




"""simple docstring"""







from dataclasses import dataclass, field
from typing import Optional

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

@dataclass
class 				snake_case_			:







   """simple docstring"""




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





def      __snake_case (				):


    """simple docstring"""







    UpperCamelCase              =	HfArgumentParser((ModelArguments,)    )
    ((UpperCamelCase)       ,	)              =	parser.parse_args_into_dataclasses()

    # Load pretrained model and tokenizer

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

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

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

    UpperCamelCase              =	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=_lowercase			,decoder_config=_lowercase			,)

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

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

    UpperCamelCase              =	AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path    )

    UpperCamelCase              =	AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path    )
    UpperCamelCase              =	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 	_snake_case     (							lowercase__ = 1				,       lowercase__ = 1000						):
				_lowerCamelCase	:     Optional[int]       =       1
				_lowerCamelCase	:     List[Any]       =       0

				for divide_by_number in range(lowercase__				,       digit + 1						):
								_lowerCamelCase	:     list[int]       =       []
								_lowerCamelCase	:     Dict       =       numerator
								for _ in range(1				,       digit + 1						):
												if now_divide in has_been_divided:
																if longest_list_length < len(lowercase__						):
																				_lowerCamelCase	:     Any       =       len(lowercase__						)
																				_lowerCamelCase	:     Any       =       divide_by_number
												else:
																has_been_divided.append(lowercase__						)
																_lowerCamelCase	:     int       =       now_divide * 10 % divide_by_number

				return the_digit


# Tests
if __name__ == "__main__":
			import doctest

			doctest.testmod()