





import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


UpperCAmelCase						:   Any			  =     logging.get_logger(__name__)





class 						_A:


				"""simple docstring"""


				def __init__(			self     ,					_A     ,					_A     ):
									__A	:						Optional[int]       =				question_encoder
									__A	:						Dict       =				generator
									__A	:						Optional[Any]       =				self.question_encoder



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									if os.path.isfile(_A     ):
														raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file"""     )
									os.makedirs(_A     ,					exist_ok=_A     )
									__A	:						str       =				os.path.join(_A     ,					'question_encoder_tokenizer'     )
									__A	:						List[str]       =				os.path.join(_A     ,					'generator_tokenizer'     )
									self.question_encoder.save_pretrained(_A     )
									self.generator.save_pretrained(_A     )



				@classmethod
				def 					UpperCAmelCase_		(			cls     ,					_A     ,					**_A     ):
									# dynamically import AutoTokenizer
									from ..auto.tokenization_auto import AutoTokenizer

									__A	:						Optional[int]       =				kwargs.pop('config'     ,					_A     )

									if config is None:
														__A	:						int       =				RagConfig.from_pretrained(_A     )

									__A	:						str       =				AutoTokenizer.from_pretrained(
									    _A     ,					config=config.question_encoder     ,					subfolder='question_encoder_tokenizer'     )
									__A	:						Optional[Any]       =				AutoTokenizer.from_pretrained(
									    _A     ,					config=config.generator     ,					subfolder='generator_tokenizer'     )
									return cls(question_encoder=_A     ,					generator=_A     )



				def __call__(			self     ,					*_A     ,					**_A     ):
									return self.current_tokenizer(*_A     ,					**_A     )



				def 					UpperCAmelCase_		(			self     ,					*_A     ,					**_A     ):
									return self.generator.batch_decode(*_A     ,					**_A     )



				def 					UpperCAmelCase_		(			self     ,					*_A     ,					**_A     ):
									return self.generator.decode(*_A     ,					**_A     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Tuple       =				self.question_encoder



				def 					UpperCAmelCase_		(			self     ):
									__A	:						str       =				self.generator





				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A = None     ,					_A = None     ,					_A = None     ,					_A = "longest"     ,					_A = None     ,					_A = True     ,					**_A     ,					):
									warnings.warn(
									    '`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '
									    'regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '
									    'context manager to prepare your targets. See the documentation of your specific tokenizer for more '
									    'details'     ,					_A     ,					)
									if max_length is None:
														__A	:						str       =				self.current_tokenizer.model_max_length
									__A	:						List[str]       =				self(
									    _A     ,					add_special_tokens=_A     ,					return_tensors=_A     ,					max_length=_A     ,					padding=_A     ,					truncation=_A     ,					**_A     ,					)
									if tgt_texts is None:
														return model_inputs
									# Process tgt_texts
									if max_target_length is None:
														__A	:						Optional[int]       =				self.current_tokenizer.model_max_length
									__A	:						Any       =				self(
									    text_target=_A     ,					add_special_tokens=_A     ,					return_tensors=_A     ,					padding=_A     ,					max_length=_A     ,					truncation=_A     ,					**_A     ,					)
									__A	:						Union[str, Any]       =				labels['input_ids']
									return model_inputs











def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      list[int]:
					__A	:						Optional[int]       =				int(a  )

					# Initialize Result
					__A	:						Optional[int]       =				[]

					# Traverse through all denomination
					for denomination in reversed(a  ):
										# Find denominations
										while int(a  ) >= int(a  ):
															total_value -= int(a  )
															answer.append(a  )  # Append the "answers" array

					return answer


# Driver Code
if __name__ == "__main__":
				UpperCAmelCase						:   List[str]			  =     []
				UpperCAmelCase						:   Optional[int]			  =     '''0'''

				if (
				    input('''Do you want to enter your denominations ? (yY/n): ''').strip().lower()
				    == "y"
				):
								UpperCAmelCase						:   List[Any]			  =     int(input('''Enter the number of denominations you want to add: ''').strip())

								for i in range(0, n):
												denominations.append(int(input(F"""Denomination {i}: """).strip()))
								UpperCAmelCase						:   int			  =     input('''Enter the change you want to make in Indian Currency: ''').strip()
				else:
								# All denominations of Indian Currency if user does not enter
								UpperCAmelCase						:   Optional[int]			  =     [1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
								UpperCAmelCase						:   Tuple			  =     input('''Enter the change you want to make: ''').strip()

				if int(value) == 0 or int(value) < 0:
								print('''The total value cannot be zero or negative.''')

				else:
								print(F"""Following is minimal change for {value}: """)
								UpperCAmelCase						:   Optional[int]			  =     find_minimum_change(denominations, value)
								# Print result
								for i in range(len(answer)):
												print(answer[i], end=''' ''')




