




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     ,   )					->		int:







  """simple docstring"""





  if config_name_or_path is None:
    lowerCamelCase__:				str        ='facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'

  if generator_tokenizer_name_or_path is None:
    lowerCamelCase__:				int        =generator_name_or_path

  if question_encoder_tokenizer_name_or_path is None:
    lowerCamelCase__:				int        =question_encoder_name_or_path

  lowerCamelCase__:				Tuple        =RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration

  # Save model.
  lowerCamelCase__:				Dict        =RagConfig.from_pretrained(lowerCAmelCase_		)
  lowerCamelCase__:				int        =AutoConfig.from_pretrained(lowerCAmelCase_		)
  lowerCamelCase__:				Union[str, Any]        =AutoConfig.from_pretrained(lowerCAmelCase_		)

  lowerCamelCase__:				Union[str, Any]        =gen_config
  lowerCamelCase__:				Tuple        =question_encoder_config

  lowerCamelCase__:				List[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__:				Optional[Any]        =AutoTokenizer.from_pretrained(lowerCAmelCase_		)
  gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"		)
  lowerCamelCase__:				Any        =AutoTokenizer.from_pretrained(lowerCAmelCase_		)
  question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"		)


if __name__ == "__main__":
     __A     =    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``"
         ),
     )

     __A     =    parser.parse_args()

     __A     =    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,
     )








import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment



@require_huggingface_suite
class        _SCREAMING_SNAKE_CASE						(     unittest.TestCase   ):







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->Tuple:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =inspect.getfile(accelerate.test_utils)
   lowerCamelCase__:				str        =os.path.sep.join(
       mod_file.split(os.path.sep)[:-1] + ["scripts", "external_deps", "test_metrics.py"])

   from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

   lowerCamelCase__:				str        =test_metrics

 @require_cpu
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Any:



   '''simple docstring'''



   debug_launcher(self.test_metrics.main	,			num_processes=1)

 @require_cpu
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->int:



   '''simple docstring'''



   debug_launcher(self.test_metrics.main)

 @require_single_gpu
 def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->int:



   '''simple docstring'''



   self.test_metrics.main()







 @require_multi_gpu
 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->Dict:



   '''simple docstring'''



   print(F"""Found {torch.cuda.device_count()} devices.""")
   lowerCamelCase__:				Optional[Any]        =["torchrun", F"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
   with patch_environment(omp_num_threads=1):
     execute_subprocess_async(UpperCAmelCase_	,			env=os.environ.copy())

