





"""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


A_     :      List[str]								=logging.get_logger(__name__)

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


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







class 	__a   (  lowerCAmelCase__		):
     SCREAMING_SNAKE_CASE__ :      Optional[Any]       		=			["feature_extractor", "tokenizer"]
     # Names need to be attr_class for attr in attributes
     SCREAMING_SNAKE_CASE__ :      Any       		=			None
     SCREAMING_SNAKE_CASE__ :      List[Any]       		=			None
     SCREAMING_SNAKE_CASE__ :      Tuple       		=			None



     def __init__(       self					,     *a__					,     **a__   ):
            # 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:
                          _lowerCamelCase    	=					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():
                   _lowerCamelCase    	=					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    	=					AUTO_TO_BASE_CLASS_MAPPING.get(a__					,     a__   )
                   if isinstance(a__					,     a__   ):
                          _lowerCamelCase    	=					tuple(getattr(a__					,     a__   ) for n in class_name if n is not None   )
                   else:
                          _lowerCamelCase    	=					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   ):
            _lowerCamelCase    	=					[F'- {name}: {repr(getattr(self					,     a__   )   )}' for name in self.attributes]
            _lowerCamelCase    	=					'\n'.join(a__   )
            return F'{self.__class__.__name__}:\n{attributes_repr}'



     def 						snake_case_			(       self					,     a__					,     a__ = False					,     **a__   ):
            os.makedirs(a__					,     exist_ok=a__   )

            if push_to_hub:
                   _lowerCamelCase    	=					kwargs.pop('commit_message'					,     a__   )
                   _lowerCamelCase    	=					kwargs.pop('repo_id'					,     save_directory.split(os.path.sep   )[-1]   )
                   _lowerCamelCase    	=					self._create_repo(a__					,     **a__   )
                   _lowerCamelCase    	=					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:
                   _lowerCamelCase    	=					[getattr(self					,     a__   ) for attribute_name in self.attributes]
                   _lowerCamelCase    	=					[(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:
                   _lowerCamelCase    	=					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:
                          _lowerCamelCase    	=					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 						snake_case_			(       cls					,     a__					,     a__ = None					,     a__ = False					,     a__ = False					,     a__ = None					,     a__ = "main"					,     **a__					,     ):
            _lowerCamelCase    	=					cache_dir
            _lowerCamelCase    	=					force_download
            _lowerCamelCase    	=					local_files_only
            _lowerCamelCase    	=					revision

            _lowerCamelCase    	=					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`.'   )
                   _lowerCamelCase    	=					use_auth_token

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

            _lowerCamelCase    	=					cls._get_arguments_from_pretrained(a__					,     **a__   )
            return cls(*a__   )



     @classmethod
     def 						snake_case_			(       cls					,     a__="AutoProcessor"   ):
            if not isinstance(a__					,     a__   ):
                   _lowerCamelCase    	=					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.'   )

            _lowerCamelCase    	=					auto_class



     @classmethod
     def 						snake_case_			(       cls					,     a__					,     **a__   ):
            _lowerCamelCase    	=					[]
            for attribute_name in cls.attributes:
                   _lowerCamelCase    	=					getattr(cls					,     F'{attribute_name}_class'   )
                   if isinstance(a__					,     a__   ):
                          _lowerCamelCase    	=					tuple(getattr(a__					,     a__   ) if n is not None else None for n in class_name   )
                          _lowerCamelCase    	=					kwargs.get('use_fast'					,     a__   )
                          if use_fast and classes[1] is not None:
                                 _lowerCamelCase    	=					classes[1]
                          else:
                                 _lowerCamelCase    	=					classes[0]
                   else:
                          _lowerCamelCase    	=					getattr(a__					,     a__   )

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



     @property
     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					getattr(self					,     self.attributes[0]   )
            return getattr(a__					,     'model_input_names'					,     a__   )
A_     :      Dict								=copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
      A_     :      List[Any]								=ProcessorMixin.push_to_hub.__doc__.format(
          object="""processor""", object_class="""AutoProcessor""", object_files="""processor files"""
      )













"""simple docstring"""


def SCREAMING_SNAKE_CASE_   (       snake_case   :					str		)->		list:
       _lowerCamelCase    	=					[]
       _lowerCamelCase    	=					1

       for i in range(len(snake_case		)		):
              if i + 1 < len(snake_case		) and text[i] == text[i + 1]:
                     count += 1
              else:
                     encoded.append((text[i], count)		)
                     _lowerCamelCase    	=					1

       return encoded





def SCREAMING_SNAKE_CASE_   (       snake_case   :					list		)->		str:
       return "".join(char * length for char, length in encoded		)


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

      testmod(name="""run_length_encode""", verbose=True)
      testmod(name="""run_length_decode""", verbose=True)






