"""simple docstring"""



from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser



@dataclass
class      _A	:




    """simple docstring"""




    UpperCamelCase_	:					str							=					field(
        metadata={'''help''': '''The output directory where the model will be written.'''}							, )
    UpperCamelCase_	:					str							=					field(
        metadata={
            '''help''': (
                '''The encoder model checkpoint for weights initialization.'''
                '''Don\'t set if you want to train an encoder model from scratch.'''
            )
        }							, )
    UpperCamelCase_	:					str							=					field(
        metadata={
            '''help''': (
                '''The decoder model checkpoint for weights initialization.'''
                '''Don\'t set if you want to train a decoder model from scratch.'''
            )
        }							, )
    UpperCamelCase_	:					Optional[str]							=					field(
        default=_lowercase							, metadata={'''help''': '''Pretrained encoder config name or path if not the same as encoder_model_name'''}					)
    UpperCamelCase_	:					Optional[str]							=					field(
        default=_lowercase							, metadata={'''help''': '''Pretrained decoder config name or path if not the same as decoder_model_name'''}					)







def    SCREAMING_SNAKE_CASE       (     ):
     __snake_case				=							HfArgumentParser((ModelArguments,))
     ((__snake_case      )		,	      )				=							parser.parse_args_into_dataclasses()

     # Load pretrained model and tokenizer

     # Use explicit specified encoder config
     if model_args.encoder_config_name:
          __snake_case				=							AutoConfig.from_pretrained(model_args.encoder_config_name)
     # Use pretrained encoder model's config
     else:
          __snake_case				=							AutoConfig.from_pretrained(model_args.encoder_model_name_or_path)

     # Use explicit specified decoder config
     if model_args.decoder_config_name:
          __snake_case				=							AutoConfig.from_pretrained(model_args.decoder_config_name)
     # Use pretrained decoder model's config
     else:
          __snake_case				=							AutoConfig.from_pretrained(model_args.decoder_model_name_or_path)

     # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
     __snake_case				=							True
     __snake_case				=							True

     __snake_case				=							FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
         encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path,      decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path,      encoder_config=snake_case__,      decoder_config=snake_case__,      )

     # GPT2 only has bos/eos tokens but not decoder_start/pad tokens
     __snake_case				=							decoder_config.decoder_start_token_id
     __snake_case				=							decoder_config.pad_token_id
     if decoder_start_token_id is None:
          __snake_case				=							decoder_config.bos_token_id
     if pad_token_id is None:
          __snake_case				=							decoder_config.eos_token_id

     # This is necessary to make Flax's generate() work
     __snake_case				=							decoder_config.eos_token_id
     __snake_case				=							decoder_start_token_id
     __snake_case				=							pad_token_id

     __snake_case				=							AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path)

     __snake_case				=							AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path)
     __snake_case				=							tokenizer.convert_ids_to_tokens(model.config.pad_token_id)

     model.save_pretrained(model_args.output_dir)
     image_processor.save_pretrained(model_args.output_dir)
     tokenizer.save_pretrained(model_args.output_dir)


if __name__ == "__main__":
       main()
"""simple docstring"""



def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							len(snake_case)
     for i in range(length - 1):
          __snake_case				=							i
          for k in range(i + 1,      snake_case):
               if collection[k] < collection[least]:
                    __snake_case				=							k
          if least != i:
               __snake_case		,	__snake_case				=							(collection[i], collection[least])
     return collection


if __name__ == "__main__":
       __lowercase		:		Optional[Any]														=							input("Enter numbers separated by a comma:\n").strip()
       __lowercase		:		Tuple														=							[int(item) for item in user_input.split(",")]
       print(selection_sort(unsorted))