"""simple docstring"""







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      : Optional[Any]       =logging.get_logger(__name__)

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


lowerCamelCase      : List[str]       ={
    '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
    '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
    '''AutoImageProcessor''': '''ImageProcessingMixin''',
}






class 							__snake_case(					A_	):


    '''simple docstring'''





    _UpperCAmelCase       =			["feature_extractor", "tokenizer"]
    # Names need to be attr_class for attr in attributes
    _UpperCAmelCase       =			None
    _UpperCAmelCase       =			None
    _UpperCAmelCase       =			None
    def __init__(  self					,       *__lowerCamelCase					,       **__lowerCamelCase	):




         '''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(__lowerCamelCase					,       self.attributes	):
              if attribute_name in kwargs:
                   raise TypeError(F'Got multiple values for argument {attribute_name}.'	)
              else:
                   __A					:					Optional[int]								=		arg

         if len(__lowerCamelCase	) != len(self.attributes	):
              raise ValueError(
                  F'This processor requires {len(self.attributes	)} arguments: {", ".join(self.attributes	)}. Got '
                  F'{len(__lowerCamelCase	)} 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					:					Optional[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(__lowerCamelCase					,       __lowerCamelCase	)
              if isinstance(__lowerCamelCase					,       __lowerCamelCase	):
                   __A					:					Tuple								=		tuple(getattr(__lowerCamelCase					,       __lowerCamelCase	) for n in class_name if n is not None	)
              else:
                   __A					:					Union[str, Any]								=		getattr(__lowerCamelCase					,       __lowerCamelCase	)

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

              setattr(self					,       __lowerCamelCase					,       __lowerCamelCase	)
    def __repr__(  self	):




         '''simple docstring'''



         __A					:					List[str]								=		[F'- {name}: {repr(getattr(self					,       __lowerCamelCase	)	)}' for name in self.attributes]
         __A					:					Any								=		'\n'.join(__lowerCamelCase	)
         return F'{self.__class__.__name__}:\n{attributes_repr}'
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase = False					,       **__lowerCamelCase	):




         '''simple docstring'''



         os.makedirs(__lowerCamelCase					,       exist_ok=__lowerCamelCase	)

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

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

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

         if push_to_hub:
              self._upload_modified_files(
                  __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       commit_message=__lowerCamelCase					,       token=kwargs.get('use_auth_token'	)					,       )
    @classmethod
    def 						_a							(  cls					,       __lowerCamelCase					,       __lowerCamelCase = None					,       __lowerCamelCase = False					,       __lowerCamelCase = False					,       __lowerCamelCase = None					,       __lowerCamelCase = "main"					,       **__lowerCamelCase					,       ):




         '''simple docstring'''



         __A					:					List[Any]								=		cache_dir
         __A					:					Optional[Any]								=		force_download
         __A					:					List[str]								=		local_files_only
         __A					:					str								=		revision

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

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

         __A					:					Optional[int]								=		cls._get_arguments_from_pretrained(__lowerCamelCase					,       **__lowerCamelCase	)
         return cls(*__lowerCamelCase	)
    @classmethod
    def 						_a							(  cls					,       __lowerCamelCase="AutoProcessor"	):




         '''simple docstring'''



         if not isinstance(__lowerCamelCase					,       __lowerCamelCase	):
              __A					:					Optional[int]								=		auto_class.__name__

         import transformers.models.auto as auto_module

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

         __A					:					Dict								=		auto_class
    @classmethod
    def 						_a							(  cls					,       __lowerCamelCase					,       **__lowerCamelCase	):




         '''simple docstring'''



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

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

    @property
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Dict								=		getattr(self					,       self.attributes[0]	)
         return getattr(__lowerCamelCase					,       'model_input_names'					,       __lowerCamelCase	)







lowerCamelCase      : Optional[int]       =copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
       lowerCamelCase      : List[Any]       =ProcessorMixin.push_to_hub.__doc__.format(
           object='''processor''', object_class='''AutoProcessor''', object_files='''processor files'''
       )




"""simple docstring"""







def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				list[int]						)    ->     int:







     '''simple docstring'''


     if not isinstance(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE						) or not all(
         isinstance(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE						) and item >= 0 for item in citations						):
          raise ValueError('The citations should be a list of non negative integers.'						)

     citations.sort()
     __A					:					Any								=		len(_SCREAMING_SNAKE_CASE						)

     for i in range(_SCREAMING_SNAKE_CASE						):
          if citations[len_citations - 1 - i] <= i:
               return i

     return len_citations


if __name__ == "__main__":
       import doctest

       doctest.testmod()



