import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


lowerCAmelCase      :				int     			=logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
lowerCAmelCase      :				str     			=direct_transformers_import(Path(__file__).parent)


lowerCAmelCase      :				Union[str, Any]     			={
    "AutoTokenizer": "PreTrainedTokenizerBase",
    "AutoFeatureExtractor": "FeatureExtractionMixin",
    "AutoImageProcessor": "ImageProcessingMixin",
}




class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






      _snake_case     		=							['feature_extractor', 'tokenizer']
      # Names need to be attr_class for attr in attributes
      _snake_case     		=							None
      _snake_case     		=							None
      _snake_case     		=							None



      def __init__(       self  :					Optional[int]     ,	*_UpperCamelCase  :					Optional[int]     ,	**_UpperCamelCase  :					Optional[Any])					->List[str]:


          """simple docstring"""


          for key in kwargs:
              if key not in self.attributes:
                  raise TypeError(F"""Unexpected keyword argument {key}.""")
          for arg, attribute_name in zip(_UpperCamelCase     ,	self.attributes):
              if attribute_name in kwargs:
                  raise TypeError(F"""Got multiple values for argument {attribute_name}.""")
              else:
                  _lowerCamelCase  :		List[Any]			    =    arg

          if len(_UpperCamelCase) != len(self.attributes):
              raise ValueError(
                  F"""This processor requires {len(self.attributes)} arguments: {', '.join(self.attributes)}. Got """
                  F"""{len(_UpperCamelCase)} arguments instead.""")

          # Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
          for attribute_name, arg in kwargs.items():
              _lowerCamelCase  :		Tuple			    =    getattr(self     ,	F"""{attribute_name}_class""")
              # Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
              _lowerCamelCase  :		Tuple			    =    AUTO_TO_BASE_CLASS_MAPPING.get(_UpperCamelCase     ,	_UpperCamelCase)
              if isinstance(_UpperCamelCase     ,	_UpperCamelCase):
                  _lowerCamelCase  :		Optional[int]			    =    tuple(getattr(_UpperCamelCase     ,	_UpperCamelCase) for n in class_name if n is not None)
              else:
                  _lowerCamelCase  :		Optional[int]			    =    getattr(_UpperCamelCase     ,	_UpperCamelCase)

              if not isinstance(_UpperCamelCase     ,	_UpperCamelCase):
                  raise ValueError(
                      F"""Received a {type(_UpperCamelCase).__name__} for argument {attribute_name}, but a {class_name} was expected.""")

              setattr(self     ,	_UpperCamelCase     ,	_UpperCamelCase)



      def __repr__(       self  :					List[Any])					->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase  :		Optional[Any]			    =    [F"""- {name}: {repr(getattr(self     ,	_UpperCamelCase))}""" for name in self.attributes]
          _lowerCamelCase  :		Union[str, Any]			    =    """\n""".join(_UpperCamelCase)
          return F"""{self.__class__.__name__}:\n{attributes_repr}"""



      def 						_SCREAMING_SNAKE_CASE     (       self  :					int     ,	_UpperCamelCase  :					List[str]     ,	_UpperCamelCase  :					bool = False     ,	**_UpperCamelCase  :					Dict)					->str:


          """simple docstring"""


          os.makedirs(_UpperCamelCase     ,	exist_ok=_UpperCamelCase)

          if push_to_hub:
              _lowerCamelCase  :		List[str]			    =    kwargs.pop("""commit_message"""     ,	_UpperCamelCase)
              _lowerCamelCase  :		Optional[Any]			    =    kwargs.pop("""repo_id"""     ,	save_directory.split(os.path.sep)[-1])
              _lowerCamelCase  :		Tuple			    =    self._create_repo(_UpperCamelCase     ,	**_UpperCamelCase)
              _lowerCamelCase  :		Any			    =    self._get_files_timestamps(_UpperCamelCase)
          # If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
          # loaded from the Hub.
          if self._auto_class is not None:
              _lowerCamelCase  :		Any			    =    [getattr(self     ,	_UpperCamelCase) for attribute_name in self.attributes]
              _lowerCamelCase  :		str			    =    [(a.init_kwargs if isinstance(_UpperCamelCase     ,	_UpperCamelCase) else a) for a in attrs]
              custom_object_save(self     ,	_UpperCamelCase     ,	config=_UpperCamelCase)

          for attribute_name in self.attributes:
              _lowerCamelCase  :		Any			    =    getattr(self     ,	_UpperCamelCase)
              # Include the processor class in the attribute config so this processor can then be reloaded with the
              # `AutoProcessor` API.
              if hasattr(_UpperCamelCase     ,	"""_set_processor_class"""):
                  attribute._set_processor_class(self.__class__.__name__)
              attribute.save_pretrained(_UpperCamelCase)

          if self._auto_class is not None:
              # We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
              for attribute_name in self.attributes:
                  _lowerCamelCase  :		Any			    =    getattr(self     ,	_UpperCamelCase)
                  if isinstance(_UpperCamelCase     ,	_UpperCamelCase):
                      del attribute.init_kwargs["auto_map"]

          if push_to_hub:
              self._upload_modified_files(
                  _UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase     ,	commit_message=_UpperCamelCase     ,	token=kwargs.get("""use_auth_token""")     ,	)



      @classmethod
      def 						_SCREAMING_SNAKE_CASE     (       cls  :					Tuple     ,	_UpperCamelCase  :					Union[str, os.PathLike]     ,	_UpperCamelCase  :					Optional[Union[str, os.PathLike]] = None     ,	_UpperCamelCase  :					bool = False     ,	_UpperCamelCase  :					bool = False     ,	_UpperCamelCase  :					Optional[Union[str, bool]] = None     ,	_UpperCamelCase  :					str = "main"     ,	**_UpperCamelCase  :					Optional[Any]     ,	)					->Optional[int]:


          """simple docstring"""


          _lowerCamelCase  :		Tuple			    =    cache_dir
          _lowerCamelCase  :		List[Any]			    =    force_download
          _lowerCamelCase  :		Tuple			    =    local_files_only
          _lowerCamelCase  :		Dict			    =    revision

          _lowerCamelCase  :		List[Any]			    =    kwargs.pop("""use_auth_token"""     ,	_UpperCamelCase)
          if use_auth_token is not None:
              warnings.warn(
                  """The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers."""     ,	_UpperCamelCase)
              if token is not None:
                  raise ValueError(
                      """`token` and `use_auth_token` are both specified. Please set only the argument `token`.""")
              _lowerCamelCase  :		List[str]			    =    use_auth_token

          if token is not None:
              # change to `token` in a follow-up PR
              _lowerCamelCase  :		Any			    =    token

          _lowerCamelCase  :		Dict			    =    cls._get_arguments_from_pretrained(_UpperCamelCase     ,	**_UpperCamelCase)
          return cls(*_UpperCamelCase)



      @classmethod
      def 						_SCREAMING_SNAKE_CASE     (       cls  :					Tuple     ,	_UpperCamelCase  :					List[str]="AutoProcessor")					->List[str]:


          """simple docstring"""


          if not isinstance(_UpperCamelCase     ,	_UpperCamelCase):
              _lowerCamelCase  :		List[str]			    =    auto_class.__name__

          import transformers.models.auto as auto_module

          if not hasattr(_UpperCamelCase     ,	_UpperCamelCase):
              raise ValueError(F"""{auto_class} is not a valid auto class.""")

          _lowerCamelCase  :		List[Any]			    =    auto_class



      @classmethod
      def 						_SCREAMING_SNAKE_CASE     (       cls  :					Optional[int]     ,	_UpperCamelCase  :					Tuple     ,	**_UpperCamelCase  :					Optional[int])					->List[Any]:


          """simple docstring"""


          _lowerCamelCase  :		List[str]			    =    []
          for attribute_name in cls.attributes:
              _lowerCamelCase  :		int			    =    getattr(cls     ,	F"""{attribute_name}_class""")
              if isinstance(_UpperCamelCase     ,	_UpperCamelCase):
                  _lowerCamelCase  :		str			    =    tuple(getattr(_UpperCamelCase     ,	_UpperCamelCase) if n is not None else None for n in class_name)
                  _lowerCamelCase  :		str			    =    kwargs.get("""use_fast"""     ,	_UpperCamelCase)
                  if use_fast and classes[1] is not None:
                      _lowerCamelCase  :		Optional[int]			    =    classes[1]
                  else:
                      _lowerCamelCase  :		Tuple			    =    classes[0]
              else:
                  _lowerCamelCase  :		Tuple			    =    getattr(_UpperCamelCase     ,	_UpperCamelCase)

              args.append(attribute_class.from_pretrained(_UpperCamelCase     ,	**_UpperCamelCase))
          return args




      @property
      def 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any])					->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase  :		int			    =    getattr(self     ,	self.attributes[0])
          return getattr(_UpperCamelCase     ,	"""model_input_names"""     ,	_UpperCamelCase)





lowerCAmelCase      :				Any     			=copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
    lowerCAmelCase      :				Any     			=ProcessorMixin.push_to_hub.__doc__.format(
        object="processor", object_class="AutoProcessor", object_files="processor files"
    )





lowerCAmelCase      :				Union[str, Any]     			=1000







def     A__	(       __A						):







    '''simple docstring'''
    return x * x







def     A__	(       __A ,			__A ,			__A ,			__A = 4						):







    '''simple docstring'''
    assert callable(
        __A						), F"""the function(object) passed should be callable your input : {function}"""
    assert isinstance(__A ,			(float, int)						), F"""a should be float or integer your input : {a}"""
    assert isinstance(function(__A						) ,			(float, int)						), (
        "the function should return integer or float return type of your function, "
        F"""{type(__A						)}"""
    )
    assert isinstance(__A ,			(float, int)						), F"""b should be float or integer your input : {b}"""
    assert (
        isinstance(__A ,			__A						) and precision > 0
    ), F"""precision should be positive integer your input : {precision}"""

    # just applying the formula of simpson for approximate integration written in
    # mentioned article in first comment of this file and above this function

    _lowerCamelCase  :		Optional[Any]			    =    (b - a) / N_STEPS
    _lowerCamelCase  :		str			    =    function(__A						) + function(__A						)

    for i in range(1 ,			__A						):
        _lowerCamelCase  :		str			    =    a + h * i
        result += function(__A						) * (4 if i % 2 else 2)

    result *= h / 3
    return round(__A ,			__A						)


if __name__ == "__main__":
    import doctest

    doctest.testmod()




