
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


lowercase__				:				Dict						=      logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
lowercase__				:				Union[str, Any]						=      direct_transformers_import(Path(__file__).parent)


lowercase__				:				Optional[int]						=      {
    "AutoTokenizer": "PreTrainedTokenizerBase",
    "AutoFeatureExtractor": "FeatureExtractionMixin",
    "AutoImageProcessor": "ImageProcessingMixin",
}






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							_snake_case  		=	['feature_extractor', 'tokenizer']
							# Names need to be attr_class for attr in attributes
							_snake_case  		=	None
							_snake_case  		=	None
							_snake_case  		=	None



							def __init__(	self  ,						*SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )-> str:





													'''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(SCREAMING_SNAKE_CASE_  ,						self.attributes      ):
																			if attribute_name in kwargs:
																									raise TypeError(F"Got multiple values for argument {attribute_name}."      )
																			else:
																									__UpperCamelCase  	=      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():
																			__UpperCamelCase  	=      getattr(self  ,						F"{attribute_name}_class"      )
																			# Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
																			__UpperCamelCase  	=      AUTO_TO_BASE_CLASS_MAPPING.get(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
																			if isinstance(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ):
																									__UpperCamelCase  	=      tuple(getattr(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ) for n in class_name if n is not None      )
																			else:
																									__UpperCamelCase  	=      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      )-> Any:





													'''simple docstring'''






													__UpperCamelCase  	=      [F"- {name}: {repr(getattr(self  ,						SCREAMING_SNAKE_CASE_      )      )}" for name in self.attributes]
													__UpperCamelCase  	=      '''\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[Any]:





													'''simple docstring'''






													os.makedirs(SCREAMING_SNAKE_CASE_  ,						exist_ok=SCREAMING_SNAKE_CASE_      )

													if push_to_hub:
																			__UpperCamelCase  	=      kwargs.pop('''commit_message'''  ,						SCREAMING_SNAKE_CASE_      )
																			__UpperCamelCase  	=      kwargs.pop('''repo_id'''  ,						save_directory.split(os.path.sep      )[-1]      )
																			__UpperCamelCase  	=      self._create_repo(SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )
																			__UpperCamelCase  	=      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:
																			__UpperCamelCase  	=      [getattr(self  ,						SCREAMING_SNAKE_CASE_      ) for attribute_name in self.attributes]
																			__UpperCamelCase  	=      [(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:
																			__UpperCamelCase  	=      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:
																									__UpperCamelCase  	=      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_  ,						)-> Any:





													'''simple docstring'''






													__UpperCamelCase  	=      cache_dir
													__UpperCamelCase  	=      force_download
													__UpperCamelCase  	=      local_files_only
													__UpperCamelCase  	=      revision

													__UpperCamelCase  	=      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`.'''      )
																			__UpperCamelCase  	=      use_auth_token

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

													__UpperCamelCase  	=      cls._get_arguments_from_pretrained(SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )
													return cls(*SCREAMING_SNAKE_CASE_      )



							@classmethod
							def      A__		(	cls  ,						SCREAMING_SNAKE_CASE_="AutoProcessor"      )-> Optional[int]:





													'''simple docstring'''






													if not isinstance(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ):
																			__UpperCamelCase  	=      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."      )

													__UpperCamelCase  	=      auto_class



							@classmethod
							def      A__		(	cls  ,						SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )-> Optional[int]:





													'''simple docstring'''






													__UpperCamelCase  	=      []
													for attribute_name in cls.attributes:
																			__UpperCamelCase  	=      getattr(cls  ,						F"{attribute_name}_class"      )
																			if isinstance(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ):
																									__UpperCamelCase  	=      tuple(getattr(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ) if n is not None else None for n in class_name      )
																									__UpperCamelCase  	=      kwargs.get('''use_fast'''  ,						SCREAMING_SNAKE_CASE_      )
																									if use_fast and classes[1] is not None:
																															__UpperCamelCase  	=      classes[1]
																									else:
																															__UpperCamelCase  	=      classes[0]
																			else:
																									__UpperCamelCase  	=      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      )-> Any:





													'''simple docstring'''






													__UpperCamelCase  	=      getattr(self  ,						self.attributes[0]      )
													return getattr(SCREAMING_SNAKE_CASE_  ,						'''model_input_names'''  ,						SCREAMING_SNAKE_CASE_      )





lowercase__				:				Any						=      copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
				lowercase__				:				str						=      ProcessorMixin.push_to_hub.__doc__.format(
				    object="processor", object_class="AutoProcessor", object_files="processor files"
				)








class 			lowerCamelCase_					:





							def __init__(							self    ):
								a_     			=   [0, 1]





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE    ):
								if (difference := index - (len(self.sequence    ) - 2)) >= 1:
									for _ in range(_SCREAMING_SNAKE_CASE    ):
										self.sequence.append(self.sequence[-1] + self.sequence[-2]    )
								return self.sequence[:index]







def 			__SCREAMING_SNAKE_CASE		(		)     ->	None:



	"""simple docstring"""






	print(
	    """Fibonacci Series Using Dynamic Programming\n"""     ,					"""Enter the index of the Fibonacci number you want to calculate """     ,					"""in the prompt below. (To exit enter exit or Ctrl-C)\n"""     ,					sep=""""""     ,					)
	a_     			=   Fibonacci()

	while True:
		a_     			=   input(""">> """	)
		if prompt in {"exit", "quit"}:
			break

		try:
			a_     			=   int(UpperCamelCase	)
		except ValueError:
			print("""Enter a number or 'exit'"""	)
			continue

		print(fibonacci.get(UpperCamelCase	)	)


if __name__ == "__main__":
			main()