'''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


__snake_case   :      Union[str, Any]						    =  logging.get_logger(__name__)

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


__snake_case   :      Optional[Any]						    =  {
    'AutoTokenizer': 'PreTrainedTokenizerBase',
    'AutoFeatureExtractor': 'FeatureExtractionMixin',
    'AutoImageProcessor': 'ImageProcessingMixin',
}




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					Dict    =				['feature_extractor', 'tokenizer']
       # Names need to be attr_class for attr in attributes
       __lowercase					:					int    =				None
       __lowercase					:					Union[str, Any]    =				None
       __lowercase					:					int    =				None

       def __init__(      self    ,     *_SCREAMING_SNAKE_CASE    ,     **_SCREAMING_SNAKE_CASE )		->     Tuple:
           # 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(_SCREAMING_SNAKE_CASE    ,     self.attributes ):
               if attribute_name in kwargs:
                   raise TypeError(F'''Got multiple values for argument {attribute_name}.''' )
               else:
                   A_     =   arg

           if len(_SCREAMING_SNAKE_CASE ) != len(self.attributes ):
               raise ValueError(
                   F'''This processor requires {len(self.attributes )} arguments: {', '.join(self.attributes )}. Got '''
                   F'''{len(_SCREAMING_SNAKE_CASE )} 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_     =   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_     =   AUTO_TO_BASE_CLASS_MAPPING.get(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )
               if isinstance(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE ):
                   A_     =   tuple(getattr(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE ) for n in class_name if n is not None )
               else:
                   A_     =   getattr(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )

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

               setattr(self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )

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

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE = False    ,     **_SCREAMING_SNAKE_CASE )		->     List[str]:
           os.makedirs(_SCREAMING_SNAKE_CASE    ,     exist_ok=_SCREAMING_SNAKE_CASE )

           if push_to_hub:
               A_     =   kwargs.pop('''commit_message'''    ,     _SCREAMING_SNAKE_CASE )
               A_     =   kwargs.pop('''repo_id'''    ,     save_directory.split(os.path.sep )[-1] )
               A_     =   self._create_repo(_SCREAMING_SNAKE_CASE    ,     **_SCREAMING_SNAKE_CASE )
               A_     =   self._get_files_timestamps(_SCREAMING_SNAKE_CASE )
           # 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_     =   [getattr(self    ,     _SCREAMING_SNAKE_CASE ) for attribute_name in self.attributes]
               A_     =   [(a.init_kwargs if isinstance(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE ) else a) for a in attrs]
               custom_object_save(self    ,     _SCREAMING_SNAKE_CASE    ,     config=_SCREAMING_SNAKE_CASE )

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

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

           if push_to_hub:
               self._upload_modified_files(
                   _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     commit_message=_SCREAMING_SNAKE_CASE    ,     token=kwargs.get('''use_auth_token''' )    ,     )

       @classmethod
       def 					__A       (      cls    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE = None    ,     _SCREAMING_SNAKE_CASE = False    ,     _SCREAMING_SNAKE_CASE = False    ,     _SCREAMING_SNAKE_CASE = None    ,     _SCREAMING_SNAKE_CASE = "main"    ,     **_SCREAMING_SNAKE_CASE    ,     )		->     int:
           A_     =   cache_dir
           A_     =   force_download
           A_     =   local_files_only
           A_     =   revision

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

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

           A_     =   cls._get_arguments_from_pretrained(_SCREAMING_SNAKE_CASE    ,     **_SCREAMING_SNAKE_CASE )
           return cls(*_SCREAMING_SNAKE_CASE )

       @classmethod
       def 					__A       (      cls    ,     _SCREAMING_SNAKE_CASE="AutoProcessor" )		->     int:
           if not isinstance(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE ):
               A_     =   auto_class.__name__

           import transformers.models.auto as auto_module

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

           A_     =   auto_class

       @classmethod
       def 					__A       (      cls    ,     _SCREAMING_SNAKE_CASE    ,     **_SCREAMING_SNAKE_CASE )		->     List[str]:
           A_     =   []
           for attribute_name in cls.attributes:
               A_     =   getattr(cls    ,     F'''{attribute_name}_class''' )
               if isinstance(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE ):
                   A_     =   tuple(getattr(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE ) if n is not None else None for n in class_name )
                   A_     =   kwargs.get('''use_fast'''    ,     _SCREAMING_SNAKE_CASE )
                   if use_fast and classes[1] is not None:
                       A_     =   classes[1]
                   else:
                       A_     =   classes[0]
               else:
                   A_     =   getattr(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )

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

       @property
       def 					__A       (      self )		->     Union[str, Any]:
           A_     =   getattr(self    ,     self.attributes[0] )
           return getattr(_SCREAMING_SNAKE_CASE    ,     '''model_input_names'''    ,     _SCREAMING_SNAKE_CASE )
__snake_case   :      List[Any]						    =  copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
  __snake_case   :      Any						    =  ProcessorMixin.push_to_hub.__doc__.format(
      object='processor', object_class='AutoProcessor', object_files='processor files'
  )











from dataclasses import dataclass
from typing import Callable, Optional

import datasets







@dataclass
class 							a_  (		datasets.BuilderConfig				):
						UpperCamelCase__      :						Optional[Callable]				       =None
						UpperCamelCase__      :						Optional[dict]				       =None
						UpperCamelCase__      :						Optional[datasets.Features]				       =None

						def 	__a       (    self	:List[str])		->					List[str]:
												assert self.generator is not None, "generator must be specified"

												if self.gen_kwargs is None:
																		UpperCAmelCase_     							=     {}







class 							a_  (		datasets.GeneratorBasedBuilder				):
						UpperCamelCase__      :						List[str]				       =GeneratorConfig

						def 	__a       (    self	:List[str])		->					List[Any]:
												return datasets.DatasetInfo(features=self.config.features)

						def 	__a       (    self	:Optional[Any]   ,						_lowercase	:List[str])		->					Optional[Any]:
												return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,						gen_kwargs=self.config.gen_kwargs)]




						def 	__a       (    self	:Optional[int]   ,						**_lowercase	:str)		->					Union[str, Any]:
												for idx, ex in enumerate(self.config.generator(**_lowercase)):
																		yield idx, ex





