





import os
import warnings
from typing import List, Optional

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


lowerCAmelCase_        =							logging.get_logger(__name__)



class    __lowerCAmelCase			:






			def __init__(self   ,				__magic_name__   ,				__magic_name__       )	->  List[Any]:


							'''simple docstring'''



							snake_case_		:       Optional[int]           =    question_encoder
							snake_case_		:       Optional[int]           =    generator
							snake_case_		:       Optional[Any]           =    self.question_encoder






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Dict:


							'''simple docstring'''



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






			@classmethod
			def      lowerCamelCase    (cls   ,				__magic_name__   ,				**__magic_name__       )	->  Any:


							'''simple docstring'''



							from ..auto.tokenization_auto import AutoTokenizer

							snake_case_		:       List[str]           =    kwargs.pop('''config'''   ,				__magic_name__       )

							if config is None:
											snake_case_		:       int           =    RagConfig.from_pretrained(__magic_name__       )

							snake_case_		:       Dict           =    AutoTokenizer.from_pretrained(
							    __magic_name__   ,				config=config.question_encoder   ,				subfolder='''question_encoder_tokenizer'''       )
							snake_case_		:       Dict           =    AutoTokenizer.from_pretrained(
							    __magic_name__   ,				config=config.generator   ,				subfolder='''generator_tokenizer'''       )
							return cls(question_encoder=__magic_name__   ,				generator=__magic_name__       )






			def __call__(self   ,				*__magic_name__   ,				**__magic_name__       )	->  Tuple:


							'''simple docstring'''



							return self.current_tokenizer(*__magic_name__   ,				**__magic_name__       )






			def      lowerCamelCase    (self   ,				*__magic_name__   ,				**__magic_name__       )	->  Dict:


							'''simple docstring'''



							return self.generator.batch_decode(*__magic_name__   ,				**__magic_name__       )






			def      lowerCamelCase    (self   ,				*__magic_name__   ,				**__magic_name__       )	->  int:


							'''simple docstring'''



							return self.generator.decode(*__magic_name__   ,				**__magic_name__       )






			def      lowerCamelCase    (self       )	->  Union[str, Any]:


							'''simple docstring'''



							snake_case_		:       Any           =    self.question_encoder






			def      lowerCamelCase    (self       )	->  Dict:


							'''simple docstring'''



							snake_case_		:       Dict           =    self.generator






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = "longest"   ,				__magic_name__ = None   ,				__magic_name__ = True   ,				**__magic_name__   ,				)	->  BatchEncoding:


							'''simple docstring'''



							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'''   ,				__magic_name__   ,				)
							if max_length is None:
											snake_case_		:       Dict           =    self.current_tokenizer.model_max_length
							snake_case_		:       List[str]           =    self(
							    __magic_name__   ,				add_special_tokens=__magic_name__   ,				return_tensors=__magic_name__   ,				max_length=__magic_name__   ,				padding=__magic_name__   ,				truncation=__magic_name__   ,				**__magic_name__   ,				)
							if tgt_texts is None:
											return model_inputs
							# Process tgt_texts
							if max_target_length is None:
											snake_case_		:       Optional[int]           =    self.current_tokenizer.model_max_length
							snake_case_		:       Union[str, Any]           =    self(
							    text_target=__magic_name__   ,				add_special_tokens=__magic_name__   ,				return_tensors=__magic_name__   ,				padding=__magic_name__   ,				max_length=__magic_name__   ,				truncation=__magic_name__   ,				**__magic_name__   ,				)
							snake_case_		:       str           =    labels['''input_ids''']
							return model_inputs








"""simple docstring"""
from __future__ import annotations






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	if len(snake_case__       ) == 0:
		return False
	A_		:    Union[str, Any]						     = len(snake_case__       ) // 2
	if a_list[midpoint] == item:
		return True
	if item < a_list[midpoint]:
		return binary_search(a_list[:midpoint] , snake_case__       )
	else:
		return binary_search(a_list[midpoint + 1 :] , snake_case__       )


if __name__ == "__main__":
							_lowerCAmelCase       =				input("Enter numbers separated by comma:\n").strip()
							_lowerCAmelCase       =				[int(item.strip()) for item in user_input.split(",")]
							_lowerCAmelCase       =				int(input("Enter the number to be found in the list:\n").strip())
							_lowerCAmelCase       =				"" if binary_search(sequence, target) else "not "
							print(F'{target} was {not_str}found in {sequence}')

