


"""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 				SCREAMING_SNAKE_CASE__    :
       """simple docstring"""


       def __init__(						self		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					List[Any]								=							question_encoder
         lowerCAmelCase					:					Optional[Any]								=							generator
         lowerCAmelCase					:					List[str]								=							self.question_encoder

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         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__						)
         lowerCAmelCase					:					Tuple								=							os.path.join(snake_case__		,					"question_encoder_tokenizer"						)
         lowerCAmelCase					:					Optional[int]								=							os.path.join(snake_case__		,					"generator_tokenizer"						)
         self.question_encoder.save_pretrained(snake_case__						)
         self.generator.save_pretrained(snake_case__						)

       @classmethod
       def 	lowercase__							(						cls		,					snake_case__		,					**snake_case__						):






         """simple docstring"""





         from ..auto.tokenization_auto import AutoTokenizer

         lowerCAmelCase					:					List[str]								=							kwargs.pop("config"		,					snake_case__						)

         if config is None:
           lowerCAmelCase					:					str								=							RagConfig.from_pretrained(snake_case__						)

         lowerCAmelCase					:					str								=							AutoTokenizer.from_pretrained(
             snake_case__		,					config=config.question_encoder		,					subfolder="question_encoder_tokenizer"						)
         lowerCAmelCase					:					Dict								=							AutoTokenizer.from_pretrained(
             snake_case__		,					config=config.generator		,					subfolder="generator_tokenizer"						)
         return cls(question_encoder=snake_case__		,					generator=snake_case__						)

       def __call__(						self		,					*snake_case__		,					**snake_case__						):






         """simple docstring"""





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

       def 	lowercase__							(						self		,					*snake_case__		,					**snake_case__						):






         """simple docstring"""





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

       def 	lowercase__							(						self		,					*snake_case__		,					**snake_case__						):






         """simple docstring"""





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

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							self.question_encoder

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					str								=							self.generator







       def 	lowercase__							(						self		,					snake_case__		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = "longest"		,					snake_case__ = None		,					snake_case__ = True		,					**snake_case__		,					):






         """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"		,					snake_case__		,					)
         if max_length is None:
           lowerCAmelCase					:					Dict								=							self.current_tokenizer.model_max_length
         lowerCAmelCase					:					List[Any]								=							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:
           lowerCAmelCase					:					Union[str, Any]								=							self.current_tokenizer.model_max_length
         lowerCAmelCase					:					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__		,					)
         lowerCAmelCase					:					Tuple								=							labels["input_ids"]
         return model_inputs









"""simple docstring"""







def  a__      (    SCREAMING_SNAKE_CASE   :   list[int]    ):


  '''simple docstring'''







  lowerCAmelCase					:					str								=							len(SCREAMING_SNAKE_CASE    )
  for i in range(SCREAMING_SNAKE_CASE    ):
    for j in range(i + 1    ,				SCREAMING_SNAKE_CASE    ):
      if numbers[j] < numbers[i]:
        lowerCAmelCase						,  lowerCAmelCase					:					Any								=							numbers[j], numbers[i]
  return numbers


if __name__ == "__main__":
     lowerCAmelCase__				    =       input('''Enter numbers separated by a comma:\n''').strip()
     lowerCAmelCase__				    =       [int(item) for item in user_input.split(''',''')]
     print(exchange_sort(unsorted))





