hf-dev-train/transformers-main/src/transformers/utils/generic.py

# 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
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
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


if is_flax_available():
    import jax.numpy as jnp


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_numpy_array(x):
    """
    Tests if `x` is a numpy array or not.
    """
    return _is_numpy(x)


def _is_torch(x):
    import torch

    return isinstance(x, torch.Tensor)


def is_torch_tensor(x):
    """
    Tests if `x` is a torch tensor or not. Safe to call even if torch is not installed.
    """
    return False if not is_torch_available() else _is_torch(x)


def _is_torch_device(x):
    import torch

    return isinstance(x, torch.device)


def is_torch_device(x):
    """
    Tests if `x` is a torch device or not. Safe to call even if torch is not installed.
    """
    return False if not is_torch_available() else _is_torch_device(x)


def _is_torch_dtype(x):
    import torch

    if isinstance(x, str):
        if hasattr(torch, x):
            x = getattr(torch, x)
        else:
            return False
    return isinstance(x, torch.dtype)


def is_torch_dtype(x):
    """
    Tests if `x` is a torch dtype or not. Safe to call even if torch is not installed.
    """
    return False if not is_torch_available() else _is_torch_dtype(x)


def _is_tensorflow(x):
    import tensorflow as tf

    return isinstance(x, tf.Tensor)


def is_tf_tensor(x):
    """
    Tests if `x` is a tensorflow tensor or not. Safe to call even if tensorflow is not installed.
    """
    return False if not is_tf_available() else _is_tensorflow(x)


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

    return isinstance(x, jnp.ndarray)


def is_jax_tensor(x):
    """
    Tests if `x` is a Jax tensor or not. Safe to call even if jax is not installed.
    """
    return False if not is_flax_available() else _is_jax(x)


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_tensor(obj):
        return obj.numpy().tolist()
    elif is_torch_tensor(obj):
        return obj.detach().cpu().tolist()
    elif is_jax_tensor(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_tensor(obj):
        return obj.numpy()
    elif is_torch_tensor(obj):
        return obj.detach().cpu().numpy()
    elif is_jax_tensor(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.

    <Tip warning={true}>

    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 idx, element in enumerate(iterator):
                    if (
                        not isinstance(element, (list, tuple))
                        or not len(element) == 2
                        or not isinstance(element[0], str)
                    ):
                        if idx == 0:
                            # If we do not have an iterator of key/values, set it as attribute
                            self[class_fields[0].name] = first_field
                        else:
                            # If we have a mixed iterator, raise an error
                            raise ValueError(
                                f"Cannot set key/value for {element}. It needs to be a tuple (key, value)."
                            )
                        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 = dict(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(str, 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 can_return_loss(model_class):
    """
    Check if a given model can return loss.

    Args:
        model_class (`type`): The class of the model.
    """
    base_classes = str(inspect.getmro(model_class))

    if "keras.engine.training.Model" in base_classes:
        signature = inspect.signature(model_class.call)  # TensorFlow models
    elif "torch.nn.modules.module.Module" in base_classes:
        signature = inspect.signature(model_class.forward)  # PyTorch models
    else:
        signature = inspect.signature(model_class.__call__)  # Flax models

    for p in signature.parameters:
        if p == "return_loss" and signature.parameters[p].default is True:
            return True

    return False


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__
    base_classes = str(inspect.getmro(model_class))

    if "keras.engine.training.Model" in base_classes:
        signature = inspect.signature(model_class.call)  # TensorFlow models
    elif "torch.nn.modules.module.Module" in base_classes:
        signature = inspect.signature(model_class.forward)  # PyTorch models
    else:
        signature = inspect.signature(model_class.__call__)  # Flax models

    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]


def flatten_dict(d: MutableMapping, parent_key: str = "", delimiter: str = "."):
    """Flatten a nested dict into a single level dict."""

    def _flatten_dict(d, parent_key="", delimiter="."):
        for k, v in d.items():
            key = str(parent_key) + delimiter + str(k) if parent_key else k
            if v and isinstance(v, MutableMapping):
                yield from flatten_dict(v, key, delimiter=delimiter).items()
            else:
                yield key, v

    return dict(_flatten_dict(d, parent_key, delimiter))


@contextmanager
def working_or_temp_dir(working_dir, use_temp_dir: bool = False):
    if use_temp_dir:
        with tempfile.TemporaryDirectory() as tmp_dir:
            yield tmp_dir
    else:
        yield working_dir


def transpose(array, axes=None):
    """
    Framework-agnostic version of `numpy.transpose` that will work on torch/TensorFlow/Jax tensors as well as NumPy
    arrays.
    """
    if is_numpy_array(array):
        return np.transpose(array, axes=axes)
    elif is_torch_tensor(array):
        return array.T if axes is None else array.permute(*axes)
    elif is_tf_tensor(array):
        import tensorflow as tf

        return tf.transpose(array, perm=axes)
    elif is_jax_tensor(array):
        return jnp.transpose(array, axes=axes)
    else:
        raise ValueError(f"Type not supported for transpose: {type(array)}.")


def reshape(array, newshape):
    """
    Framework-agnostic version of `numpy.reshape` that will work on torch/TensorFlow/Jax tensors as well as NumPy
    arrays.
    """
    if is_numpy_array(array):
        return np.reshape(array, newshape)
    elif is_torch_tensor(array):
        return array.reshape(*newshape)
    elif is_tf_tensor(array):
        import tensorflow as tf

        return tf.reshape(array, newshape)
    elif is_jax_tensor(array):
        return jnp.reshape(array, newshape)
    else:
        raise ValueError(f"Type not supported for reshape: {type(array)}.")


def squeeze(array, axis=None):
    """
    Framework-agnostic version of `numpy.squeeze` that will work on torch/TensorFlow/Jax tensors as well as NumPy
    arrays.
    """
    if is_numpy_array(array):
        return np.squeeze(array, axis=axis)
    elif is_torch_tensor(array):
        return array.squeeze() if axis is None else array.squeeze(dim=axis)
    elif is_tf_tensor(array):
        import tensorflow as tf

        return tf.squeeze(array, axis=axis)
    elif is_jax_tensor(array):
        return jnp.squeeze(array, axis=axis)
    else:
        raise ValueError(f"Type not supported for squeeze: {type(array)}.")


def expand_dims(array, axis):
    """
    Framework-agnostic version of `numpy.expand_dims` that will work on torch/TensorFlow/Jax tensors as well as NumPy
    arrays.
    """
    if is_numpy_array(array):
        return np.expand_dims(array, axis)
    elif is_torch_tensor(array):
        return array.unsqueeze(dim=axis)
    elif is_tf_tensor(array):
        import tensorflow as tf

        return tf.expand_dims(array, axis=axis)
    elif is_jax_tensor(array):
        return jnp.expand_dims(array, axis=axis)
    else:
        raise ValueError(f"Type not supported for expand_dims: {type(array)}.")


def tensor_size(array):
    """
    Framework-agnostic version of `numpy.size` that will work on torch/TensorFlow/Jax tensors as well as NumPy arrays.
    """
    if is_numpy_array(array):
        return np.size(array)
    elif is_torch_tensor(array):
        return array.numel()
    elif is_tf_tensor(array):
        import tensorflow as tf

        return tf.size(array)
    elif is_jax_tensor(array):
        return array.size
    else:
        raise ValueError(f"Type not supported for expand_dims: {type(array)}.")