

"""simple docstring"""





from dataclasses import dataclass, field
from typing import Optional

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


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



def     __lowercase					(    )					->					Union[str, Any]:
      __snake_case      :    Union[str, Any]  			=HfArgumentParser((ModelArguments,) )
      ((__snake_case      )			,				      )      :    List[Any]  			=parser.parse_args_into_dataclasses()

      # Load pretrained model and tokenizer

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

      # Use explicit specified decoder config
      if model_args.decoder_config_name:
            __snake_case      :    Optional[int]  			=AutoConfig.from_pretrained(model_args.decoder_config_name )
      # Use pretrained decoder model's config
      else:
            __snake_case      :    Union[str, Any]  			=AutoConfig.from_pretrained(model_args.decoder_model_name_or_path )

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

      __snake_case      :    List[Any]  			=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      :    Union[str, Any]  			=decoder_config.decoder_start_token_id
      __snake_case      :    str  			=decoder_config.pad_token_id
      if decoder_start_token_id is None:
            __snake_case      :    Optional[int]  			=decoder_config.bos_token_id
      if pad_token_id is None:
            __snake_case      :    Optional[int]  			=decoder_config.eos_token_id

      # This is necessary to make Flax's generate() work
      __snake_case      :    Dict  			=decoder_config.eos_token_id
      __snake_case      :    Optional[int]  			=decoder_start_token_id
      __snake_case      :    Any  			=pad_token_id

      __snake_case      :    List[str]  			=AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path )

      __snake_case      :    Dict  			=AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path )
      __snake_case      :    Any  			=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"""





# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


UpperCamelCase_	:				int										= subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
UpperCamelCase_	:				Optional[int]										= (
    subprocess.check_output(F'''git diff --diff-filter=d --name-only {fork_point_sha}'''.split()).decode("""utf-8""").split()
)

UpperCamelCase_	:				List[Any]										= """|""".join(sys.argv[1:])
UpperCamelCase_	:				List[str]										= re.compile(rF'''^({joined_dirs}).*?\.py$''')

UpperCamelCase_	:				str										= [x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")


