
'''simple docstring'''







import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Tuple				,				__SCREAMING_SNAKE_CASE :			Union[str, Any]				,				__SCREAMING_SNAKE_CASE :			Dict				,				__SCREAMING_SNAKE_CASE :			Optional[int]				,				__SCREAMING_SNAKE_CASE :			Optional[Any] = None				,				__SCREAMING_SNAKE_CASE :			Union[str, Any] = None				,				__SCREAMING_SNAKE_CASE :			Tuple = None				,				):



      """simple docstring"""





      if config_name_or_path is None:
            lowercase_     :					List[Any]													=	'''facebook/rag-token-base''' if model_type == '''rag_token''' else '''facebook/rag-sequence-base'''

      if generator_tokenizer_name_or_path is None:
            lowercase_     :					int													=	generator_name_or_path

      if question_encoder_tokenizer_name_or_path is None:
            lowercase_     :					Any													=	question_encoder_name_or_path

      lowercase_     :					Dict													=	RagTokenForGeneration if model_type == '''rag_token''' else RagSequenceForGeneration

      # Save model.
      lowercase_     :					Dict													=	RagConfig.from_pretrained(_lowerCAmelCase							)
      lowercase_     :					Dict													=	AutoConfig.from_pretrained(_lowerCAmelCase							)
      lowercase_     :					Union[str, Any]													=	AutoConfig.from_pretrained(_lowerCAmelCase							)

      lowercase_     :					Optional[int]													=	gen_config
      lowercase_     :					Dict													=	question_encoder_config

      lowercase_     :					List[Any]													=	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.
      lowercase_     :					Optional[Any]													=	AutoTokenizer.from_pretrained(_lowerCAmelCase							)
      gen_tokenizer.save_pretrained(dest_dir / '''generator_tokenizer/'''							)
      lowercase_     :					Any													=	AutoTokenizer.from_pretrained(_lowerCAmelCase							)
      question_encoder_tokenizer.save_pretrained(dest_dir / '''question_encoder_tokenizer/'''							)


if __name__ == "__main__":
       _lowercase			:  Optional[Any]   							=	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``"
           ),
       )

       _lowercase			:  Union[str, Any]   							=	parser.parse_args()

       _lowercase			:  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 logging
import os

from .state import PartialState






class 			lowerCAmelCase__       (  logging.LoggerAdapter					):






					@staticmethod
					def      _snake_case   (       __SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	PartialState()
											return not main_process_only or (main_process_only and state.is_main_process)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							*__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if PartialState._shared_state == {}:
																	raise RuntimeError(
																	    '''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'''		)
											lowercase_     :					Tuple													=	kwargs.pop('''main_process_only'''    ,							__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Optional[int]													=	kwargs.pop('''in_order'''    ,							__SCREAMING_SNAKE_CASE		)

											if self.isEnabledFor(__SCREAMING_SNAKE_CASE		):
																	if self._should_log(__SCREAMING_SNAKE_CASE		):
																							lowercase_						,			lowercase_     :					Optional[Any]													=	self.process(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)
																							self.logger.log(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							*__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)

																	elif in_order:
																							lowercase_     :					Optional[Any]													=	PartialState()
																							for i in range(state.num_processes		):
																													if i == state.process_index:
																																			lowercase_						,			lowercase_     :					Optional[int]													=	self.process(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)
																																			self.logger.log(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							*__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)
																													state.wait_for_everyone()







def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			str = None							):



						"""simple docstring"""





						if log_level is None:
												lowercase_     :					Any													=	os.environ.get('''ACCELERATE_LOG_LEVEL'''				,				__SCREAMING_SNAKE_CASE							)
						lowercase_     :					List[str]													=	logging.getLogger(__SCREAMING_SNAKE_CASE							)
						if log_level is not None:
												logger.setLevel(log_level.upper()							)
												logger.root.setLevel(log_level.upper()							)
						return MultiProcessAdapter(__SCREAMING_SNAKE_CASE				,				{}							)



