

from dataclasses import dataclass, field
from typing import Optional

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





@dataclass
class  __lowerCAmelCase     :
  _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=SCREAMING_SNAKE_CASE			,			metadata={"""help""": """Pretrained encoder config name or path if not the same as encoder_model_name"""}							)
  _a									=  field(
      default=SCREAMING_SNAKE_CASE			,			metadata={"""help""": """Pretrained decoder config name or path if not the same as decoder_model_name"""}							)
def      a      (			)    ->      List[Any]:





      """simple docstring"""



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

      # Load pretrained model and tokenizer

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

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

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

      _lowercase	       =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=A__      ,   decoder_config=A__      ,   )

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

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

      _lowercase	       =AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path     )

      _lowercase	       =AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path     )
      _lowercase	       =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 __future__ import annotations
def      a      (			A__	:   list[int]     )    ->      int:





      """simple docstring"""



      if not nums:
            return 0
      _lowercase	       =nums[0]
      _lowercase	       =0
      for num in nums[1:]:
            _lowercase       ,       _lowercase	       =(
                max_excluding + num,
                max(A__      ,   A__     ),
            )
      return max(A__      ,   A__     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()


