
'''simple docstring'''






import argparse
from pathlib import Path

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







def 		A_       (	_lowerCAmelCase   :    Optional[int]				,	_lowerCAmelCase   :    str				,	_lowerCAmelCase   :    str				,	_lowerCAmelCase   :    Path				,	_lowerCAmelCase   :    str = None				,	_lowerCAmelCase   :    str = None				,	_lowerCAmelCase   :    str = None				,	):



      """simple docstring"""




      if config_name_or_path is None:
            _lowerCamelCase				:   Any								=   "facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

      if generator_tokenizer_name_or_path is None:
            _lowerCamelCase				:   Tuple								=   generator_name_or_path

      if question_encoder_tokenizer_name_or_path is None:
            _lowerCamelCase				:   List[str]								=   question_encoder_name_or_path

      _lowerCamelCase				:   Optional[Any]								=   RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration

      # Save model.
      _lowerCamelCase				:   Union[str, Any]								=   RagConfig.from_pretrained(_lowerCAmelCase							)
      _lowerCamelCase				:   List[Any]								=   AutoConfig.from_pretrained(_lowerCAmelCase							)
      _lowerCamelCase				:   Optional[Any]								=   AutoConfig.from_pretrained(_lowerCAmelCase							)

      _lowerCamelCase				:   Tuple								=   gen_config
      _lowerCamelCase				:   Any								=   question_encoder_config

      _lowerCamelCase				:   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.
      _lowerCamelCase				:   str								=   AutoTokenizer.from_pretrained(_lowerCAmelCase							)
      gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"							)
      _lowerCamelCase				:   List[Any]								=   AutoTokenizer.from_pretrained(_lowerCAmelCase							)
      question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"							)


if __name__ == "__main__":
 UpperCAmelCase_       :   int   =       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``'
     ),
 )

 UpperCAmelCase_       :   Tuple   =       parser.parse_args()

 UpperCAmelCase_       :   int   =       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 math







def 		A_       (	_lowerCAmelCase   :    int							):



      """simple docstring"""




      _lowerCamelCase				:   Optional[int]								=   math.loga(math.sqrt(4 * positive_integer + 1							) / 2 + 1 / 2							)

      return exponent == int(_lowerCAmelCase							)







def 		A_       (	_lowerCAmelCase   :    float = 1 / 12345							):



      """simple docstring"""




      _lowerCamelCase				:   Optional[int]								=   0
      _lowerCamelCase				:   int								=   0

      _lowerCamelCase				:   List[str]								=   3
      while True:
            _lowerCamelCase				:   List[Any]								=   (integer**2 - 1) / 4
            # if candidate is an integer, then there is a partition for k
            if partition_candidate == int(_lowerCAmelCase							):
                  _lowerCamelCase				:   Any								=   int(_lowerCAmelCase							)
                  total_partitions += 1
                  if check_partition_perfect(_lowerCAmelCase							):
                        perfect_partitions += 1
            if perfect_partitions > 0:
                  if perfect_partitions / total_partitions < max_proportion:
                        return int(_lowerCAmelCase							)
            integer += 1


if __name__ == "__main__":
 print(f'''{solution() = }''')