


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:	Any      							=					logging.get_logger(__name__)

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


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




class 			lowercase_				(lowercase__      ):
				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					,     *lowercase_					,     **lowercase_)     ->   str:
											# 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(lowercase_					,     self.attributes):
																		if attribute_name in kwargs:
																									raise TypeError(F"""Got multiple values for argument {attribute_name}.""")
																		else:
																									a__     						=arg

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

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

																		setattr(self					,     lowercase_					,     lowercase_)





				def __repr__(							self)     ->   List[str]:
											a__     						=[F"""- {name}: {repr(getattr(self					,     lowercase_))}""" for name in self.attributes]
											a__     						='\n'.join(lowercase_)
											return F"""{self.__class__.__name__}:\n{attributes_repr}"""





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_ = False					,     **lowercase_)     ->   Optional[int]:
											os.makedirs(lowercase_					,     exist_ok=lowercase_)

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

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

											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					,     lowercase_)
																									if isinstance(lowercase_					,     lowercase_):
																																del attribute.init_kwargs["auto_map"]

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





				@classmethod
				def 	__UpperCamelCase						(							cls					,     lowercase_					,     lowercase_ = None					,     lowercase_ = False					,     lowercase_ = False					,     lowercase_ = None					,     lowercase_ = "main"					,     **lowercase_					,     )     ->   str:
											a__     						=cache_dir
											a__     						=force_download
											a__     						=local_files_only
											a__     						=revision

											a__     						=kwargs.pop('use_auth_token'					,     lowercase_)
											if use_auth_token is not None:
																		warnings.warn(
																		    'The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.'					,     lowercase_)
																		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(lowercase_					,     **lowercase_)
											return cls(*lowercase_)





				@classmethod
				def 	__UpperCamelCase						(							cls					,     lowercase_="AutoProcessor")     ->   Any:
											if not isinstance(lowercase_					,     lowercase_):
																		a__     						=auto_class.__name__

											import transformers.models.auto as auto_module

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

											a__     						=auto_class





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

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





				@property
				def 	__UpperCamelCase						(							self)     ->   List[Any]:
											a__     						=getattr(self					,     self.attributes[0])
											return getattr(lowercase_					,     'model_input_names'					,     lowercase_)







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





from __future__ import annotations






def      _lowercase(				__a							:	float | int						,    __a							:	float | int						,    __a							:	float | int						,    ):
							if not all((nth_term, start_term_a, common_ratio_r)    ):
														return []
							a__     						=[]
							a__     						=1
							a__     						=common_ratio_r
							for _ in range(int(__a    )    ):
														if not series:
																					series.append(__a    )
														else:
																					power += 1
																					series.append(float(start_term_a * multiple    )    )
																					a__     						=pow(float(__a    )						,    __a    )
							return series


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	_lowerCAmelCase:	Tuple      							=					float(input('Enter the last number (n term) of the Geometric Series'))
	_lowerCAmelCase:	Optional[int]      							=					float(input('Enter the starting term (a) of the Geometric Series'))
	_lowerCAmelCase:	List[Any]      							=					float(
	    input('Enter the common ratio between two terms (r) of the Geometric Series')
	)
	print('Formula of Geometric Series => a + ar + ar^2 ... +ar^n')
	print(geometric_series(nth_term, start_term_a, common_ratio_r))

