


from dataclasses import dataclass, field
from typing import Optional

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






@dataclass
class     UpperCAmelCase__       :



    """simple docstring"""






    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=A_   ,  metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"}		)
    UpperCAmelCase__						:		Optional[str]      =  field(
        default=A_   ,  metadata={"help": "Pretrained decoder config name or path if not the same as decoder_model_name"}		)

def 						_UpperCAmelCase				(			):
     __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=SCREAMING_SNAKE_CASE__			,     decoder_config=SCREAMING_SNAKE_CASE__			,     )

     # 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()









import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Tuple        =						['''image_processor''', '''tokenizer''']
      snake_case__				:     Union[str, Any]        =						'''CLIPImageProcessor'''
      snake_case__				:     Dict        =						('''CLIPTokenizer''', '''CLIPTokenizerFast''')
      def __init__(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=None     ,							SCREAMING_SNAKE_CASE__				:       List[str]=None     ,							**SCREAMING_SNAKE_CASE__				:       int     )		->			Any:
             a_					:		List[Any]							=  None
             if "feature_extractor" in kwargs:
                    warnings.warn(
                        'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                        ' instead.'     ,							SCREAMING_SNAKE_CASE__     ,							)
                    a_					:		Tuple							=  kwargs.pop('feature_extractor'     )

             a_					:		Tuple							=  image_processor if image_processor is not None else feature_extractor
             if image_processor is None:
                    raise ValueError('You need to specify an `image_processor`.'     )
             if tokenizer is None:
                    raise ValueError('You need to specify a `tokenizer`.'     )

             super().__init__(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
      def __call__(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=None     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=None     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=None     ,							**SCREAMING_SNAKE_CASE__				:       str     )		->			Optional[Any]:

             if text is None and images is None:
                    raise ValueError('You have to specify either text or images. Both cannot be none.'     )

             if text is not None:
                    a_					:		List[str]							=  self.tokenizer(SCREAMING_SNAKE_CASE__     ,							return_tensors=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )

             if images is not None:
                    a_					:		Dict							=  self.image_processor(SCREAMING_SNAKE_CASE__     ,							return_tensors=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )

             if text is not None and images is not None:
                    a_					:		Dict							=  image_features.pixel_values
                    return encoding
             elif text is not None:
                    return encoding
             else:
                    return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE__     )     ,							tensor_type=SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     ,							*SCREAMING_SNAKE_CASE__				:       List[Any]     ,							**SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			List[Any]:
             return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							*SCREAMING_SNAKE_CASE__				:       List[Any]     ,							**SCREAMING_SNAKE_CASE__				:       Dict     )		->			Optional[Any]:
             return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			Any:
             a_					:		str							=  self.tokenizer.model_input_names
             a_					:		Tuple							=  self.image_processor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			str:
             warnings.warn(
                 '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'     ,							SCREAMING_SNAKE_CASE__     ,							)
             return self.image_processor_class

      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Tuple:
             warnings.warn(
                 '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'     ,							SCREAMING_SNAKE_CASE__     ,							)
             return self.image_processor


