

'''simple docstring'''







import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__ = None,       UpperCAmelCase__ = None,       UpperCAmelCase__ = None,       )			->			List[Any]:
       if config_name_or_path is None:
              A_						=      """facebook/rag-token-base""" if model_type == """rag_token""" else """facebook/rag-sequence-base"""

       if generator_tokenizer_name_or_path is None:
              A_						=      generator_name_or_path

       if question_encoder_tokenizer_name_or_path is None:
              A_						=      question_encoder_name_or_path

       A_						=      RagTokenForGeneration if model_type == """rag_token""" else RagSequenceForGeneration

       # Save model.
       A_						=      RagConfig.from_pretrained(UpperCAmelCase__				)
       A_						=      AutoConfig.from_pretrained(UpperCAmelCase__				)
       A_						=      AutoConfig.from_pretrained(UpperCAmelCase__				)

       A_						=      gen_config
       A_						=      question_encoder_config

       A_						=      model_class.from_pretrained_question_encoder_generator(
           UpperCAmelCase__,       UpperCAmelCase__,       config=UpperCAmelCase__				)
       rag_model.save_pretrained(UpperCAmelCase__				)

       # Sanity check.
       model_class.from_pretrained(UpperCAmelCase__				)

       # Save tokenizers.
       A_						=      AutoTokenizer.from_pretrained(UpperCAmelCase__				)
       gen_tokenizer.save_pretrained(dest_dir / """generator_tokenizer/"""				)
       A_						=      AutoTokenizer.from_pretrained(UpperCAmelCase__				)
       question_encoder_tokenizer.save_pretrained(dest_dir / """question_encoder_tokenizer/"""				)


if __name__ == "__main__":
      __lowerCamelCase		       =    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``'''
          ),
      )

      __lowerCamelCase		       =    parser.parse_args()

      __lowerCamelCase		       =    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,
      )



'''simple docstring'''







from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 					A__      (      _snake_case			):
    lowercase						=		"ClapFeatureExtractor"
    lowercase						=		("RobertaTokenizer", "RobertaTokenizerFast")



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__       )						->      Tuple:





           '''simple docstring'''
           super().__init__(UpperCamelCase__		,				UpperCamelCase__       )



    def __call__(					self		,				UpperCamelCase__=None		,				UpperCamelCase__=None		,				UpperCamelCase__=None		,				**UpperCamelCase__       )						->      Tuple:





           '''simple docstring'''
           A_						=      kwargs.pop("""sampling_rate"""		,				UpperCamelCase__       )

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

           if text is not None:
                  A_						=      self.tokenizer(UpperCamelCase__		,				return_tensors=UpperCamelCase__		,				**UpperCamelCase__       )

           if audios is not None:
                  A_						=      self.feature_extractor(
                      UpperCamelCase__		,				sampling_rate=UpperCamelCase__		,				return_tensors=UpperCamelCase__		,				**UpperCamelCase__       )

           if text is not None and audios is not None:
                  A_						=      audio_features.input_features
                  return encoding
           elif text is not None:
                  return encoding
           else:
                  return BatchEncoding(data=dict(**UpperCamelCase__       )		,				tensor_type=UpperCamelCase__       )



    def 					snake_case_			(					self		,				*UpperCamelCase__		,				**UpperCamelCase__       )						->      Optional[int]:





           '''simple docstring'''
           return self.tokenizer.batch_decode(*UpperCamelCase__		,				**UpperCamelCase__       )



    def 					snake_case_			(					self		,				*UpperCamelCase__		,				**UpperCamelCase__       )						->      str:





           '''simple docstring'''
           return self.tokenizer.decode(*UpperCamelCase__		,				**UpperCamelCase__       )







    @property
    def 					snake_case_			(					self       )						->      int:





           '''simple docstring'''
           A_						=      self.tokenizer.model_input_names
           A_						=      self.feature_extractor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names       )       )
