


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


__magic_name__       					=      logging.get_logger(__name__)

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


__magic_name__       					=      {
    '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
    '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
    '''AutoImageProcessor''': '''ImageProcessingMixin''',
}





class    lowerCAmelCase__				(			__lowerCamelCase       ):






						"""simple docstring"""
						__UpperCAmelCase   :		List[str]  					=	['''feature_extractor''', '''tokenizer''']
						# Names need to be attr_class for attr in attributes
						__UpperCAmelCase   :		List[Any]  					=	None
						__UpperCAmelCase   :		Any  					=	None
						__UpperCAmelCase   :		Union[str, Any]  					=	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_			:							Optional[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():
								lowerCamelCase_			:							List[Any]      =       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_			:							Dict      =       AUTO_TO_BASE_CLASS_MAPPING.get(a_				,							a_     )
								if isinstance(a_				,							a_     ):
									lowerCamelCase_			:							List[Any]      =       tuple(getattr(a_				,							a_     ) for n in class_name if n is not None     )
								else:
									lowerCamelCase_			:							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     ):
							lowerCamelCase_			:							Tuple      =       [F"""- {name}: {repr(getattr(self				,							a_     )     )}""" for name in self.attributes]
							lowerCamelCase_			:							Optional[Any]      =       "\n".join(a_     )
							return F"""{self.__class__.__name__}:\n{attributes_repr}"""




						def 					_UpperCamelCase			(   self				,							a_				,							a_ = False				,							**a_     ):
							os.makedirs(a_				,							exist_ok=a_     )

							if push_to_hub:
								lowerCamelCase_			:							Optional[int]      =       kwargs.pop("commit_message"				,							a_     )
								lowerCamelCase_			:							str      =       kwargs.pop("repo_id"				,							save_directory.split(os.path.sep     )[-1]     )
								lowerCamelCase_			:							Any      =       self._create_repo(a_				,							**a_     )
								lowerCamelCase_			:							Optional[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:
								lowerCamelCase_			:							List[str]      =       [getattr(self				,							a_     ) for attribute_name in self.attributes]
								lowerCamelCase_			:							Optional[int]      =       [(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_			:							Tuple      =       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_			:							List[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				,							a_				,							a_ = None				,							a_ = False				,							a_ = False				,							a_ = None				,							a_ = "main"				,							**a_				,							):
							lowerCamelCase_			:							Any      =       cache_dir
							lowerCamelCase_			:							Dict      =       force_download
							lowerCamelCase_			:							Optional[int]      =       local_files_only
							lowerCamelCase_			:							Optional[Any]      =       revision

							lowerCamelCase_			:							List[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`."     )
								lowerCamelCase_			:							List[str]      =       use_auth_token

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

							lowerCamelCase_			:							Dict      =       cls._get_arguments_from_pretrained(a_				,							**a_     )
							return cls(*a_     )




						@classmethod
						def 					_UpperCamelCase			(   cls				,							a_="AutoProcessor"     ):
							if not isinstance(a_				,							a_     ):
								lowerCamelCase_			:							Any      =       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_			:							List[str]      =       auto_class




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

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




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




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





def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							Union[str, Any]      =       0
	for ele in arr:
		total_sum += product_sum(lowerCAmelCase_      ,					depth + 1) if isinstance(lowerCAmelCase_      ,					lowerCAmelCase_) else ele
	return total_sum * depth







def     __magic_name__  (      lowerCAmelCase_):
	'''simple docstring'''


	return product_sum(lowerCAmelCase_      ,					1)


if __name__ == "__main__":
							import doctest

							doctest.testmod()

