from dataclasses import dataclass, field
from typing import Optional

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


@dataclass
class      lowerCamelCase    :
  """simple docstring"""







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



def    a__       (						):
      SCREAMING_SNAKE_CASE_      =     HfArgumentParser((ModelArguments,) )
      ((SCREAMING_SNAKE_CASE_)   ,       )      =     parser.parse_args_into_dataclasses()

      # Load pretrained model and tokenizer

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

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

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

      SCREAMING_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=__UpperCamelCase   ,					decoder_config=__UpperCamelCase   ,					)

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

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

      SCREAMING_SNAKE_CASE_      =     AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path )

      SCREAMING_SNAKE_CASE_      =     AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path )
      SCREAMING_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()



from functools import lru_cache


@lru_cache
def    a__       (						__UpperCamelCase ):
      if num < 0:
            raise ValueError("Number should not be negative." )

      return 1 if num in (0, 1) else num * factorial(num - 1 )


if __name__ == "__main__":
      import doctest

      doctest.testmod()


