




'''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 		lowercase_   :


   """simple docstring"""

   def __init__(   self :     List[Any]  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				question_encoder
      _SCREAMING_SNAKE_CASE	=				generator
      _SCREAMING_SNAKE_CASE	=				self.question_encoder



   def 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      if os.path.isfile(lowercase_   ):
         raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file"""   )
      os.makedirs(lowercase_  ,				exist_ok=lowercase_   )
      _SCREAMING_SNAKE_CASE	=				os.path.join(lowercase_  ,				"question_encoder_tokenizer"   )
      _SCREAMING_SNAKE_CASE	=				os.path.join(lowercase_  ,				"generator_tokenizer"   )
      self.question_encoder.save_pretrained(lowercase_   )
      self.generator.save_pretrained(lowercase_   )



   @classmethod
   def 							lowerCAmelCase_      (   cls :     Optional[Any]  ,				__lowerCamelCase :     Optional[int]  ,				**__lowerCamelCase :     List[str]   ):

      """simple docstring"""
      from ..auto.tokenization_auto import AutoTokenizer

      _SCREAMING_SNAKE_CASE	=				kwargs.pop("config"  ,				lowercase_   )

      if config is None:
         _SCREAMING_SNAKE_CASE	=				RagConfig.from_pretrained(lowercase_   )

      _SCREAMING_SNAKE_CASE	=				AutoTokenizer.from_pretrained(
          lowercase_  ,				config=config.question_encoder  ,				subfolder="question_encoder_tokenizer"   )
      _SCREAMING_SNAKE_CASE	=				AutoTokenizer.from_pretrained(
          lowercase_  ,				config=config.generator  ,				subfolder="generator_tokenizer"   )
      return cls(question_encoder=lowercase_  ,				generator=lowercase_   )



   def __call__(   self :     Any  ,				*__lowerCamelCase :     Union[str, Any]  ,				**__lowerCamelCase :     List[str]   ):

      """simple docstring"""
      return self.current_tokenizer(*lowercase_  ,				**lowercase_   )



   def 							lowerCAmelCase_      (   self :     Optional[Any]  ,				*__lowerCamelCase :     int  ,				**__lowerCamelCase :     Union[str, Any]   ):

      """simple docstring"""
      return self.generator.batch_decode(*lowercase_  ,				**lowercase_   )



   def 							lowerCAmelCase_      (   self :     Optional[Any]  ,				*__lowerCamelCase :     str  ,				**__lowerCamelCase :     str   ):

      """simple docstring"""
      return self.generator.decode(*lowercase_  ,				**lowercase_   )



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.question_encoder



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.generator



   def 							lowerCAmelCase_      (   self :     Optional[Any]  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     List[str] = None  ,				__lowerCamelCase :     Optional[Any] = None  ,				__lowerCamelCase :     Optional[int] = None  ,				__lowerCamelCase :     Union[str, Any] = "longest"  ,				__lowerCamelCase :     Any = None  ,				__lowerCamelCase :     Dict = True  ,				**__lowerCamelCase :     Dict  ,				):

      """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"  ,				lowercase_  ,				)
      if max_length is None:
         _SCREAMING_SNAKE_CASE	=				self.current_tokenizer.model_max_length
      _SCREAMING_SNAKE_CASE	=				self(
          lowercase_  ,				add_special_tokens=lowercase_  ,				return_tensors=lowercase_  ,				max_length=lowercase_  ,				padding=lowercase_  ,				truncation=lowercase_  ,				**lowercase_  ,				)
      if tgt_texts is None:
         return model_inputs
      # Process tgt_texts
      if max_target_length is None:
         _SCREAMING_SNAKE_CASE	=				self.current_tokenizer.model_max_length
      _SCREAMING_SNAKE_CASE	=				self(
          text_target=lowercase_  ,				add_special_tokens=lowercase_  ,				return_tensors=lowercase_  ,				padding=lowercase_  ,				max_length=lowercase_  ,				truncation=lowercase_  ,				**lowercase_  ,				)
      _SCREAMING_SNAKE_CASE	=				labels["input_ids"]
      return model_inputs










'''simple docstring'''



def 			SCREAMING_SNAKE_CASE_			(							__A    :			list  )			->			list:
   if len(__A  ) <= 1:
      return lst

   _SCREAMING_SNAKE_CASE	=				1

   while i < len(__A  ):
      if lst[i - 1] <= lst[i]:
         i += 1
      else:
         _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				lst[i], lst[i - 1]
         i -= 1
         if i == 0:
            _SCREAMING_SNAKE_CASE	=				1

   return lst


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

