+                amount,
+                time_unix_nano,
+                self,
+                context or get_current(),
+                attributes,
+            )
+        )
+
+
+class UpDownCounter(_Synchronous, APIUpDownCounter):
+    def __new__(cls, *args, **kwargs):
+        if cls is UpDownCounter:
+            raise TypeError("UpDownCounter must be instantiated via a meter.")
+        return super().__new__(cls)
+
+    def add(
+        self,
+        amount: Union[int, float],
+        attributes: dict[str, str] | None = None,
+        context: Context | None = None,
+    ):
+        time_unix_nano = time_ns()
+        self._measurement_consumer.consume_measurement(
+            Measurement(
+                amount,
+                time_unix_nano,
+                self,
+                context or get_current(),
+                attributes,
+            )
+        )
+
+
+class ObservableCounter(_Asynchronous, APIObservableCounter):
+    def __new__(cls, *args, **kwargs):
+        if cls is ObservableCounter:
+            raise TypeError(
+                "ObservableCounter must be instantiated via a meter."
+            )
+        return super().__new__(cls)
+
+
+class ObservableUpDownCounter(_Asynchronous, APIObservableUpDownCounter):
+    def __new__(cls, *args, **kwargs):
+        if cls is ObservableUpDownCounter:
+            raise TypeError(
+                "ObservableUpDownCounter must be instantiated via a meter."
+            )
+        return super().__new__(cls)
+
+
+class Histogram(_Synchronous, APIHistogram):
+    def __init__(
+        self,
+        name: str,
+        instrumentation_scope: InstrumentationScope,
+        measurement_consumer: "opentelemetry.sdk.metrics.MeasurementConsumer",
+        unit: str = "",
+        description: str = "",
+        explicit_bucket_boundaries_advisory: Sequence[float] | None = None,
+    ):
+        super().__init__(
+            name,
+            unit=unit,
+            description=description,
+            instrumentation_scope=instrumentation_scope,
+            measurement_consumer=measurement_consumer,
+        )
+        self._advisory = _MetricsHistogramAdvisory(
+            explicit_bucket_boundaries=explicit_bucket_boundaries_advisory
+        )
+
+    def __new__(cls, *args, **kwargs):
+        if cls is Histogram:
+            raise TypeError("Histogram must be instantiated via a meter.")
+        return super().__new__(cls)
+
+    def record(
+        self,
+        amount: Union[int, float],
+        attributes: dict[str, str] | None = None,
+        context: Context | None = None,
+    ):
+        if amount < 0:
+            _logger.warning(
+                "Record amount must be non-negative on Histogram %s.",
+                self.name,
+            )
+            return
+        time_unix_nano = time_ns()
+        self._measurement_consumer.consume_measurement(
+            Measurement(
+                amount,
+                time_unix_nano,
+                self,
+                context or get_current(),
+                attributes,
+            )
+        )
+
+
+class Gauge(_Synchronous, APIGauge):
+    def __new__(cls, *args, **kwargs):
+        if cls is Gauge:
+            raise TypeError("Gauge must be instantiated via a meter.")
+        return super().__new__(cls)
+
+    def set(
+        self,
+        amount: Union[int, float],
+        attributes: dict[str, str] | None = None,
+        context: Context | None = None,
+    ):
+        time_unix_nano = time_ns()
+        self._measurement_consumer.consume_measurement(
+            Measurement(
+                amount,
+                time_unix_nano,
+                self,
+                context or get_current(),
+                attributes,
+            )
+        )
+
+
+class ObservableGauge(_Asynchronous, APIObservableGauge):
+    def __new__(cls, *args, **kwargs):
+        if cls is ObservableGauge:
+            raise TypeError(
+                "ObservableGauge must be instantiated via a meter."
+            )
+        return super().__new__(cls)
+
+
+# Below classes exist to prevent the direct instantiation
+class _Counter(Counter):
+    pass
+
+
+class _UpDownCounter(UpDownCounter):
+    pass
+
+
+class _ObservableCounter(ObservableCounter):
+    pass
+
+
+class _ObservableUpDownCounter(ObservableUpDownCounter):
+    pass
+
+
+class _Histogram(Histogram):
+    pass
+
+
+class _Gauge(Gauge):
+    pass
+
+
+class _ObservableGauge(ObservableGauge):
+    pass
diff --git a/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/measurement.py b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/measurement.py
new file mode 100644
index 00000000..56619a83
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/measurement.py
@@ -0,0 +1,45 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass
+from typing import Union
+
+from opentelemetry.context import Context
+from opentelemetry.metrics import Instrument
+from opentelemetry.util.types import Attributes
+
+
+@dataclass(frozen=True)
+class Measurement:
+    """
+    Represents a data point reported via the metrics API to the SDK.
+
+    Attributes
+        value: Measured value
+        time_unix_nano: The time the API call was made to record the Measurement
+        instrument: The instrument that produced this `Measurement`.
+        context: The active Context of the Measurement at API call time.
+        attributes: Measurement attributes
+    """
+
+    # TODO Fix doc - if using valid Google `Attributes:` key, the attributes are duplicated
+    # one will come from napoleon extension and the other from autodoc extension. This
+    # will raise an sphinx error of duplicated object description
+    # See https://github.com/sphinx-doc/sphinx/issues/8664
+
+    value: Union[int, float]
+    time_unix_nano: int
+    instrument: Instrument
+    context: Context
+    attributes: Attributes = None
diff --git a/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/measurement_consumer.py b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/measurement_consumer.py
new file mode 100644
index 00000000..c6510330
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/measurement_consumer.py
@@ -0,0 +1,145 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# pylint: disable=unused-import
+
+from abc import ABC, abstractmethod
+from threading import Lock
+from time import time_ns
+from typing import Iterable, List, Mapping, Optional
+
+# This kind of import is needed to avoid Sphinx errors.
+import opentelemetry.sdk.metrics
+import opentelemetry.sdk.metrics._internal.instrument
+import opentelemetry.sdk.metrics._internal.sdk_configuration
+from opentelemetry.metrics._internal.instrument import CallbackOptions
+from opentelemetry.sdk.metrics._internal.exceptions import MetricsTimeoutError
+from opentelemetry.sdk.metrics._internal.measurement import Measurement
+from opentelemetry.sdk.metrics._internal.metric_reader_storage import (
+    MetricReaderStorage,
+)
+from opentelemetry.sdk.metrics._internal.point import Metric
+
+
+class MeasurementConsumer(ABC):
+    @abstractmethod
+    def consume_measurement(self, measurement: Measurement) -> None:
+        pass
+
+    @abstractmethod
+    def register_asynchronous_instrument(
+        self,
+        instrument: (
+            "opentelemetry.sdk.metrics._internal.instrument_Asynchronous"
+        ),
+    ):
+        pass
+
+    @abstractmethod
+    def collect(
+        self,
+        metric_reader: "opentelemetry.sdk.metrics.MetricReader",
+        timeout_millis: float = 10_000,
+    ) -> Optional[Iterable[Metric]]:
+        pass
+
+
+class SynchronousMeasurementConsumer(MeasurementConsumer):
+    def __init__(
+        self,
+        sdk_config: "opentelemetry.sdk.metrics._internal.SdkConfiguration",
+    ) -> None:
+        self._lock = Lock()
+        self._sdk_config = sdk_config
+        # should never be mutated
+        self._reader_storages: Mapping[
+            "opentelemetry.sdk.metrics.MetricReader", MetricReaderStorage
+        ] = {
+            reader: MetricReaderStorage(
+                sdk_config,
+                reader._instrument_class_temporality,
+                reader._instrument_class_aggregation,
+            )
+            for reader in sdk_config.metric_readers
+        }
+        self._async_instruments: List[
+            "opentelemetry.sdk.metrics._internal.instrument._Asynchronous"
+        ] = []
+
+    def consume_measurement(self, measurement: Measurement) -> None:
+        should_sample_exemplar = (
+            self._sdk_config.exemplar_filter.should_sample(
+                measurement.value,
+                measurement.time_unix_nano,
+                measurement.attributes,
+                measurement.context,
+            )
+        )
+        for reader_storage in self._reader_storages.values():
+            reader_storage.consume_measurement(
+                measurement, should_sample_exemplar
+            )
+
+    def register_asynchronous_instrument(
+        self,
+        instrument: (
+            "opentelemetry.sdk.metrics._internal.instrument._Asynchronous"
+        ),
+    ) -> None:
+        with self._lock:
+            self._async_instruments.append(instrument)
+
+    def collect(
+        self,
+        metric_reader: "opentelemetry.sdk.metrics.MetricReader",
+        timeout_millis: float = 10_000,
+    ) -> Optional[Iterable[Metric]]:
+        with self._lock:
+            metric_reader_storage = self._reader_storages[metric_reader]
+            # for now, just use the defaults
+            callback_options = CallbackOptions()
+            deadline_ns = time_ns() + (timeout_millis * 1e6)
+
+            default_timeout_ns = 10000 * 1e6
+
+            for async_instrument in self._async_instruments:
+                remaining_time = deadline_ns - time_ns()
+
+                if remaining_time < default_timeout_ns:
+                    callback_options = CallbackOptions(
+                        timeout_millis=remaining_time / 1e6
+                    )
+
+                measurements = async_instrument.callback(callback_options)
+                if time_ns() >= deadline_ns:
+                    raise MetricsTimeoutError(
+                        "Timed out while executing callback"
+                    )
+
+                for measurement in measurements:
+                    should_sample_exemplar = (
+                        self._sdk_config.exemplar_filter.should_sample(
+                            measurement.value,
+                            measurement.time_unix_nano,
+                            measurement.attributes,
+                            measurement.context,
+                        )
+                    )
+                    metric_reader_storage.consume_measurement(
+                        measurement, should_sample_exemplar
+                    )
+
+            result = self._reader_storages[metric_reader].collect()
+
+        return result
diff --git a/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/metric_reader_storage.py b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/metric_reader_storage.py
new file mode 100644
index 00000000..f5121811
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/metric_reader_storage.py
@@ -0,0 +1,315 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from logging import getLogger
+from threading import RLock
+from time import time_ns
+from typing import Dict, List, Optional
+
+from opentelemetry.metrics import (
+    Asynchronous,
+    Counter,
+    Instrument,
+    ObservableCounter,
+)
+from opentelemetry.sdk.metrics._internal._view_instrument_match import (
+    _ViewInstrumentMatch,
+)
+from opentelemetry.sdk.metrics._internal.aggregation import (
+    Aggregation,
+    ExplicitBucketHistogramAggregation,
+    _DropAggregation,
+    _ExplicitBucketHistogramAggregation,
+    _ExponentialBucketHistogramAggregation,
+    _LastValueAggregation,
+    _SumAggregation,
+)
+from opentelemetry.sdk.metrics._internal.export import AggregationTemporality
+from opentelemetry.sdk.metrics._internal.measurement import Measurement
+from opentelemetry.sdk.metrics._internal.point import (
+    ExponentialHistogram,
+    Gauge,
+    Histogram,
+    Metric,
+    MetricsData,
+    ResourceMetrics,
+    ScopeMetrics,
+    Sum,
+)
+from opentelemetry.sdk.metrics._internal.sdk_configuration import (
+    SdkConfiguration,
+)
+from opentelemetry.sdk.metrics._internal.view import View
+from opentelemetry.sdk.util.instrumentation import InstrumentationScope
+
+_logger = getLogger(__name__)
+
+_DEFAULT_VIEW = View(instrument_name="")
+
+
+class MetricReaderStorage:
+    """The SDK's storage for a given reader"""
+
+    def __init__(
+        self,
+        sdk_config: SdkConfiguration,
+        instrument_class_temporality: Dict[type, AggregationTemporality],
+        instrument_class_aggregation: Dict[type, Aggregation],
+    ) -> None:
+        self._lock = RLock()
+        self._sdk_config = sdk_config
+        self._instrument_view_instrument_matches: Dict[
+            Instrument, List[_ViewInstrumentMatch]
+        ] = {}
+        self._instrument_class_temporality = instrument_class_temporality
+        self._instrument_class_aggregation = instrument_class_aggregation
+
+    def _get_or_init_view_instrument_match(
+        self, instrument: Instrument
+    ) -> List[_ViewInstrumentMatch]:
+        # Optimistically get the relevant views for the given instrument. Once set for a given
+        # instrument, the mapping will never change
+
+        if instrument in self._instrument_view_instrument_matches:
+            return self._instrument_view_instrument_matches[instrument]
+
+        with self._lock:
+            # double check if it was set before we held the lock
+            if instrument in self._instrument_view_instrument_matches:
+                return self._instrument_view_instrument_matches[instrument]
+
+            # not present, hold the lock and add a new mapping
+            view_instrument_matches = []
+
+            self._handle_view_instrument_match(
+                instrument, view_instrument_matches
+            )
+
+            # if no view targeted the instrument, use the default
+            if not view_instrument_matches:
+                view_instrument_matches.append(
+                    _ViewInstrumentMatch(
+                        view=_DEFAULT_VIEW,
+                        instrument=instrument,
+                        instrument_class_aggregation=(
+                            self._instrument_class_aggregation
+                        ),
+                    )
+                )
+            self._instrument_view_instrument_matches[instrument] = (
+                view_instrument_matches
+            )
+
+            return view_instrument_matches
+
+    def consume_measurement(
+        self, measurement: Measurement, should_sample_exemplar: bool = True
+    ) -> None:
+        for view_instrument_match in self._get_or_init_view_instrument_match(
+            measurement.instrument
+        ):
+            view_instrument_match.consume_measurement(
+                measurement, should_sample_exemplar
+            )
+
+    def collect(self) -> Optional[MetricsData]:
+        # Use a list instead of yielding to prevent a slow reader from holding
+        # SDK locks
+
+        # While holding the lock, new _ViewInstrumentMatch can't be added from
+        # another thread (so we are sure we collect all existing view).
+        # However, instruments can still send measurements that will make it
+        # into the individual aggregations; collection will acquire those locks
+        # iteratively to keep locking as fine-grained as possible. One side
+        # effect is that end times can be slightly skewed among the metric
+        # streams produced by the SDK, but we still align the output timestamps
+        # for a single instrument.
+
+        collection_start_nanos = time_ns()
+
+        with self._lock:
+            instrumentation_scope_scope_metrics: Dict[
+                InstrumentationScope, ScopeMetrics
+            ] = {}
+
+            for (
+                instrument,
+                view_instrument_matches,
+            ) in self._instrument_view_instrument_matches.items():
+                aggregation_temporality = self._instrument_class_temporality[
+                    instrument.__class__
+                ]
+
+                metrics: List[Metric] = []
+
+                for view_instrument_match in view_instrument_matches:
+                    data_points = view_instrument_match.collect(
+                        aggregation_temporality, collection_start_nanos
+                    )
+
+                    if data_points is None:
+                        continue
+
+                    if isinstance(
+                        # pylint: disable=protected-access
+                        view_instrument_match._aggregation,
+                        _SumAggregation,
+                    ):
+                        data = Sum(
+                            aggregation_temporality=aggregation_temporality,
+                            data_points=data_points,
+                            is_monotonic=isinstance(
+                                instrument, (Counter, ObservableCounter)
+                            ),
+                        )
+                    elif isinstance(
+                        # pylint: disable=protected-access
+                        view_instrument_match._aggregation,
+                        _LastValueAggregation,
+                    ):
+                        data = Gauge(data_points=data_points)
+                    elif isinstance(
+                        # pylint: disable=protected-access
+                        view_instrument_match._aggregation,
+                        _ExplicitBucketHistogramAggregation,
+                    ):
+                        data = Histogram(
+                            data_points=data_points,
+                            aggregation_temporality=aggregation_temporality,
+                        )
+                    elif isinstance(
+                        # pylint: disable=protected-access
+                        view_instrument_match._aggregation,
+                        _DropAggregation,
+                    ):
+                        continue
+
+                    elif isinstance(
+                        # pylint: disable=protected-access
+                        view_instrument_match._aggregation,
+                        _ExponentialBucketHistogramAggregation,
+                    ):
+                        data = ExponentialHistogram(
+                            data_points=data_points,
+                            aggregation_temporality=aggregation_temporality,
+                        )
+
+                    metrics.append(
+                        Metric(
+                            # pylint: disable=protected-access
+                            # pylint: disable=possibly-used-before-assignment
+                            name=view_instrument_match._name,
+                            description=view_instrument_match._description,
+                            unit=view_instrument_match._instrument.unit,
+                            data=data,
+                        )
+                    )
+
+                if metrics:
+                    if instrument.instrumentation_scope not in (
+                        instrumentation_scope_scope_metrics
+                    ):
+                        instrumentation_scope_scope_metrics[
+                            instrument.instrumentation_scope
+                        ] = ScopeMetrics(
+                            scope=instrument.instrumentation_scope,
+                            metrics=metrics,
+                            schema_url=instrument.instrumentation_scope.schema_url,
+                        )
+                    else:
+                        instrumentation_scope_scope_metrics[
+                            instrument.instrumentation_scope
+                        ].metrics.extend(metrics)
+
+            if instrumentation_scope_scope_metrics:
+                return MetricsData(
+                    resource_metrics=[
+                        ResourceMetrics(
+                            resource=self._sdk_config.resource,
+                            scope_metrics=list(
+                                instrumentation_scope_scope_metrics.values()
+                            ),
+                            schema_url=self._sdk_config.resource.schema_url,
+                        )
+                    ]
+                )
+
+            return None
+
+    def _handle_view_instrument_match(
+        self,
+        instrument: Instrument,
+        view_instrument_matches: List["_ViewInstrumentMatch"],
+    ) -> None:
+        for view in self._sdk_config.views:
+            # pylint: disable=protected-access
+            if not view._match(instrument):
+                continue
+
+            if not self._check_view_instrument_compatibility(view, instrument):
+                continue
+
+            new_view_instrument_match = _ViewInstrumentMatch(
+                view=view,
+                instrument=instrument,
+                instrument_class_aggregation=(
+                    self._instrument_class_aggregation
+                ),
+            )
+
+            for (
+                existing_view_instrument_matches
+            ) in self._instrument_view_instrument_matches.values():
+                for (
+                    existing_view_instrument_match
+                ) in existing_view_instrument_matches:
+                    if existing_view_instrument_match.conflicts(
+                        new_view_instrument_match
+                    ):
+                        _logger.warning(
+                            "Views %s and %s will cause conflicting "
+                            "metrics identities",
+                            existing_view_instrument_match._view,
+                            new_view_instrument_match._view,
+                        )
+
+            view_instrument_matches.append(new_view_instrument_match)
+
+    @staticmethod
+    def _check_view_instrument_compatibility(
+        view: View, instrument: Instrument
+    ) -> bool:
+        """
+        Checks if a view and an instrument are compatible.
+
+        Returns `true` if they are compatible and a `_ViewInstrumentMatch`
+        object should be created, `false` otherwise.
+        """
+
+        result = True
+
+        # pylint: disable=protected-access
+        if isinstance(instrument, Asynchronous) and isinstance(
+            view._aggregation, ExplicitBucketHistogramAggregation
+        ):
+            _logger.warning(
+                "View %s and instrument %s will produce "
+                "semantic errors when matched, the view "
+                "has not been applied.",
+                view,
+                instrument,
+            )
+            result = False
+
+        return result
diff --git a/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/point.py b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/point.py
new file mode 100644
index 00000000..8c7e3469
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/point.py
@@ -0,0 +1,277 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# pylint: disable=unused-import
+
+from dataclasses import asdict, dataclass, field
+from json import dumps, loads
+from typing import Optional, Sequence, Union
+
+# This kind of import is needed to avoid Sphinx errors.
+import opentelemetry.sdk.metrics._internal
+from opentelemetry.sdk.metrics._internal.exemplar import Exemplar
+from opentelemetry.sdk.resources import Resource
+from opentelemetry.sdk.util.instrumentation import InstrumentationScope
+from opentelemetry.util.types import Attributes
+
+
+@dataclass(frozen=True)
+class NumberDataPoint:
+    """Single data point in a timeseries that describes the time-varying scalar
+    value of a metric.
+    """
+
+    attributes: Attributes
+    start_time_unix_nano: int
+    time_unix_nano: int
+    value: Union[int, float]
+    exemplars: Sequence[Exemplar] = field(default_factory=list)
+
+    def to_json(self, indent: Optional[int] = 4) -> str:
+        return dumps(asdict(self), indent=indent)
+
+
+@dataclass(frozen=True)
+class HistogramDataPoint:
+    """Single data point in a timeseries that describes the time-varying scalar
+    value of a metric.
+    """
+
+    attributes: Attributes
+    start_time_unix_nano: int
+    time_unix_nano: int
+    count: int
+    sum: Union[int, float]
+    bucket_counts: Sequence[int]
+    explicit_bounds: Sequence[float]
+    min: float
+    max: float
+    exemplars: Sequence[Exemplar] = field(default_factory=list)
+
+    def to_json(self, indent: Optional[int] = 4) -> str:
+        return dumps(asdict(self), indent=indent)
+
+
+@dataclass(frozen=True)
+class Buckets:
+    offset: int
+    bucket_counts: Sequence[int]
+
+
+@dataclass(frozen=True)
+class ExponentialHistogramDataPoint:
+    """Single data point in a timeseries whose boundaries are defined by an
+    exponential function. This timeseries describes the time-varying scalar
+    value of a metric.
+    """
+
+    attributes: Attributes
+    start_time_unix_nano: int
+    time_unix_nano: int
+    count: int
+    sum: Union[int, float]
+    scale: int
+    zero_count: int
+    positive: Buckets
+    negative: Buckets
+    flags: int
+    min: float
+    max: float
+    exemplars: Sequence[Exemplar] = field(default_factory=list)
+
+    def to_json(self, indent: Optional[int] = 4) -> str:
+        return dumps(asdict(self), indent=indent)
+
+
+@dataclass(frozen=True)
+class ExponentialHistogram:
+    """Represents the type of a metric that is calculated by aggregating as an
+    ExponentialHistogram of all reported measurements over a time interval.
+    """
+
+    data_points: Sequence[ExponentialHistogramDataPoint]
+    aggregation_temporality: (
+        "opentelemetry.sdk.metrics.export.AggregationTemporality"
+    )
+
+    def to_json(self, indent: Optional[int] = 4) -> str:
+        return dumps(
+            {
+                "data_points": [
+                    loads(data_point.to_json(indent=indent))
+                    for data_point in self.data_points
+                ],
+                "aggregation_temporality": self.aggregation_temporality,
+            },
+            indent=indent,
+        )
+
+
+@dataclass(frozen=True)
+class Sum:
+    """Represents the type of a scalar metric that is calculated as a sum of
+    all reported measurements over a time interval."""
+
+    data_points: Sequence[NumberDataPoint]
+    aggregation_temporality: (
+        "opentelemetry.sdk.metrics.export.AggregationTemporality"
+    )
+    is_monotonic: bool
+
+    def to_json(self, indent: Optional[int] = 4) -> str:
+        return dumps(
+            {
+                "data_points": [
+                    loads(data_point.to_json(indent=indent))
+                    for data_point in self.data_points
+                ],
+                "aggregation_temporality": self.aggregation_temporality,
+                "is_monotonic": self.is_monotonic,
+            },
+            indent=indent,
+        )
+
+
+@dataclass(frozen=True)
+class Gauge:
+    """Represents the type of a scalar metric that always exports the current
+    value for every data point. It should be used for an unknown
+    aggregation."""
+
+    data_points: Sequence[NumberDataPoint]
+
+    def to_json(self, indent: Optional[int] = 4) -> str:
+        return dumps(
+            {
+                "data_points": [
+                    loads(data_point.to_json(indent=indent))
+                    for data_point in self.data_points
+                ],
+            },
+            indent=indent,
+        )
+
+
+@dataclass(frozen=True)
+class Histogram:
+    """Represents the type of a metric that is calculated by aggregating as a
+    histogram of all reported measurements over a time interval."""
+
+    data_points: Sequence[HistogramDataPoint]
+    aggregation_temporality: (
+        "opentelemetry.sdk.metrics.export.AggregationTemporality"
+    )
+
+    def to_json(self, indent: Optional[int] = 4) -> str:
+        return dumps(
+            {
+                "data_points": [
+                    loads(data_point.to_json(indent=indent))
+                    for data_point in self.data_points
+                ],
+                "aggregation_temporality": self.aggregation_temporality,
+            },
+            indent=indent,
+        )
+
+
+# pylint: disable=invalid-name
+DataT = Union[Sum, Gauge, Histogram, ExponentialHistogram]
+DataPointT = Union[
+    NumberDataPoint, HistogramDataPoint, ExponentialHistogramDataPoint
+]
+
+
+@dataclass(frozen=True)
+class Metric:
+    """Represents a metric point in the OpenTelemetry data model to be
+    exported."""
+
+    name: str
+    description: Optional[str]
+    unit: Optional[str]
+    data: DataT
+
+    def to_json(self, indent: Optional[int] = 4) -> str:
+        return dumps(
+            {
+                "name": self.name,
+                "description": self.description or "",
+                "unit": self.unit or "",
+                "data": loads(self.data.to_json(indent=indent)),
+            },
+            indent=indent,
+        )
+
+
+@dataclass(frozen=True)
+class ScopeMetrics:
+    """A collection of Metrics produced by a scope"""
+
+    scope: InstrumentationScope
+    metrics: Sequence[Metric]
+    schema_url: str
+
+    def to_json(self, indent: Optional[int] = 4) -> str:
+        return dumps(
+            {
+                "scope": loads(self.scope.to_json(indent=indent)),
+                "metrics": [
+                    loads(metric.to_json(indent=indent))
+                    for metric in self.metrics
+                ],
+                "schema_url": self.schema_url,
+            },
+            indent=indent,
+        )
+
+
+@dataclass(frozen=True)
+class ResourceMetrics:
+    """A collection of ScopeMetrics from a Resource"""
+
+    resource: Resource
+    scope_metrics: Sequence[ScopeMetrics]
+    schema_url: str
+
+    def to_json(self, indent: Optional[int] = 4) -> str:
+        return dumps(
+            {
+                "resource": loads(self.resource.to_json(indent=indent)),
+                "scope_metrics": [
+                    loads(scope_metrics.to_json(indent=indent))
+                    for scope_metrics in self.scope_metrics
+                ],
+                "schema_url": self.schema_url,
+            },
+            indent=indent,
+        )
+
+
+@dataclass(frozen=True)
+class MetricsData:
+    """An array of ResourceMetrics"""
+
+    resource_metrics: Sequence[ResourceMetrics]
+
+    def to_json(self, indent: Optional[int] = 4) -> str:
+        return dumps(
+            {
+                "resource_metrics": [
+                    loads(resource_metrics.to_json(indent=indent))
+                    for resource_metrics in self.resource_metrics
+                ]
+            },
+            indent=indent,
+        )
diff --git a/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/sdk_configuration.py b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/sdk_configuration.py
new file mode 100644
index 00000000..3d88facb
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/sdk_configuration.py
@@ -0,0 +1,30 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# pylint: disable=unused-import
+
+from dataclasses import dataclass
+from typing import Sequence
+
+# This kind of import is needed to avoid Sphinx errors.
+import opentelemetry.sdk.metrics
+import opentelemetry.sdk.resources
+
+
+@dataclass
+class SdkConfiguration:
+    exemplar_filter: "opentelemetry.sdk.metrics.ExemplarFilter"
+    resource: "opentelemetry.sdk.resources.Resource"
+    metric_readers: Sequence["opentelemetry.sdk.metrics.MetricReader"]
+    views: Sequence["opentelemetry.sdk.metrics.View"]
diff --git a/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/view.py b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/view.py
new file mode 100644
index 00000000..b3fa029d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/_internal/view.py
@@ -0,0 +1,195 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from fnmatch import fnmatch
+from logging import getLogger
+from typing import Callable, Optional, Set, Type
+
+from opentelemetry.metrics import Instrument
+from opentelemetry.sdk.metrics._internal.aggregation import (
+    Aggregation,
+    DefaultAggregation,
+    _Aggregation,
+    _ExplicitBucketHistogramAggregation,
+    _ExponentialBucketHistogramAggregation,
+)
+from opentelemetry.sdk.metrics._internal.exemplar import (
+    AlignedHistogramBucketExemplarReservoir,
+    ExemplarReservoirBuilder,
+    SimpleFixedSizeExemplarReservoir,
+)
+
+_logger = getLogger(__name__)
+
+
+def _default_reservoir_factory(
+    aggregation_type: Type[_Aggregation],
+) -> ExemplarReservoirBuilder:
+    """Default reservoir factory per aggregation."""
+    if issubclass(aggregation_type, _ExplicitBucketHistogramAggregation):
+        return AlignedHistogramBucketExemplarReservoir
+    if issubclass(aggregation_type, _ExponentialBucketHistogramAggregation):
+        return SimpleFixedSizeExemplarReservoir
+    return SimpleFixedSizeExemplarReservoir
+
+
+class View:
+    """
+    A `View` configuration parameters can be used for the following
+    purposes:
+
+    1. Match instruments: When an instrument matches a view, measurements
+       received by that instrument will be processed.
+    2. Customize metric streams: A metric stream is identified by a match
+       between a view and an instrument and a set of attributes. The metric
+       stream can be customized by certain attributes of the corresponding view.
+
+    The attributes documented next serve one of the previous two purposes.
+
+    Args:
+        instrument_type: This is an instrument matching attribute: the class the
+            instrument must be to match the view.
+
+        instrument_name: This is an instrument matching attribute: the name the
+            instrument must have to match the view. Wild card characters are supported. Wild
+            card characters should not be used with this attribute if the view has also a
+            ``name`` defined.
+
+        meter_name: This is an instrument matching attribute: the name the
+            instrument meter must have to match the view.
+
+        meter_version: This is an instrument matching attribute: the version
+            the instrument meter must have to match the view.
+
+        meter_schema_url: This is an instrument matching attribute: the schema
+            URL the instrument meter must have to match the view.
+
+        name: This is a metric stream customizing attribute: the name of the
+            metric stream. If `None`, the name of the instrument will be used.
+
+        description: This is a metric stream customizing attribute: the
+            description of the metric stream. If `None`, the description of the instrument will
+            be used.
+
+        attribute_keys: This is a metric stream customizing attribute: this is
+            a set of attribute keys. If not `None` then only the measurement attributes that
+            are in ``attribute_keys`` will be used to identify the metric stream.
+
+        aggregation: This is a metric stream customizing attribute: the
+            aggregation instance to use when data is aggregated for the
+            corresponding metrics stream. If `None` an instance of
+            `DefaultAggregation` will be used.
+
+        exemplar_reservoir_factory: This is a metric stream customizing attribute:
+            the exemplar reservoir factory
+
+        instrument_unit: This is an instrument matching attribute: the unit the
+            instrument must have to match the view.
+
+    This class is not intended to be subclassed by the user.
+    """
+
+    _default_aggregation = DefaultAggregation()
+
+    def __init__(
+        self,
+        instrument_type: Optional[Type[Instrument]] = None,
+        instrument_name: Optional[str] = None,
+        meter_name: Optional[str] = None,
+        meter_version: Optional[str] = None,
+        meter_schema_url: Optional[str] = None,
+        name: Optional[str] = None,
+        description: Optional[str] = None,
+        attribute_keys: Optional[Set[str]] = None,
+        aggregation: Optional[Aggregation] = None,
+        exemplar_reservoir_factory: Optional[
+            Callable[[Type[_Aggregation]], ExemplarReservoirBuilder]
+        ] = None,
+        instrument_unit: Optional[str] = None,
+    ):
+        if (
+            instrument_type
+            is instrument_name
+            is instrument_unit
+            is meter_name
+            is meter_version
+            is meter_schema_url
+            is None
+        ):
+            # pylint: disable=broad-exception-raised
+            raise Exception(
+                "Some instrument selection "
+                f"criteria must be provided for View {name}"
+            )
+
+        if (
+            name is not None
+            and instrument_name is not None
+            and ("*" in instrument_name or "?" in instrument_name)
+        ):
+            # pylint: disable=broad-exception-raised
+            raise Exception(
+                f"View {name} declared with wildcard "
+                "characters in instrument_name"
+            )
+
+        # _name, _description, _aggregation, _exemplar_reservoir_factory and
+        # _attribute_keys will be accessed when instantiating a _ViewInstrumentMatch.
+        self._name = name
+        self._instrument_type = instrument_type
+        self._instrument_name = instrument_name
+        self._instrument_unit = instrument_unit
+        self._meter_name = meter_name
+        self._meter_version = meter_version
+        self._meter_schema_url = meter_schema_url
+
+        self._description = description
+        self._attribute_keys = attribute_keys
+        self._aggregation = aggregation or self._default_aggregation
+        self._exemplar_reservoir_factory = (
+            exemplar_reservoir_factory or _default_reservoir_factory
+        )
+
+    # pylint: disable=too-many-return-statements
+    # pylint: disable=too-many-branches
+    def _match(self, instrument: Instrument) -> bool:
+        if self._instrument_type is not None:
+            if not isinstance(instrument, self._instrument_type):
+                return False
+
+        if self._instrument_name is not None:
+            if not fnmatch(instrument.name, self._instrument_name):
+                return False
+
+        if self._instrument_unit is not None:
+            if not fnmatch(instrument.unit, self._instrument_unit):
+                return False
+
+        if self._meter_name is not None:
+            if instrument.instrumentation_scope.name != self._meter_name:
+                return False
+
+        if self._meter_version is not None:
+            if instrument.instrumentation_scope.version != self._meter_version:
+                return False
+
+        if self._meter_schema_url is not None:
+            if (
+                instrument.instrumentation_scope.schema_url
+                != self._meter_schema_url
+            ):
+                return False
+
+        return True
diff --git a/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/export/__init__.py b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/export/__init__.py
new file mode 100644
index 00000000..478237cd
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/export/__init__.py
@@ -0,0 +1,66 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from opentelemetry.sdk.metrics._internal.export import (
+    AggregationTemporality,
+    ConsoleMetricExporter,
+    InMemoryMetricReader,
+    MetricExporter,
+    MetricExportResult,
+    MetricReader,
+    PeriodicExportingMetricReader,
+)
+
+# The point module is not in the export directory to avoid a circular import.
+from opentelemetry.sdk.metrics._internal.point import (  # noqa: F401
+    Buckets,
+    DataPointT,
+    DataT,
+    ExponentialHistogram,
+    ExponentialHistogramDataPoint,
+    Gauge,
+    Histogram,
+    HistogramDataPoint,
+    Metric,
+    MetricsData,
+    NumberDataPoint,
+    ResourceMetrics,
+    ScopeMetrics,
+    Sum,
+)
+
+__all__ = [
+    "AggregationTemporality",
+    "Buckets",
+    "ConsoleMetricExporter",
+    "InMemoryMetricReader",
+    "MetricExporter",
+    "MetricExportResult",
+    "MetricReader",
+    "PeriodicExportingMetricReader",
+    "DataPointT",
+    "DataT",
+    "ExponentialHistogram",
+    "ExponentialHistogramDataPoint",
+    "Gauge",
+    "Histogram",
+    "HistogramDataPoint",
+    "Metric",
+    "MetricsData",
+    "NumberDataPoint",
+    "ResourceMetrics",
+    "ScopeMetrics",
+    "Sum",
+]
diff --git a/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/view/__init__.py b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/view/__init__.py
new file mode 100644
index 00000000..c07adf6c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/opentelemetry/sdk/metrics/view/__init__.py
@@ -0,0 +1,35 @@
+# Copyright The OpenTelemetry Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from opentelemetry.sdk.metrics._internal.aggregation import (
+    Aggregation,
+    DefaultAggregation,
+    DropAggregation,
+    ExplicitBucketHistogramAggregation,
+    ExponentialBucketHistogramAggregation,
+    LastValueAggregation,
+    SumAggregation,
+)
+from opentelemetry.sdk.metrics._internal.view import View
+
+__all__ = [
+    "Aggregation",
+    "DefaultAggregation",
+    "DropAggregation",
+    "ExplicitBucketHistogramAggregation",
+    "ExponentialBucketHistogramAggregation",
+    "LastValueAggregation",
+    "SumAggregation",
+    "View",
+]