





from dataclasses import dataclass, field
from typing import Optional

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



@dataclass
class    A__  :
      _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=__snake_case			,  metadata={'help': 'Pretrained encoder config name or path if not the same as encoder_model_name'}     )
      _UpperCAmelCase      :Optional[str]		       =  field(
          default=__snake_case			,  metadata={'help': 'Pretrained decoder config name or path if not the same as decoder_model_name'}     )



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

 # Load pretrained model and tokenizer

 # Use explicit specified encoder config
 if model_args.encoder_config_name:
  UpperCamelCase      :   List[Any]			      =  AutoConfig.from_pretrained(model_args.encoder_config_name	)
 # Use pretrained encoder model's config
 else:
  UpperCamelCase      :   Union[str, Any]			      =  AutoConfig.from_pretrained(model_args.encoder_model_name_or_path	)

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

 # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
 UpperCamelCase      :   Optional[int]			      =  True
 UpperCamelCase      :   Optional[int]			      =  True

 UpperCamelCase      :   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=_lowerCAmelCase				,  decoder_config=_lowerCAmelCase				,  )

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

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

 UpperCamelCase      :   Union[str, Any]			      =  AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path	)

 UpperCamelCase      :   Union[str, Any]			      =  AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path	)
 UpperCamelCase      :   List[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()







import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format



class    A__  :
      _UpperCAmelCase      :Union[str, Any]		       =  None





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   int			      =  self.feature_extraction_class(**self.feat_extract_dict  )
       UpperCamelCase      :   Union[str, Any]			      =  json.loads(feat_extract.to_json_string()  )
       for key, value in self.feat_extract_dict.items():
        self.assertEqual(obj[key]			,				A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   str			      =  self.feature_extraction_class(**self.feat_extract_dict  )

       with tempfile.TemporaryDirectory() as tmpdirname:
        UpperCamelCase      :   List[Any]			      =  os.path.join(A_			,				"feat_extract.json"  )
        feat_extract_first.to_json_file(A_  )
        UpperCamelCase      :   str			      =  self.feature_extraction_class.from_json_file(A_  )

       self.assertEqual(feat_extract_second.to_dict()			,				feat_extract_first.to_dict()  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Union[str, Any]			      =  self.feature_extraction_class(**self.feat_extract_dict  )

       with tempfile.TemporaryDirectory() as tmpdirname:
        UpperCamelCase      :   int			      =  feat_extract_first.save_pretrained(A_  )[0]
        check_json_file_has_correct_format(A_  )
        UpperCamelCase      :   Optional[Any]			      =  self.feature_extraction_class.from_pretrained(A_  )

       self.assertEqual(feat_extract_second.to_dict()			,				feat_extract_first.to_dict()  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Any			      =  self.feature_extraction_class()
       self.assertIsNotNone(A_  )
