






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(			_a   ):





       """simple docstring"""







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


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







def     snake_case   (UpperCAmelCase__    )			->							int:
  UpperCamelCase_:   Optional[int]        =							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   (UpperCAmelCase__    )			->							int:


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

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

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

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

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

  return isinstance(UpperCAmelCase__      ,       np.ndarray    )

def     snake_case   (UpperCAmelCase__    )			->							Any:
  return isinstance(UpperCAmelCase__      ,       np.ndarray    )

def     snake_case   (UpperCAmelCase__    )			->							Optional[int]:
  return _is_numpy(UpperCAmelCase__    )

def     snake_case   (UpperCAmelCase__    )			->							Tuple:
  import torch

  return isinstance(UpperCAmelCase__      ,       torch.Tensor    )

def     snake_case   (UpperCAmelCase__    )			->							List[str]:
  return False if not is_torch_available() else _is_torch(UpperCAmelCase__    )

def     snake_case   (UpperCAmelCase__    )			->							Optional[Any]:
  import torch

  return isinstance(UpperCAmelCase__      ,       torch.device    )

def     snake_case   (UpperCAmelCase__    )			->							List[Any]:
  return False if not is_torch_available() else _is_torch_device(UpperCAmelCase__    )

def     snake_case   (UpperCAmelCase__    )			->							Optional[Any]:
  import torch

  if isinstance(UpperCAmelCase__      ,       UpperCAmelCase__    ):
    if hasattr(UpperCAmelCase__      ,       UpperCAmelCase__    ):
      UpperCamelCase_:   int        =							getattr(UpperCAmelCase__      ,       UpperCAmelCase__    )
    else:
      return False
  return isinstance(UpperCAmelCase__      ,       torch.dtype    )

def     snake_case   (UpperCAmelCase__    )			->							Tuple:
  return False if not is_torch_available() else _is_torch_dtype(UpperCAmelCase__    )

def     snake_case   (UpperCAmelCase__    )			->							Union[str, Any]:
  import tensorflow as tf

  return isinstance(UpperCAmelCase__      ,       tf.Tensor    )

def     snake_case   (UpperCAmelCase__    )			->							Any:
  return False if not is_tf_available() else _is_tensorflow(UpperCAmelCase__    )

def     snake_case   (UpperCAmelCase__    )			->							Tuple:
  import tensorflow as tf

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

def     snake_case   (UpperCAmelCase__    )			->							int:
  return False if not is_tf_available() else _is_tf_symbolic_tensor(UpperCAmelCase__    )

def     snake_case   (UpperCAmelCase__    )			->							List[str]:
  import jax.numpy as jnp  # noqa: F811

  return isinstance(UpperCAmelCase__      ,       jnp.ndarray    )

def     snake_case   (UpperCAmelCase__    )			->							Union[str, Any]:
  return False if not is_flax_available() else _is_jax(UpperCAmelCase__    )

def     snake_case   (UpperCAmelCase__    )			->							Any:


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

def     snake_case   (UpperCAmelCase__    )			->							List[Any]:


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




class 							_lowerCAmelCase(			_a   ):





       """simple docstring"""







       def      _a       (   self				):
         UpperCamelCase_:   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.'''				)

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





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





       def      _a       (   self    ,     *_lowerCamelCase    ,     **_lowerCamelCase				):
         raise Exception(f'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'''				)





       def      _a       (   self    ,     *_lowerCamelCase    ,     **_lowerCamelCase				):
         raise Exception(f'''You cannot use ``pop`` on a {self.__class__.__name__} instance.'''				)





       def      _a       (   self    ,     *_lowerCamelCase    ,     **_lowerCamelCase				):
         raise Exception(f'''You cannot use ``update`` on a {self.__class__.__name__} instance.'''				)





       def __getitem__(   self    ,     _lowerCamelCase				):


         if isinstance(lowerCAmelCase_    ,     lowerCAmelCase_				):
           UpperCamelCase_:   Optional[Any]        =							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				):
         super().__setitem__(lowerCAmelCase_    ,     lowerCAmelCase_				)
         # Don't call self.__setattr__ to avoid recursion errors
         super().__setattr__(lowerCAmelCase_    ,     lowerCAmelCase_				)





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







class 							_lowerCAmelCase(			_a				,      _a   ):





       """simple docstring"""







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







class 							_lowerCAmelCase(			_a   ):





       """simple docstring"""







       a							:						Tuple										='''longest'''
       a							:						Optional[Any]										='''max_length'''
       a							:						Union[str, Any]										='''do_not_pad'''







class 							_lowerCAmelCase(			_a   ):





       """simple docstring"""







       a							:						Any										='''pt'''
       a							:						Dict										='''tf'''
       a							:						Optional[int]										='''np'''
       a							:						Dict										='''jax'''







class 							_lowerCAmelCase:





       """simple docstring"""







       def __init__(   self    ,     _lowerCamelCase				):
         UpperCamelCase_:   Optional[int]        =							context_managers
         UpperCamelCase_:   Optional[Any]        =							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     snake_case   (UpperCAmelCase__    )			->							Union[str, Any]:
  UpperCamelCase_:   str        =							infer_framework(UpperCAmelCase__    )
  if framework == "tf":
    UpperCamelCase_:   Union[str, Any]        =							inspect.signature(model_class.call    )  # TensorFlow models
  elif framework == "pt":
    UpperCamelCase_:   int        =							inspect.signature(model_class.forward    )  # PyTorch models
  else:
    UpperCamelCase_:   int        =							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   (UpperCAmelCase__    )			->							Optional[int]:
  UpperCamelCase_:   Optional[int]        =							model_class.__name__
  UpperCamelCase_:   Any        =							infer_framework(UpperCAmelCase__    )
  if framework == "tf":
    UpperCamelCase_:   List[str]        =							inspect.signature(model_class.call    )  # TensorFlow models
  elif framework == "pt":
    UpperCamelCase_:   Optional[Any]        =							inspect.signature(model_class.forward    )  # PyTorch models
  else:
    UpperCamelCase_:   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     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__ = ""      ,       UpperCAmelCase__ = "."    )			->							Optional[Any]:


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

  return dict(_flatten_dict(UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )    )

@contextmanager
def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__ = False    )			->							Union[str, Any]:


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

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__=None    )			->							List[str]:


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

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

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							List[Any]:


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

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

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__=None    )			->							Any:


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

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

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							List[str]:


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

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

def     snake_case   (UpperCAmelCase__    )			->							Dict:


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

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

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Optional[int]:


  for key, value in auto_map.items():
    if isinstance(UpperCAmelCase__      ,       (tuple, list)    ):
      UpperCamelCase_:   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:
      UpperCamelCase_:   Tuple        =							F'''{repo_id}--{value}'''

  return auto_map

def     snake_case   (UpperCAmelCase__    )			->							str:


  for base_class in inspect.getmro(UpperCAmelCase__    ):
    UpperCamelCase_:   str        =							base_class.__module__
    UpperCamelCase_:   List[str]        =							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 math import pi, sqrt
def 						snake_case_       (UpperCamelCase			:    float ):





							'''simple docstring'''


							if num <= 0:
														raise ValueError('''math domain error''' )
							if num > 171.5:
														raise OverflowError('''math range error''' )
							elif num - int(UpperCamelCase ) not in (0, 0.5):
														raise NotImplementedError('''num must be an integer or a half-integer''' )
							elif num == 0.5:
														return sqrt(UpperCamelCase )
							else:
														return 1.0 if num == 1 else (num - 1) * gamma(num - 1 )
def 						snake_case_       ():





							'''simple docstring'''


							assert gamma(0.5 ) == sqrt(UpperCamelCase )
							assert gamma(1 ) == 1.0
							assert gamma(2 ) == 1.0


if __name__ == "__main__":
				from doctest import testmod

				testmod()
				_snake_case : Optional[Any]  				=						1.0
				while num:
								_snake_case : Dict  				=						float(input('Gamma of: '))
								print(F'''gamma({num}) = {gamma(num)}''')
								print('\nEnter 0 to exit...')

