






"""simple docstring"""





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       __A		:

					def __init__(			self  :      Optional[Any]     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      Dict					)			->							Dict:
											__magic_name__:	List[Any]							=       question_encoder
											__magic_name__:	Tuple							=       generator
											__magic_name__:	List[str]							=       self.question_encoder

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      Tuple					)			->							Any:
											if os.path.isfile(__snake_case					):
																	raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'					)
											os.makedirs(__snake_case     ,		exist_ok=__snake_case					)
											__magic_name__:	Union[str, Any]							=       os.path.join(__snake_case     ,		"""question_encoder_tokenizer"""					)
											__magic_name__:	str							=       os.path.join(__snake_case     ,		"""generator_tokenizer"""					)
											self.question_encoder.save_pretrained(__snake_case					)
											self.generator.save_pretrained(__snake_case					)

					@classmethod
					def   lowerCamelCase__   (			cls  :      Tuple     ,		__snake_case  :      Optional[Any]     ,		**__snake_case  :      List[Any]					)			->							str:
											# dynamically import AutoTokenizer
											from ..auto.tokenization_auto import AutoTokenizer

											__magic_name__:	Optional[Any]							=       kwargs.pop("""config"""     ,		__snake_case					)

											if config is None:
																	__magic_name__:	Union[str, Any]							=       RagConfig.from_pretrained(__snake_case					)

											__magic_name__:	Optional[int]							=       AutoTokenizer.from_pretrained(
											    __snake_case     ,		config=config.question_encoder     ,		subfolder="""question_encoder_tokenizer"""					)
											__magic_name__:	Union[str, Any]							=       AutoTokenizer.from_pretrained(
											    __snake_case     ,		config=config.generator     ,		subfolder="""generator_tokenizer"""					)
											return cls(question_encoder=__snake_case     ,		generator=__snake_case					)

					def __call__(			self  :      Dict     ,		*__snake_case  :      Tuple     ,		**__snake_case  :      Optional[Any]					)			->							Tuple:
											return self.current_tokenizer(*__snake_case     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		*__snake_case  :      Tuple     ,		**__snake_case  :      Optional[Any]					)			->							str:
											return self.generator.batch_decode(*__snake_case     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      Optional[int]     ,		*__snake_case  :      Any     ,		**__snake_case  :      Dict					)			->							Optional[Any]:
											return self.generator.decode(*__snake_case     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							Dict:
											__magic_name__:	str							=       self.question_encoder

					def   lowerCamelCase__   (			self  :      Any					)			->							List[str]:
											__magic_name__:	Union[str, Any]							=       self.generator






					def   lowerCamelCase__   (			self  :      str     ,		__snake_case  :      List[str]     ,		__snake_case  :      Optional[List[str]] = None     ,		__snake_case  :      Optional[int] = None     ,		__snake_case  :      Optional[int] = None     ,		__snake_case  :      str = "longest"     ,		__snake_case  :      str = None     ,		__snake_case  :      bool = True     ,		**__snake_case  :      Optional[Any]     ,		)			->							BatchEncoding:
											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"""     ,		__snake_case     ,		)
											if max_length is None:
																	__magic_name__:	List[str]							=       self.current_tokenizer.model_max_length
											__magic_name__:	Dict							=       self(
											    __snake_case     ,		add_special_tokens=__snake_case     ,		return_tensors=__snake_case     ,		max_length=__snake_case     ,		padding=__snake_case     ,		truncation=__snake_case     ,		**__snake_case     ,		)
											if tgt_texts is None:
																	return model_inputs
											# Process tgt_texts
											if max_target_length is None:
																	__magic_name__:	Dict							=       self.current_tokenizer.model_max_length
											__magic_name__:	Union[str, Any]							=       self(
											    text_target=__snake_case     ,		add_special_tokens=__snake_case     ,		return_tensors=__snake_case     ,		padding=__snake_case     ,		max_length=__snake_case     ,		truncation=__snake_case     ,		**__snake_case     ,		)
											__magic_name__:	List[str]							=       labels["""input_ids"""]
											return model_inputs











"""simple docstring"""





from __future__ import annotations



def   a    (     __UpperCAmelCase      :    list  )    ->							list:
						if len(__UpperCAmelCase  ) == 0:
												return []
						__magic_name__,			__magic_name__:	List[str]							=       min(__UpperCAmelCase  ), max(__UpperCAmelCase  )
						__magic_name__:	Optional[int]							=       int(max_value - min_value  ) + 1
						__magic_name__:	list[list]							=       [[] for _ in range(__UpperCAmelCase  )]

						for i in my_list:
												buckets[int(i - min_value  )].append(__UpperCAmelCase  )

						return [v for bucket in buckets for v in sorted(__UpperCAmelCase  )]


if __name__ == "__main__":
							from doctest import testmod

							testmod()
							assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
							assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]



