






"""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   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	def __get__(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_=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"""				)
		A_		:    Optional[Any]						     = """__cached_""" + self.fget.__name__
		A_		:    Optional[int]						     = getattr(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				)
		if cached is None:
			A_		:    List[Any]						     = self.fget(lowerCAmelCase_				)
			setattr(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				)
		return cached






def       __UpperCamelCase						(		snake_case__       ):
	A_		:    Optional[Any]						     = 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       __UpperCamelCase						(		snake_case__       ):
	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       __UpperCamelCase						(		snake_case__       ):
	return isinstance(snake_case__ , np.ndarray       )






def       __UpperCamelCase						(		snake_case__       ):
	return _is_numpy(snake_case__       )






def       __UpperCamelCase						(		snake_case__       ):
	import torch

	return isinstance(snake_case__ , torch.Tensor       )






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






def       __UpperCamelCase						(		snake_case__       ):
	import torch

	return isinstance(snake_case__ , torch.device       )






def       __UpperCamelCase						(		snake_case__       ):
	return False if not is_torch_available() else _is_torch_device(snake_case__       )






def       __UpperCamelCase						(		snake_case__       ):
	import torch

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






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






def       __UpperCamelCase						(		snake_case__       ):
	import tensorflow as tf

	return isinstance(snake_case__ , tf.Tensor       )






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






def       __UpperCamelCase						(		snake_case__       ):
	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       __UpperCamelCase						(		snake_case__       ):
	return False if not is_tf_available() else _is_tf_symbolic_tensor(snake_case__       )






def       __UpperCamelCase						(		snake_case__       ):
	import jax.numpy as jnp  # noqa: F811

	return isinstance(snake_case__ , jnp.ndarray       )






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






def       __UpperCamelCase						(		snake_case__       ):
	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       __UpperCamelCase						(		snake_case__       ):
	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   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	def  lowerCamelCase(self				):
		A_		:    List[Any]						     = 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_		:    List[str]						     = getattr(self  ,  class_fields[0].name				)
		A_		:    Optional[Any]						     = 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_		:    str						     = first_field.items()
				A_		:    Dict						     = True
			else:
				try:
					A_		:    Any						     = iter(lowerCAmelCase_				)
					A_		:    str						     = True
				except TypeError:
					A_		:    int						     = 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_		:    Dict						     = 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_		:    Union[str, Any]						     = element[1]
			elif first_field is not None:
				A_		:    str						     = first_field
		else:
			for field in class_fields:
				A_		:    int						     = getattr(self  ,  field.name				)
				if v is not None:
					A_		:    Union[str, Any]						     = v





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





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





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





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





	def __getitem__(self  ,  lowerCAmelCase_				):
		if isinstance(lowerCAmelCase_  ,  lowerCAmelCase_				):
			A_		:    int						     = dict(self.items()				)
			return inner_dict[k]
		else:
			return self.to_tuple()[k]





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





	def  lowerCamelCase(self				):
		return tuple(self[k] for k in self.keys()				)






class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




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






class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	_A							:					List[Any] 			=					"""longest"""
	_A							:					List[str] 			=					"""max_length"""
	_A							:					str 			=					"""do_not_pad"""






class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	_A							:					int 			=					"""pt"""
	_A							:					List[Any] 			=					"""tf"""
	_A							:					Any 			=					"""np"""
	_A							:					Optional[int] 			=					"""jax"""






class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	def __init__(self  ,  lowerCAmelCase_				):
		A_		:    str						     = context_managers
		A_		:    Optional[int]						     = ExitStack()





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





	def __exit__(self  ,  *lowerCAmelCase_  ,  **lowerCAmelCase_				):
		self.stack.__exit__(*lowerCAmelCase_  ,  **lowerCAmelCase_				)






def       __UpperCamelCase						(		snake_case__       ):
	A_		:    Dict						     = infer_framework(snake_case__       )
	if framework == "tf":
		A_		:    Optional[Any]						     = inspect.signature(model_class.call       )  # TensorFlow models
	elif framework == "pt":
		A_		:    Union[str, Any]						     = inspect.signature(model_class.forward       )  # PyTorch models
	else:
		A_		:    Union[str, Any]						     = 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       __UpperCamelCase						(		snake_case__       ):
	A_		:    str						     = model_class.__name__
	A_		:    List[str]						     = infer_framework(snake_case__       )
	if framework == "tf":
		A_		:    int						     = inspect.signature(model_class.call       )  # TensorFlow models
	elif framework == "pt":
		A_		:    str						     = inspect.signature(model_class.forward       )  # PyTorch models
	else:
		A_		:    int						     = 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       __UpperCamelCase						(		snake_case__ , snake_case__ = "" , snake_case__ = "."       ):

	def _flatten_dict(snake_case__ , snake_case__="" , snake_case__="."       ):
		for k, v in d.items():
			A_		:    List[Any]						     = 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       __UpperCamelCase						(		snake_case__ , snake_case__ = False       ):
	if use_temp_dir:
		with tempfile.TemporaryDirectory() as tmp_dir:
			yield tmp_dir
	else:
		yield working_dir






def       __UpperCamelCase						(		snake_case__ , snake_case__=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       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	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       __UpperCamelCase						(		snake_case__ , snake_case__=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       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	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       __UpperCamelCase						(		snake_case__       ):
	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       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	for key, value in auto_map.items():
		if isinstance(snake_case__ , (tuple, list)       ):
			A_		:    Optional[Any]						     = [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_		:    Optional[int]						     = F"""{repo_id}--{value}"""

	return auto_map






def       __UpperCamelCase						(		snake_case__       ):
	for base_class in inspect.getmro(snake_case__       ):
		A_		:    Tuple						     = base_class.__module__
		A_		:    Optional[int]						     = 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"""
def       __UpperCamelCase						(		snake_case__ = 200       ):
	A_		:    Union[str, Any]						     = [1, 2, 5, 10, 20, 50, 100, 200]
	A_		:    int						     = [0] * (pence + 1)
	A_		:    Tuple						     = 1  # base case: 1 way to make 0 pence

	for coin in coins:
		for i in range(snake_case__ , pence + 1 , 1       ):
			number_of_ways[i] += number_of_ways[i - coin]
	return number_of_ways[pence]


if __name__ == "__main__":
							assert solution(200) == 73_682

