"""simple docstring"""



import argparse
from pathlib import Path

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



def 					_lowerCamelCase(					a ,  a ,  a ,  a ,  a = None ,  a = None ,  a = 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(a							)
       __a     				=  AutoConfig.from_pretrained(a							)
       __a     				=  AutoConfig.from_pretrained(a							)

       __a     				=  gen_config
       __a     				=  question_encoder_config

       __a     				=  model_class.from_pretrained_question_encoder_generator(
           a ,  a ,  config=a							)
       rag_model.save_pretrained(a							)

       # Sanity check.
       model_class.from_pretrained(a							)

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


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:str			=	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__:List[Any]			=	parser.parse_args()

   SCREAMING_SNAKE_CASE__:str			=	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"""



import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


SCREAMING_SNAKE_CASE__:Union[str, Any]			=	logging.get_logger(__name__)






class 				snake_case__     :
 _snake_case   :			List[str]           =		None



@experimental
def 					_lowerCamelCase(					a ,  a ,  a ,  a ,  a ,  a ,  a							):
       if ParallelBackendConfig.backend_name is None:
              return _map_with_multiprocessing_pool(
                  a ,  a ,  a ,  a ,  a ,  a ,  a							)

       return _map_with_joblib(a ,  a ,  a ,  a ,  a ,  a ,  a							)



def 					_lowerCamelCase(					a ,  a ,  a ,  a ,  a ,  a ,  a							):
       __a     				=  num_proc if num_proc <= len(a							) else len(a							)
       __a     				=  []  # We organize the splits ourselve (contiguous splits)
       for index in range(a							):
              __a     				=  len(a							) // num_proc
              __a     				=  len(a							) % num_proc
              __a     				=  div * index + min(a ,  a							)
              __a     				=  start + div + (1 if index < mod else 0)
              split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)							)

       if len(a							) != sum(len(i[1]							) for i in split_kwds							):
              raise ValueError(
                  F"Error dividing inputs iterable among processes. "
                  F"Total number of objects {len(a							)}, "
                  F"length: {sum(len(i[1]							) for i in split_kwds							)}"							)

       logger.info(
           F"Spawning {num_proc} processes for {len(a							)} objects in slices of {[len(i[1]							) for i in split_kwds]}"							)
       __a				, __a     				=  None, None
       if not disable_tqdm:
              __a				, __a     				=  (RLock(),), tqdm.set_lock
       with Pool(a ,  initargs=a ,  initializer=a							) as pool:
              __a     				=  pool.map(a ,  a							)
       logger.info(F"Finished {num_proc} processes"							)
       __a     				=  [obj for proc_res in mapped for obj in proc_res]
       logger.info(F"Unpacked {len(a							)} objects"							)

       return mapped



def 					_lowerCamelCase(					a ,  a ,  a ,  a ,  a ,  a ,  a							):
       # progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
       # and it requires monkey-patching joblib internal classes which is subject to change
       import joblib

       with joblib.parallel_backend(ParallelBackendConfig.backend_name ,  n_jobs=a							):
              return joblib.Parallel()(
                  joblib.delayed(a							)((function, obj, types, None, True, None)							) for obj in iterable							)




@experimental
@contextlib.contextmanager
def 					_lowerCamelCase(					a							):
       __a     				=  backend_name

       if backend_name == "spark":
              from joblibspark import register_spark

              register_spark()

              # TODO: call create_cache_and_write_probe if "download" in steps
              # TODO: raise NotImplementedError when Dataset.map etc is called

       try:
              yield
       finally:
              __a     				=  None
