

"""simple docstring"""



import argparse
from pathlib import Path

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





def  _snake_case     (    UpperCAmelCase_  :					Any					,   UpperCAmelCase_  :					str					,   UpperCAmelCase_  :					str					,   UpperCAmelCase_  :					Path					,   UpperCAmelCase_  :					str = None					,   UpperCAmelCase_  :					str = None					,   UpperCAmelCase_  :					str = None					,   ):
     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__":
 SCREAMING_SNAKE_CASE_					:	Tuple							  =						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``'
     ),
 )

 SCREAMING_SNAKE_CASE_					:	Dict							  =						parser.parse_args()

 SCREAMING_SNAKE_CASE_					:	Any							  =						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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_					:	Any							  =						{
    'configuration_jukebox': [
        'JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'JukeboxConfig',
        'JukeboxPriorConfig',
        'JukeboxVQVAEConfig',
    ],
    'tokenization_jukebox': ['JukeboxTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						[
     'JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST',
     'JukeboxModel',
     'JukeboxPreTrainedModel',
     'JukeboxVQVAE',
     'JukeboxPrior',
 ]

if TYPE_CHECKING:
 from .configuration_jukebox import (
     JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
     JukeboxConfig,
     JukeboxPriorConfig,
     JukeboxVQVAEConfig,
 )
 from .tokenization_jukebox import JukeboxTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_jukebox import (
      JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
      JukeboxModel,
      JukeboxPreTrainedModel,
      JukeboxPrior,
      JukeboxVQVAE,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






