


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   	=  logging.get_logger(__name__)

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


__lowerCamelCase   	=  {
    "AutoTokenizer": "PreTrainedTokenizerBase",
    "AutoFeatureExtractor": "FeatureExtractionMixin",
    "AutoImageProcessor": "ImageProcessingMixin",
}


class 	a__						(    _SCREAMING_SNAKE_CASE     ):
       lowerCamelCase__:  Optional[Any]				     =  ["feature_extractor", "tokenizer"]
       # Names need to be attr_class for attr in attributes
       lowerCamelCase__:  Union[str, Any]				     =  None
       lowerCamelCase__:  List[Any]				     =  None
       lowerCamelCase__:  Optional[Any]				     =  None

       def __init__( self   :  Optional[int]   ,				*lowerCamelCase_   :  Optional[int]   ,				**lowerCamelCase_   :  Union[str, Any]     ):
            # Sanitize args and kwargs
            for key in kwargs:
                 if key not in self.attributes:
                      raise TypeError(F'''Unexpected keyword argument {key}.'''     )
            for arg, attribute_name in zip(A_   ,				self.attributes     ):
                 if attribute_name in kwargs:
                      raise TypeError(F'''Got multiple values for argument {attribute_name}.'''     )
                 else:
                      a_     :  List[Any]				      =     arg

            if len(A_     ) != len(self.attributes     ):
                 raise ValueError(
                     F'''This processor requires {len(self.attributes     )} arguments: {", ".join(self.attributes     )}. Got '''
                     F'''{len(A_     )} 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():
                 a_     :  int				      =     getattr(self   ,				F'''{attribute_name}_class'''     )
                 # Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
                 a_     :  str				      =     AUTO_TO_BASE_CLASS_MAPPING.get(A_   ,				A_     )
                 if isinstance(A_   ,				A_     ):
                      a_     :  Optional[int]				      =     tuple(getattr(A_   ,				A_     ) for n in class_name if n is not None     )
                 else:
                      a_     :  Optional[Any]				      =     getattr(A_   ,				A_     )

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

                 setattr(self   ,				A_   ,				A_     )

       def __repr__( self   :  Optional[int]     ):
            a_     :  Dict				      =     [F'''- {name}: {repr(getattr(self   ,				A_     )     )}''' for name in self.attributes]
            a_     :  Union[str, Any]				      =     """\n""".join(A_     )
            return F'''{self.__class__.__name__}:\n{attributes_repr}'''

       def     UpperCAmelCase( self   :  str   ,				lowerCamelCase_   :  List[str]   ,				lowerCamelCase_   :  List[str] = False   ,				**lowerCamelCase_   :  Tuple     ):
            os.makedirs(A_   ,				exist_ok=A_     )

            if push_to_hub:
                 a_     :  Tuple				      =     kwargs.pop("""commit_message"""   ,				A_     )
                 a_     :  List[str]				      =     kwargs.pop("""repo_id"""   ,				save_directory.split(os.path.sep     )[-1]     )
                 a_     :  Optional[int]				      =     self._create_repo(A_   ,				**A_     )
                 a_     :  Union[str, Any]				      =     self._get_files_timestamps(A_     )
            # 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:
                 a_     :  int				      =     [getattr(self   ,				A_     ) for attribute_name in self.attributes]
                 a_     :  Dict				      =     [(a.init_kwargs if isinstance(A_   ,				A_     ) else a) for a in attrs]
                 custom_object_save(self   ,				A_   ,				config=A_     )

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

            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:
                      a_     :  Optional[Any]				      =     getattr(self   ,				A_     )
                      if isinstance(A_   ,				A_     ):
                           del attribute.init_kwargs["auto_map"]

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

       @classmethod
       def     UpperCAmelCase( cls   :  str   ,				lowerCamelCase_   :  Dict   ,				lowerCamelCase_   :  Optional[int] = None   ,				lowerCamelCase_   :  Union[str, Any] = False   ,				lowerCamelCase_   :  Dict = False   ,				lowerCamelCase_   :  str = None   ,				lowerCamelCase_   :  Optional[Any] = "main"   ,				**lowerCamelCase_   :  Optional[Any]   ,				):
            a_     :  List[str]				      =     cache_dir
            a_     :  Union[str, Any]				      =     force_download
            a_     :  Union[str, Any]				      =     local_files_only
            a_     :  Dict				      =     revision

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

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

            a_     :  Optional[int]				      =     cls._get_arguments_from_pretrained(A_   ,				**A_     )
            return cls(*A_     )

       @classmethod
       def     UpperCAmelCase( cls   :  Tuple   ,				lowerCamelCase_   :  Tuple="AutoProcessor"     ):
            if not isinstance(A_   ,				A_     ):
                 a_     :  List[str]				      =     auto_class.__name__

            import transformers.models.auto as auto_module

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

            a_     :  Optional[Any]				      =     auto_class

       @classmethod
       def     UpperCAmelCase( cls   :  Union[str, Any]   ,				lowerCamelCase_   :  Tuple   ,				**lowerCamelCase_   :  Dict     ):
            a_     :  List[Any]				      =     []
            for attribute_name in cls.attributes:
                 a_     :  Any				      =     getattr(cls   ,				F'''{attribute_name}_class'''     )
                 if isinstance(A_   ,				A_     ):
                      a_     :  str				      =     tuple(getattr(A_   ,				A_     ) if n is not None else None for n in class_name     )
                      a_     :  Dict				      =     kwargs.get("""use_fast"""   ,				A_     )
                      if use_fast and classes[1] is not None:
                           a_     :  Any				      =     classes[1]
                      else:
                           a_     :  Optional[int]				      =     classes[0]
                 else:
                      a_     :  Optional[Any]				      =     getattr(A_   ,				A_     )

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

       @property
       def     UpperCAmelCase( self   :  Tuple     ):
            a_     :  Any				      =     getattr(self   ,				self.attributes[0]     )
            return getattr(A_   ,				"""model_input_names"""   ,				A_     )

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





def  A			(   lowercase					)  ->    str:
		'''simple docstring'''

		if not isinstance(lowercase       ,			lowercase					):
				UpperCamelCase            =	(
				    'operation can not be conducted on a object of type '
				    f'''{type(lowercase					).__name__}'''
				)
				raise TypeError(lowercase					)
		UpperCamelCase            =	''
		for _ in range(0       ,			32					):
				bit_string += str(number % 2					)
				UpperCamelCase            =	number >> 1
		return bit_string


def  A			(   lowercase					)  ->    str:
		'''simple docstring'''

		if number < 0:
				raise ValueError('the value of input must be positive'					)
		elif isinstance(lowercase       ,			lowercase					):
				raise TypeError('Input value must be a \'int\' type'					)
		elif isinstance(lowercase       ,			lowercase					):
				raise TypeError('\'<\' not supported between instances of \'str\' and \'int\''					)
		UpperCamelCase            =	0
		# iterator over [1 to 32],since we are dealing with 32 bit integer
		for _ in range(1       ,			33					):
				# left shift the bits by unity
				UpperCamelCase            =	result << 1
				# get the end bit
				UpperCamelCase            =	number % 2
				# right shift the bits by unity
				UpperCamelCase            =	number >> 1
				# add that bit to our ans
				UpperCamelCase            =	result | end_bit
		return get_reverse_bit_string(lowercase					)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
