



'''simple docstring'''





from ...processing_utils import ProcessorMixin






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												=["""image_processor""", """feature_extractor"""]
  a_												="""TvltImageProcessor"""
  a_												="""TvltFeatureExtractor"""




  def __init__( self      :	Union[str, Any]			,	_a      :	str			,	_a      :	List[str]   )					->					Optional[int]:
    super().__init__(image_processor=_a			,	feature_extractor=_a   )

    __lowerCamelCase       :       Dict           = image_processor
    __lowerCamelCase       :       Optional[Any]           = feature_extractor




  def __call__( self      :	int			,	_a      :	int=None			,	_a      :	List[str]=None			,	_a      :	Tuple=None			,	_a      :	List[Any]=None			,	_a      :	Union[str, Any]=False			,	_a      :	str=False			,	*_a      :	str			,	**_a      :	Dict			,	)					->					str:

    if images is None and audio is None:
      raise ValueError('You need to specify either an `images` or `audio` input to process.'   )

    __lowerCamelCase       :       Optional[int]           = None
    if images is not None:
      __lowerCamelCase       :       Any           = self.image_processor(_a			,	mask_pixel=_a			,	*_a			,	**_a   )
    if images_mixed is not None:
      __lowerCamelCase       :       List[str]           = self.image_processor(_a			,	is_mixed=_a			,	*_a			,	**_a   )
    if audio is not None:
      __lowerCamelCase       :       Any           = self.feature_extractor(
          _a			,	*_a			,	sampling_rate=_a			,	mask_audio=_a			,	**_a   )

    __lowerCamelCase       :       Dict           = {}
    if audio is not None:
      output_dict.update(_a   )
    if images is not None:
      output_dict.update(_a   )
    if images_mixed_dict is not None:
      output_dict.update(_a   )
    return output_dict




  @property
  def 						_lowercase     ( self      :	Tuple   )					->					str:
    __lowerCamelCase       :       List[str]           = self.image_processor.model_input_names
    __lowerCamelCase       :       Any           = self.feature_extractor.model_input_names
    return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names   )   )











'''simple docstring'''





import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = None       ,_lowerCAmelCase = None       ,_lowerCAmelCase = None       ,)      ->      Optional[int]:
  if config_name_or_path is None:
    __lowerCamelCase       :       List[Any]           = 'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'

  if generator_tokenizer_name_or_path is None:
    __lowerCamelCase       :       Optional[int]           = generator_name_or_path

  if question_encoder_tokenizer_name_or_path is None:
    __lowerCamelCase       :       Tuple           = question_encoder_name_or_path

  __lowerCamelCase       :       Union[str, Any]           = RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration

  # Save model.
  __lowerCamelCase       :       Tuple           = RagConfig.from_pretrained(_lowerCAmelCase       )
  __lowerCamelCase       :       List[Any]           = AutoConfig.from_pretrained(_lowerCAmelCase       )
  __lowerCamelCase       :       Union[str, Any]           = AutoConfig.from_pretrained(_lowerCAmelCase       )

  __lowerCamelCase       :       Tuple           = gen_config
  __lowerCamelCase       :       List[Any]           = question_encoder_config

  __lowerCamelCase       :       str           = model_class.from_pretrained_question_encoder_generator(
      _lowerCAmelCase       ,_lowerCAmelCase       ,config=_lowerCAmelCase       )
  rag_model.save_pretrained(_lowerCAmelCase       )

  # Sanity check.
  model_class.from_pretrained(_lowerCAmelCase       )

  # Save tokenizers.
  __lowerCamelCase       :       List[str]           = AutoTokenizer.from_pretrained(_lowerCAmelCase       )
  gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'       )
  __lowerCamelCase       :       Optional[int]           = AutoTokenizer.from_pretrained(_lowerCAmelCase       )
  question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/'       )


if __name__ == "__main__":
   _UpperCamelCase											=						argparse.ArgumentParser()
   parser.add_argument(
       '--model_type',
       choices=['rag_sequence', 'rag_token'],
       required=True,
       type=str,
       help='RAG model type: rag_sequence, rag_token',
   )
   parser.add_argument('--dest', type=str, required=True, help='Path to the output checkpoint directory.')
   parser.add_argument('--generator_name_or_path', type=str, required=True, help='Generator model identifier')
   parser.add_argument(
       '--question_encoder_name_or_path', type=str, required=True, help='Question encoder model identifier'
   )

   parser.add_argument(
       '--generator_tokenizer_name_or_path',
       type=str,
       help='Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``',
   )
   parser.add_argument(
       '--question_encoder_tokenizer_name_or_path',
       type=str,
       help='Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``',
   )
   parser.add_argument(
       '--config_name_or_path',
       type=str,
       help=(
           'Identifier of the model config to use, if not provided, resolves to a base config for a given'
           ' ``model_type``'
       ),
   )

   _UpperCamelCase											=						parser.parse_args()

   _UpperCamelCase											=						Path(args.dest)
   dest_dir.mkdir(exist_ok=True)

   consolidate(
       args.model_type,
       args.generator_name_or_path,
       args.question_encoder_name_or_path,
       dest_dir,
       args.config_name_or_path,
       args.generator_tokenizer_name_or_path,
       args.question_encoder_tokenizer_name_or_path,
   )






