FM / utils

/usr/local/lib/python3.6/site-packages/transformers/utils/

/usr/local/lib/python3.6/site-packages/transformers/utils

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
doc.py	38836	0644	edit dl rm
dummy_detectron2_objects.py	391	0644	edit dl rm
dummy_flax_objects.py	24855	0644	edit dl rm
dummy_pt_objects.py	111443	0644	edit dl rm
dummy_pytorch_quantization_and_torch_objects.py	2476	0644	edit dl rm
dummy_scatter_objects.py	1138	0644	edit dl rm
dummy_sentencepiece_and_speech_objects.py	342	0644	edit dl rm
dummy_sentencepiece_and_tokenizers_objects.py	301	0644	edit dl rm
dummy_sentencepiece_objects.py	4371	0644	edit dl rm
dummy_speech_objects.py	315	0644	edit dl rm
dummy_tf_objects.py	50456	0644	edit dl rm
dummy_timm_and_vision_objects.py	903	0644	edit dl rm
dummy_timm_objects.py	805	0644	edit dl rm
dummy_tokenizers_objects.py	8173	0644	edit dl rm
dummy_vision_objects.py	3486	0644	edit dl rm
fx.py	23186	0644	edit dl rm
fx_transformations.py	12178	0644	edit dl rm
generic.py	9820	0644	edit dl rm
hp_naming.py	4971	0644	edit dl rm
hub.py	42932	0644	edit dl rm
import_utils.py	29288	0644	edit dl rm
logging.py	9516	0644	edit dl rm
model_parallel_utils.py	2276	0644	edit dl rm
notebook.py	14524	0644	edit dl rm
sentencepiece_model_pb2.py	39607	0644	edit dl rm
versions.py	4378	0644	edit dl rm
__init__.py	5055	0644	edit dl rm

Edit: /usr/local/lib/python3.6/site-packages/transformers/utils/generic.py (9820B)

# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# 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.
"""
Generic utilities
"""

import inspect
from collections import OrderedDict, UserDict
from contextlib import ExitStack
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple

import numpy as np

from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy

class cached_property(property):
    """
    Descriptor that mimics @property but caches output in member variable.

From tensorflow_datasets

Built-in in functools from Python 3.8.
    """

def __get__(self, obj, objtype=None):
        # See docs.python.org/3/howto/descriptor.html#properties
        if obj is None:
            return self
        if self.fget is None:
            raise AttributeError("unreadable attribute")
        attr = "__cached_" + self.fget.__name__
        cached = getattr(obj, attr, None)
        if cached is None:
            cached = self.fget(obj)
            setattr(obj, attr, cached)
        return cached

def is_tensor(x):
    """
    Tests if `x` is a `torch.Tensor`, `tf.Tensor`, `jaxlib.xla_extension.DeviceArray` or `np.ndarray`.
    """
    if is_torch_fx_proxy(x):
        return True
    if is_torch_available():
        import torch

if isinstance(x, torch.Tensor):
            return True
    if is_tf_available():
        import tensorflow as tf

if isinstance(x, tf.Tensor):
            return True

if is_flax_available():
        import jax.numpy as jnp
        from jax.core import Tracer

if isinstance(x, (jnp.ndarray, Tracer)):
            return True

return isinstance(x, np.ndarray)

def _is_numpy(x):
    return isinstance(x, np.ndarray)

def _is_torch(x):
    import torch

return isinstance(x, torch.Tensor)

def _is_torch_device(x):
    import torch

return isinstance(x, torch.device)

def _is_tensorflow(x):
    import tensorflow as tf

return isinstance(x, tf.Tensor)

def _is_jax(x):
    import jax.numpy as jnp  # noqa: F811

return isinstance(x, jnp.ndarray)

def to_py_obj(obj):
    """
    Convert a TensorFlow tensor, PyTorch tensor, Numpy array or python list to a python list.
    """
    if isinstance(obj, (dict, UserDict)):
        return {k: to_py_obj(v) for k, v in obj.items()}
    elif isinstance(obj, (list, tuple)):
        return [to_py_obj(o) for o in obj]
    elif is_tf_available() and _is_tensorflow(obj):
        return obj.numpy().tolist()
    elif is_torch_available() and _is_torch(obj):
        return obj.detach().cpu().tolist()
    elif is_flax_available() and _is_jax(obj):
        return np.asarray(obj).tolist()
    elif isinstance(obj, (np.ndarray, np.number)):  # tolist also works on 0d np arrays
        return obj.tolist()
    else:
        return obj

def to_numpy(obj):
    """
    Convert a TensorFlow tensor, PyTorch tensor, Numpy array or python list to a Numpy array.
    """
    if isinstance(obj, (dict, UserDict)):
        return {k: to_numpy(v) for k, v in obj.items()}
    elif isinstance(obj, (list, tuple)):
        return np.array(obj)
    elif is_tf_available() and _is_tensorflow(obj):
        return obj.numpy()
    elif is_torch_available() and _is_torch(obj):
        return obj.detach().cpu().numpy()
    elif is_flax_available() and _is_jax(obj):
        return np.asarray(obj)
    else:
        return obj

class ModelOutput(OrderedDict):
    """
    Base class for all model outputs as dataclass. Has a `__getitem__` that allows indexing by integer or slice (like a
    tuple) or strings (like a dictionary) that will ignore the `None` attributes. Otherwise behaves like a regular
    python dictionary.

You can't unpack a `ModelOutput` directly. Use the [`~utils.ModelOutput.to_tuple`] method to convert it to a tuple
    before.

</Tip>
    """

def __post_init__(self):
        class_fields = fields(self)

# Safety and consistency checks
        if not len(class_fields):
            raise ValueError(f"{self.__class__.__name__} has no fields.")
        if not all(field.default is None for field in class_fields[1:]):
            raise ValueError(f"{self.__class__.__name__} should not have more than one required field.")

first_field = getattr(self, class_fields[0].name)
        other_fields_are_none = all(getattr(self, field.name) is None for field in class_fields[1:])

if other_fields_are_none and not is_tensor(first_field):
            if isinstance(first_field, dict):
                iterator = first_field.items()
                first_field_iterator = True
            else:
                try:
                    iterator = iter(first_field)
                    first_field_iterator = True
                except TypeError:
                    first_field_iterator = False

# if we provided an iterator as first field and the iterator is a (key, value) iterator
            # set the associated fields
            if first_field_iterator:
                for element in iterator:
                    if (
                        not isinstance(element, (list, tuple))
                        or not len(element) == 2
                        or not isinstance(element[0], str)
                    ):
                        break
                    setattr(self, element[0], element[1])
                    if element[1] is not None:
                        self[element[0]] = element[1]
            elif first_field is not None:
                self[class_fields[0].name] = first_field
        else:
            for field in class_fields:
                v = getattr(self, field.name)
                if v is not None:
                    self[field.name] = v

def __delitem__(self, *args, **kwargs):
        raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.")

def setdefault(self, *args, **kwargs):
        raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.")

def pop(self, *args, **kwargs):
        raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance.")

def update(self, *args, **kwargs):
        raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance.")

def __getitem__(self, k):
        if isinstance(k, str):
            inner_dict = {k: v for (k, v) in self.items()}
            return inner_dict[k]
        else:
            return self.to_tuple()[k]

def __setattr__(self, name, value):
        if name in self.keys() and value is not None:
            # Don't call self.__setitem__ to avoid recursion errors
            super().__setitem__(name, value)
        super().__setattr__(name, value)

def __setitem__(self, key, value):
        # Will raise a KeyException if needed
        super().__setitem__(key, value)
        # Don't call self.__setattr__ to avoid recursion errors
        super().__setattr__(key, value)

def to_tuple(self) -> Tuple[Any]:
        """
        Convert self to a tuple containing all the attributes/keys that are not `None`.
        """
        return tuple(self[k] for k in self.keys())

class ExplicitEnum(Enum):
    """
    Enum with more explicit error message for missing values.
    """

@classmethod
    def _missing_(cls, value):
        raise ValueError(
            f"{value} is not a valid {cls.__name__}, please select one of {list(cls._value2member_map_.keys())}"
        )

class PaddingStrategy(ExplicitEnum):
    """
    Possible values for the `padding` argument in [`PreTrainedTokenizerBase.__call__`]. Useful for tab-completion in an
    IDE.
    """

LONGEST = "longest"
    MAX_LENGTH = "max_length"
    DO_NOT_PAD = "do_not_pad"

class TensorType(ExplicitEnum):
    """
    Possible values for the `return_tensors` argument in [`PreTrainedTokenizerBase.__call__`]. Useful for
    tab-completion in an IDE.
    """

PYTORCH = "pt"
    TENSORFLOW = "tf"
    NUMPY = "np"
    JAX = "jax"

class ContextManagers:
    """
    Wrapper for `contextlib.ExitStack` which enters a collection of context managers. Adaptation of `ContextManagers`
    in the `fastcore` library.
    """

def __init__(self, context_managers: List[ContextManager]):
        self.context_managers = context_managers
        self.stack = ExitStack()

def __enter__(self):
        for context_manager in self.context_managers:
            self.stack.enter_context(context_manager)

def __exit__(self, *args, **kwargs):
        self.stack.__exit__(*args, **kwargs)

def find_labels(model_class):
    """
    Find the labels used by a given model.

Args:
        model_class (`type`): The class of the model.
    """
    model_name = model_class.__name__
    if model_name.startswith("TF"):
        signature = inspect.signature(model_class.call)
    elif model_name.startswith("Flax"):
        signature = inspect.signature(model_class.__call__)
    else:
        signature = inspect.signature(model_class.forward)
    if "QuestionAnswering" in model_name:
        return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
    else:
        return [p for p in signature.parameters if "label" in p]

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