two version of R2R are hereHEADmaster

1 files changed, 111 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/huggingface_hub/utils/_safetensors.py b/.venv/lib/python3.12/site-packages/huggingface_hub/utils/_safetensors.py
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+import functools
+import operator
+from collections import defaultdict
+from dataclasses import dataclass, field
+from typing import Dict, List, Literal, Optional, Tuple
+
+
+FILENAME_T = str
+TENSOR_NAME_T = str
+DTYPE_T = Literal["F64", "F32", "F16", "BF16", "I64", "I32", "I16", "I8", "U8", "BOOL"]
+
+
+@dataclass
+class TensorInfo:
+    """Information about a tensor.
+
+    For more details regarding the safetensors format, check out https://huggingface.co/docs/safetensors/index#format.
+
+    Attributes:
+        dtype (`str`):
+            The data type of the tensor ("F64", "F32", "F16", "BF16", "I64", "I32", "I16", "I8", "U8", "BOOL").
+        shape (`List[int]`):
+            The shape of the tensor.
+        data_offsets (`Tuple[int, int]`):
+            The offsets of the data in the file as a tuple `[BEGIN, END]`.
+        parameter_count (`int`):
+            The number of parameters in the tensor.
+    """
+
+    dtype: DTYPE_T
+    shape: List[int]
+    data_offsets: Tuple[int, int]
+    parameter_count: int = field(init=False)
+
+    def __post_init__(self) -> None:
+        # Taken from https://stackoverflow.com/a/13840436
+        try:
+            self.parameter_count = functools.reduce(operator.mul, self.shape)
+        except TypeError:
+            self.parameter_count = 1  # scalar value has no shape
+
+
+@dataclass
+class SafetensorsFileMetadata:
+    """Metadata for a Safetensors file hosted on the Hub.
+
+    This class is returned by [`parse_safetensors_file_metadata`].
+
+    For more details regarding the safetensors format, check out https://huggingface.co/docs/safetensors/index#format.
+
+    Attributes:
+        metadata (`Dict`):
+            The metadata contained in the file.
+        tensors (`Dict[str, TensorInfo]`):
+            A map of all tensors. Keys are tensor names and values are information about the corresponding tensor, as a
+            [`TensorInfo`] object.
+        parameter_count (`Dict[str, int]`):
+            A map of the number of parameters per data type. Keys are data types and values are the number of parameters
+            of that data type.
+    """
+
+    metadata: Dict[str, str]
+    tensors: Dict[TENSOR_NAME_T, TensorInfo]
+    parameter_count: Dict[DTYPE_T, int] = field(init=False)
+
+    def __post_init__(self) -> None:
+        parameter_count: Dict[DTYPE_T, int] = defaultdict(int)
+        for tensor in self.tensors.values():
+            parameter_count[tensor.dtype] += tensor.parameter_count
+        self.parameter_count = dict(parameter_count)
+
+
+@dataclass
+class SafetensorsRepoMetadata:
+    """Metadata for a Safetensors repo.
+
+    A repo is considered to be a Safetensors repo if it contains either a 'model.safetensors' weight file (non-shared
+    model) or a 'model.safetensors.index.json' index file (sharded model) at its root.
+
+    This class is returned by [`get_safetensors_metadata`].
+
+    For more details regarding the safetensors format, check out https://huggingface.co/docs/safetensors/index#format.
+
+    Attributes:
+        metadata (`Dict`, *optional*):
+            The metadata contained in the 'model.safetensors.index.json' file, if it exists. Only populated for sharded
+            models.
+        sharded (`bool`):
+            Whether the repo contains a sharded model or not.
+        weight_map (`Dict[str, str]`):
+            A map of all weights. Keys are tensor names and values are filenames of the files containing the tensors.
+        files_metadata (`Dict[str, SafetensorsFileMetadata]`):
+            A map of all files metadata. Keys are filenames and values are the metadata of the corresponding file, as
+            a [`SafetensorsFileMetadata`] object.
+        parameter_count (`Dict[str, int]`):
+            A map of the number of parameters per data type. Keys are data types and values are the number of parameters
+            of that data type.
+    """
+
+    metadata: Optional[Dict]
+    sharded: bool
+    weight_map: Dict[TENSOR_NAME_T, FILENAME_T]  # tensor name -> filename
+    files_metadata: Dict[FILENAME_T, SafetensorsFileMetadata]  # filename -> metadata
+    parameter_count: Dict[DTYPE_T, int] = field(init=False)
+
+    def __post_init__(self) -> None:
+        parameter_count: Dict[DTYPE_T, int] = defaultdict(int)
+        for file_metadata in self.files_metadata.values():
+            for dtype, nb_parameters_ in file_metadata.parameter_count.items():
+                parameter_count[dtype] += nb_parameters_
+        self.parameter_count = dict(parameter_count)