
"""simple docstring"""


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  lowerCAmelCase_ (					__UpperCAmelCase			):



						'''simple docstring'''


						def __get__(       self   :					str     ,A_   :					Any     ,A_   :					List[str]=None					)   ->      Tuple:
							# See docs.python.org/3/howto/descriptor.html#properties
							if obj is None:
								return self
							if self.fget is None:
								raise AttributeError('unreadable attribute'					)
							A        =      '__cached_' + self.fget.__name__
							A        =      getattr(lowerCamelCase__     ,lowerCamelCase__     ,lowerCamelCase__					)
							if cached is None:
								A        =      self.fget(lowerCamelCase__					)
								setattr(lowerCamelCase__     ,lowerCamelCase__     ,lowerCamelCase__					)
							return cached






def 					_snake_case (						snake_case__      :   Optional[int]   ):
	A        =      val.lower()
	if val in {"y", "yes", "t", "true", "on", "1"}:
		return 1
	if val in {"n", "no", "f", "false", "off", "0"}:
		return 0
	raise ValueError(F'invalid truth value {val!r}'   )






def 					_snake_case (						snake_case__      :   Dict   ):
	if is_torch_fx_proxy(snake_case__   ):
		return True
	if is_torch_available():
		import torch

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

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

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

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

	return isinstance(snake_case__		,					np.ndarray   )






def 					_snake_case (						snake_case__      :   Union[str, Any]   ):
	return isinstance(snake_case__		,					np.ndarray   )






def 					_snake_case (						snake_case__      :   Dict   ):
	return _is_numpy(snake_case__   )






def 					_snake_case (						snake_case__      :   Tuple   ):
	import torch

	return isinstance(snake_case__		,					torch.Tensor   )






def 					_snake_case (						snake_case__      :   Dict   ):
	return False if not is_torch_available() else _is_torch(snake_case__   )






def 					_snake_case (						snake_case__      :   Any   ):
	import torch

	return isinstance(snake_case__		,					torch.device   )






def 					_snake_case (						snake_case__      :   Optional[Any]   ):
	return False if not is_torch_available() else _is_torch_device(snake_case__   )






def 					_snake_case (						snake_case__      :   int   ):
	import torch

	if isinstance(snake_case__		,					snake_case__   ):
		if hasattr(snake_case__		,					snake_case__   ):
			A        =      getattr(snake_case__		,					snake_case__   )
		else:
			return False
	return isinstance(snake_case__		,					torch.dtype   )






def 					_snake_case (						snake_case__      :   Tuple   ):
	return False if not is_torch_available() else _is_torch_dtype(snake_case__   )






def 					_snake_case (						snake_case__      :   List[Any]   ):
	import tensorflow as tf

	return isinstance(snake_case__		,					tf.Tensor   )






def 					_snake_case (						snake_case__      :   Dict   ):
	return False if not is_tf_available() else _is_tensorflow(snake_case__   )






def 					_snake_case (						snake_case__      :   List[str]   ):
	import tensorflow as tf

	# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
	if hasattr(snake_case__		,					'is_symbolic_tensor'   ):
		return tf.is_symbolic_tensor(snake_case__   )
	return type(snake_case__   ) == tf.Tensor






def 					_snake_case (						snake_case__      :   Union[str, Any]   ):
	return False if not is_tf_available() else _is_tf_symbolic_tensor(snake_case__   )






def 					_snake_case (						snake_case__      :   Optional[int]   ):
	import jax.numpy as jnp  # noqa: F811

	return isinstance(snake_case__		,					jnp.ndarray   )






def 					_snake_case (						snake_case__      :   Tuple   ):
	return False if not is_flax_available() else _is_jax(snake_case__   )






def 					_snake_case (						snake_case__      :   List[Any]   ):
	if isinstance(snake_case__		,					(dict, UserDict)   ):
		return {k: to_py_obj(snake_case__   ) for k, v in obj.items()}
	elif isinstance(snake_case__		,					(list, tuple)   ):
		return [to_py_obj(snake_case__   ) for o in obj]
	elif is_tf_tensor(snake_case__   ):
		return obj.numpy().tolist()
	elif is_torch_tensor(snake_case__   ):
		return obj.detach().cpu().tolist()
	elif is_jax_tensor(snake_case__   ):
		return np.asarray(snake_case__   ).tolist()
	elif isinstance(snake_case__		,					(np.ndarray, np.number)   ):  # tolist also works on 0d np arrays
		return obj.tolist()
	else:
		return obj






def 					_snake_case (						snake_case__      :   Optional[Any]   ):
	if isinstance(snake_case__		,					(dict, UserDict)   ):
		return {k: to_numpy(snake_case__   ) for k, v in obj.items()}
	elif isinstance(snake_case__		,					(list, tuple)   ):
		return np.array(snake_case__   )
	elif is_tf_tensor(snake_case__   ):
		return obj.numpy()
	elif is_torch_tensor(snake_case__   ):
		return obj.detach().cpu().numpy()
	elif is_jax_tensor(snake_case__   ):
		return np.asarray(snake_case__   )
	else:
		return obj







class  lowerCAmelCase_ (					__UpperCAmelCase			):



						'''simple docstring'''


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[Any]					)   ->      Any:
							A        =      fields(self					)

							# Safety and consistency checks
							if not len(lowerCamelCase__					):
								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.'					)

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

							if other_fields_are_none and not is_tensor(lowerCamelCase__					):
								if isinstance(lowerCamelCase__     ,lowerCamelCase__					):
									A        =      first_field.items()
									A        =      True
								else:
									try:
										A        =      iter(lowerCamelCase__					)
										A        =      True
									except TypeError:
										A        =      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(lowerCamelCase__					):
										if (
										    not isinstance(lowerCamelCase__     ,(list, tuple)					)
										    or not len(lowerCamelCase__					) == 2
										    or not isinstance(element[0]     ,lowerCamelCase__					)
										):
											if idx == 0:
												# If we do not have an iterator of key/values, set it as attribute
												A        =      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:
											A        =      element[1]
								elif first_field is not None:
									A        =      first_field
							else:
								for field in class_fields:
									A        =      getattr(self     ,field.name					)
									if v is not None:
										A        =      v


						def __delitem__(       self   :					Union[str, Any]     ,*A_   :					List[str]     ,**A_   :					Dict					)   ->      str:
							raise Exception(F'You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]     ,*A_   :					List[str]     ,**A_   :					str					)   ->      int:
							raise Exception(F'You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					str     ,*A_   :					Any     ,**A_   :					Union[str, Any]					)   ->      str:
							raise Exception(F'You cannot use ``pop`` on a {self.__class__.__name__} instance.'					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					str     ,*A_   :					Optional[Any]     ,**A_   :					List[Any]					)   ->      List[str]:
							raise Exception(F'You cannot use ``update`` on a {self.__class__.__name__} instance.'					)


						def __getitem__(       self   :					List[str]     ,A_   :					List[Any]					)   ->      Union[str, Any]:
							if isinstance(lowerCamelCase__     ,lowerCamelCase__					):
								A        =      dict(self.items()					)
								return inner_dict[k]
							else:
								return self.to_tuple()[k]


						def __setattr__(       self   :					Optional[int]     ,A_   :					str     ,A_   :					Optional[int]					)   ->      Union[str, Any]:
							if name in self.keys() and value is not None:
								# Don't call self.__setitem__ to avoid recursion errors
								super().__setitem__(lowerCamelCase__     ,lowerCamelCase__					)
							super().__setattr__(lowerCamelCase__     ,lowerCamelCase__					)


						def __setitem__(       self   :					Optional[Any]     ,A_   :					str     ,A_   :					Dict					)   ->      str:
							# Will raise a KeyException if needed
							super().__setitem__(lowerCamelCase__     ,lowerCamelCase__					)
							# Don't call self.__setattr__ to avoid recursion errors
							super().__setattr__(lowerCamelCase__     ,lowerCamelCase__					)




						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]					)   ->      Optional[int]:
							return tuple(self[k] for k in self.keys()					)







class  lowerCAmelCase_ (					__UpperCAmelCase						,    __UpperCAmelCase			):



						'''simple docstring'''


						@classmethod
						def   _SCREAMING_SNAKE_CASE					(       cls   :					List[str]     ,A_   :					List[Any]					)   ->      int:
							raise ValueError(
							    F'{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys()					)}'					)







class  lowerCAmelCase_ (					__UpperCAmelCase			):



						'''simple docstring'''


						_lowerCamelCase:			str					=			'''longest'''
						_lowerCamelCase:			Optional[Any]					=			'''max_length'''
						_lowerCamelCase:			List[Any]					=			'''do_not_pad'''







class  lowerCAmelCase_ (					__UpperCAmelCase			):



						'''simple docstring'''


						_lowerCamelCase:			Any					=			'''pt'''
						_lowerCamelCase:			List[str]					=			'''tf'''
						_lowerCamelCase:			Dict					=			'''np'''
						_lowerCamelCase:			str					=			'''jax'''







class  lowerCAmelCase_ :



						'''simple docstring'''


						def __init__(       self   :					Tuple     ,A_   :					List[ContextManager]					)   ->      Any:
							A        =      context_managers
							A        =      ExitStack()


						def __enter__(       self   :					Union[str, Any]					)   ->      int:
							for context_manager in self.context_managers:
								self.stack.enter_context(lowerCamelCase__					)




						def __exit__(       self   :					Union[str, Any]     ,*A_   :					Optional[Any]     ,**A_   :					Dict					)   ->      Any:
							self.stack.__exit__(*lowerCamelCase__     ,**lowerCamelCase__					)






def 					_snake_case (						snake_case__      :   str   ):
	A        =      infer_framework(snake_case__   )
	if framework == "tf":
		A        =      inspect.signature(model_class.call   )  # TensorFlow models
	elif framework == "pt":
		A        =      inspect.signature(model_class.forward   )  # PyTorch models
	else:
		A        =      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 					_snake_case (						snake_case__      :   int   ):
	A        =      model_class.__name__
	A        =      infer_framework(snake_case__   )
	if framework == "tf":
		A        =      inspect.signature(model_class.call   )  # TensorFlow models
	elif framework == "pt":
		A        =      inspect.signature(model_class.forward   )  # PyTorch models
	else:
		A        =      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 					_snake_case (						snake_case__      :   Dict		,					snake_case__      :   Any = ""		,					snake_case__      :   str = "."   ):

	def _flatten_dict(snake_case__      :   Any		,					snake_case__      :   Dict=""		,					snake_case__      :   List[str]="."   ):
		for k, v in d.items():
			A        =      str(snake_case__   ) + delimiter + str(snake_case__   ) if parent_key else k
			if v and isinstance(snake_case__		,					snake_case__   ):
				yield from flatten_dict(snake_case__		,					snake_case__		,					delimiter=snake_case__   ).items()
			else:
				yield key, v

	return dict(_flatten_dict(snake_case__		,					snake_case__		,					snake_case__   )   )






@contextmanager
def 					_snake_case (						snake_case__      :   Tuple		,					snake_case__      :   str = False   ):
	if use_temp_dir:
		with tempfile.TemporaryDirectory() as tmp_dir:
			yield tmp_dir
	else:
		yield working_dir






def 					_snake_case (						snake_case__      :   Any		,					snake_case__      :   Dict=None   ):
	if is_numpy_array(snake_case__   ):
		return np.transpose(snake_case__		,					axes=snake_case__   )
	elif is_torch_tensor(snake_case__   ):
		return array.T if axes is None else array.permute(*snake_case__   )
	elif is_tf_tensor(snake_case__   ):
		import tensorflow as tf

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






def 					_snake_case (						snake_case__      :   str		,					snake_case__      :   Optional[Any]   ):
	if is_numpy_array(snake_case__   ):
		return np.reshape(snake_case__		,					snake_case__   )
	elif is_torch_tensor(snake_case__   ):
		return array.reshape(*snake_case__   )
	elif is_tf_tensor(snake_case__   ):
		import tensorflow as tf

		return tf.reshape(snake_case__		,					snake_case__   )
	elif is_jax_tensor(snake_case__   ):
		return jnp.reshape(snake_case__		,					snake_case__   )
	else:
		raise ValueError(F'Type not supported for reshape: {type(snake_case__   )}.'   )






def 					_snake_case (						snake_case__      :   str		,					snake_case__      :   Optional[Any]=None   ):
	if is_numpy_array(snake_case__   ):
		return np.squeeze(snake_case__		,					axis=snake_case__   )
	elif is_torch_tensor(snake_case__   ):
		return array.squeeze() if axis is None else array.squeeze(dim=snake_case__   )
	elif is_tf_tensor(snake_case__   ):
		import tensorflow as tf

		return tf.squeeze(snake_case__		,					axis=snake_case__   )
	elif is_jax_tensor(snake_case__   ):
		return jnp.squeeze(snake_case__		,					axis=snake_case__   )
	else:
		raise ValueError(F'Type not supported for squeeze: {type(snake_case__   )}.'   )






def 					_snake_case (						snake_case__      :   List[Any]		,					snake_case__      :   Tuple   ):
	if is_numpy_array(snake_case__   ):
		return np.expand_dims(snake_case__		,					snake_case__   )
	elif is_torch_tensor(snake_case__   ):
		return array.unsqueeze(dim=snake_case__   )
	elif is_tf_tensor(snake_case__   ):
		import tensorflow as tf

		return tf.expand_dims(snake_case__		,					axis=snake_case__   )
	elif is_jax_tensor(snake_case__   ):
		return jnp.expand_dims(snake_case__		,					axis=snake_case__   )
	else:
		raise ValueError(F'Type not supported for expand_dims: {type(snake_case__   )}.'   )






def 					_snake_case (						snake_case__      :   Any   ):
	if is_numpy_array(snake_case__   ):
		return np.size(snake_case__   )
	elif is_torch_tensor(snake_case__   ):
		return array.numel()
	elif is_tf_tensor(snake_case__   ):
		import tensorflow as tf

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






def 					_snake_case (						snake_case__      :   Any		,					snake_case__      :   Optional[int]   ):
	for key, value in auto_map.items():
		if isinstance(snake_case__		,					(tuple, list)   ):
			A        =      [F'{repo_id}--{v}' if (v is not None and '--' not in v) else v for v in value]
		elif value is not None and "--" not in value:
			A        =      F'{repo_id}--{value}'

	return auto_map






def 					_snake_case (						snake_case__      :   str   ):
	for base_class in inspect.getmro(snake_case__   ):
		A        =      base_class.__module__
		A        =      base_class.__name__
		if module.startswith('tensorflow'   ) or module.startswith('keras'   ) or name == "TFPreTrainedModel":
			return "tf"
		elif module.startswith('torch'   ) or name == "PreTrainedModel":
			return "pt"
		elif module.startswith('flax'   ) or module.startswith('jax'   ) or name == "FlaxPreTrainedModel":
			return "flax"
	else:
		raise TypeError(F'Could not infer framework from class {model_class}.'   )
"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase__  :  Tuple						     =	{'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  :  List[Any]						     =	[
						    'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'ViTMAEForPreTraining',
						    'ViTMAELayer',
						    'ViTMAEModel',
						    'ViTMAEPreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  :  int						     =	[
						    'TFViTMAEForPreTraining',
						    'TFViTMAEModel',
						    'TFViTMAEPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vit_mae import (
												    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ViTMAEForPreTraining,
												    ViTMAELayer,
												    ViTMAEModel,
												    ViTMAEPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
						import sys

						lowerCAmelCase__  :  int						     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



