

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 			_A		(	__magic_name__):

  def __get__(					self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE=None       ):





        """simple docstring"""







        if obj is None:
              return self
        if self.fget is None:
              raise AttributeError('unreadable attribute'       )
        SCREAMING_SNAKE_CASE_					:	List[Any]     							=  '__cached_' + self.fget.__name__
        SCREAMING_SNAKE_CASE_					:	Any     							=  getattr(__lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase       )
        if cached is None:
              SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  self.fget(__lowerCAmelCase       )
              setattr(__lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase       )
        return cached

def  A_	(				a	):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_					:	Union[str, 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  A_	(				a	):

      """simple docstring"""



      if is_torch_fx_proxy(__a	):
            return True
      if is_torch_available():
            import torch

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

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

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

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

      return isinstance(__a       , np.ndarray	)

def  A_	(				a	):

      """simple docstring"""



      return isinstance(__a       , np.ndarray	)

def  A_	(				a	):

      """simple docstring"""



      return _is_numpy(__a	)

def  A_	(				a	):

      """simple docstring"""



      import torch

      return isinstance(__a       , torch.Tensor	)

def  A_	(				a	):

      """simple docstring"""



      return False if not is_torch_available() else _is_torch(__a	)

def  A_	(				a	):

      """simple docstring"""



      import torch

      return isinstance(__a       , torch.device	)

def  A_	(				a	):

      """simple docstring"""



      return False if not is_torch_available() else _is_torch_device(__a	)

def  A_	(				a	):

      """simple docstring"""



      import torch

      if isinstance(__a       , __a	):
            if hasattr(__a       , __a	):
                  SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  getattr(__a       , __a	)
            else:
                  return False
      return isinstance(__a       , torch.dtype	)

def  A_	(				a	):

      """simple docstring"""



      return False if not is_torch_available() else _is_torch_dtype(__a	)

def  A_	(				a	):

      """simple docstring"""



      import tensorflow as tf

      return isinstance(__a       , tf.Tensor	)

def  A_	(				a	):

      """simple docstring"""



      return False if not is_tf_available() else _is_tensorflow(__a	)

def  A_	(				a	):

      """simple docstring"""



      import tensorflow as tf

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

def  A_	(				a	):

      """simple docstring"""



      return False if not is_tf_available() else _is_tf_symbolic_tensor(__a	)

def  A_	(				a	):

      """simple docstring"""



      import jax.numpy as jnp  # noqa: F811

      return isinstance(__a       , jnp.ndarray	)

def  A_	(				a	):

      """simple docstring"""



      return False if not is_flax_available() else _is_jax(__a	)

def  A_	(				a	):

      """simple docstring"""



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

def  A_	(				a	):

      """simple docstring"""



      if isinstance(__a       , (dict, UserDict)	):
            return {k: to_numpy(__a	) for k, v in obj.items()}
      elif isinstance(__a       , (list, tuple)	):
            return np.array(__a	)
      elif is_tf_tensor(__a	):
            return obj.numpy()
      elif is_torch_tensor(__a	):
            return obj.detach().cpu().numpy()
      elif is_jax_tensor(__a	):
            return np.asarray(__a	)
      else:
            return obj
class 			_A		(	__magic_name__):

  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_					:	Dict     							=  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."       )

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

  def __delitem__(					self			,       *_SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE       ):





        """simple docstring"""







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

  def 			UpperCAmelCase     (					self			,       *_SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE       ):





        """simple docstring"""







        raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance."       )

  def 			UpperCAmelCase     (					self			,       *_SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE       ):





        """simple docstring"""







        raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance."       )

  def 			UpperCAmelCase     (					self			,       *_SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE       ):





        """simple docstring"""







        raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance."       )

  def __getitem__(					self			,       _SCREAMING_SNAKE_CASE       ):





        """simple docstring"""







        if isinstance(__lowerCAmelCase			,       __lowerCAmelCase       ):
              SCREAMING_SNAKE_CASE_					:	Any     							=  dict(self.items()       )
              return inner_dict[k]
        else:
              return self.to_tuple()[k]

  def __setattr__(					self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE       ):





        """simple docstring"""







        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			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE       ):





        """simple docstring"""







        super().__setitem__(__lowerCAmelCase			,       __lowerCAmelCase       )
        # Don't call self.__setattr__ to avoid recursion errors
        super().__setattr__(__lowerCAmelCase			,       __lowerCAmelCase       )



  def 			UpperCAmelCase     (					self       ):





        """simple docstring"""







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

class 			_A		(	__magic_name__ ,  __magic_name__):

  @classmethod
  def 			UpperCAmelCase     (					cls			,       _SCREAMING_SNAKE_CASE       ):





        """simple docstring"""







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

class 			_A		(	__magic_name__):
  SCREAMING_SNAKE_CASE     :   Optional[int]							=	'''longest'''
  SCREAMING_SNAKE_CASE     :   str							=	'''max_length'''
  SCREAMING_SNAKE_CASE     :   List[str]							=	'''do_not_pad'''

class 			_A		(	__magic_name__):
  SCREAMING_SNAKE_CASE     :   str							=	'''pt'''
  SCREAMING_SNAKE_CASE     :   int							=	'''tf'''
  SCREAMING_SNAKE_CASE     :   Dict							=	'''np'''
  SCREAMING_SNAKE_CASE     :   Union[str, Any]							=	'''jax'''

class 			_A		:

  def __init__(					self			,       _SCREAMING_SNAKE_CASE       ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_					:	int     							=  context_managers
        SCREAMING_SNAKE_CASE_					:	Any     							=  ExitStack()

  def __enter__(					self       ):





        """simple docstring"""







        for context_manager in self.context_managers:
              self.stack.enter_context(__lowerCAmelCase       )



  def __exit__(					self			,       *_SCREAMING_SNAKE_CASE			,       **_SCREAMING_SNAKE_CASE       ):





        """simple docstring"""







        self.stack.__exit__(*__lowerCAmelCase			,       **__lowerCAmelCase       )

def  A_	(				a	):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_					:	List[str]     							=  infer_framework(__a	)
      if framework == "tf":
            SCREAMING_SNAKE_CASE_					:	Tuple     							=  inspect.signature(model_class.call	)  # TensorFlow models
      elif framework == "pt":
            SCREAMING_SNAKE_CASE_					:	int     							=  inspect.signature(model_class.forward	)  # PyTorch models
      else:
            SCREAMING_SNAKE_CASE_					:	List[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  A_	(				a	):

      """simple docstring"""



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

      """simple docstring"""



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

      return dict(_flatten_dict(__a       , __a       , __a	)	)

@contextmanager
def  A_	(				a       , a = False	):

      """simple docstring"""



      if use_temp_dir:
            with tempfile.TemporaryDirectory() as tmp_dir:
                  yield tmp_dir
      else:
            yield working_dir

def  A_	(				a       , a=None	):

      """simple docstring"""



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

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

def  A_	(				a       , a	):

      """simple docstring"""



      if is_numpy_array(__a	):
            return np.reshape(__a       , __a	)
      elif is_torch_tensor(__a	):
            return array.reshape(*__a	)
      elif is_tf_tensor(__a	):
            import tensorflow as tf

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

def  A_	(				a       , a=None	):

      """simple docstring"""



      if is_numpy_array(__a	):
            return np.squeeze(__a       , axis=__a	)
      elif is_torch_tensor(__a	):
            return array.squeeze() if axis is None else array.squeeze(dim=__a	)
      elif is_tf_tensor(__a	):
            import tensorflow as tf

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

def  A_	(				a       , a	):

      """simple docstring"""



      if is_numpy_array(__a	):
            return np.expand_dims(__a       , __a	)
      elif is_torch_tensor(__a	):
            return array.unsqueeze(dim=__a	)
      elif is_tf_tensor(__a	):
            import tensorflow as tf

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

def  A_	(				a	):

      """simple docstring"""



      if is_numpy_array(__a	):
            return np.size(__a	)
      elif is_torch_tensor(__a	):
            return array.numel()
      elif is_tf_tensor(__a	):
            import tensorflow as tf

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

def  A_	(				a       , a	):

      """simple docstring"""



      for key, value in auto_map.items():
            if isinstance(__a       , (tuple, list)	):
                  SCREAMING_SNAKE_CASE_					:	Optional[int]     							=  [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:
                  SCREAMING_SNAKE_CASE_					:	Any     							=  f"{repo_id}--{value}"

      return auto_map



def  A_	(				a	):

      """simple docstring"""



      for base_class in inspect.getmro(__a	):
            SCREAMING_SNAKE_CASE_					:	Dict     							=  base_class.__module__
            SCREAMING_SNAKE_CASE_					:	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}."	)













def   __lowerCamelCase   (				__a  :int       )						->							Dict:






		"""simple docstring"""

		A__     =  len(__a       )
		A__     =  sum(__a       )

		A__     =  [[False for x in range(s + 1       )] for y in range(n + 1       )]

		for i in range(1       ,	n + 1       ):
				A__     =  True

		for i in range(1       ,	s + 1       ):
				A__     =  False

		for i in range(1       ,	n + 1       ):
				for j in range(1       ,	s + 1       ):
						A__     =  dp[i][j - 1]

						if arr[i - 1] <= j:
								A__     =  dp[i][j] or dp[i - 1][j - arr[i - 1]]

		for j in range(int(s / 2       )       ,	-1       ,	-1       ):
				if dp[n][j] is True:
						A__     =  s - 2 * j
						break

		return diff



