


"""simple docstring"""


from math import pow, sqrt


def    _lowerCamelCase   (     *lowerCamelCase__	:							float  ):
 lowercase__       :       Any				=						len(lowerCamelCase__  ) > 0 and all(value > 0.0 for value in values  )
 return result


def    _lowerCamelCase   (     lowerCamelCase__	:							float       ,		lowerCamelCase__	:							float  ):
 return (
     round(sqrt(molar_mass_a / molar_mass_a  )       ,		6  )
     if validate(lowerCamelCase__       ,		lowerCamelCase__  )
     else ValueError("""Input Error: Molar mass values must greater than 0."""  )
 )


def    _lowerCamelCase   (     lowerCamelCase__	:							float       ,		lowerCamelCase__	:							float       ,		lowerCamelCase__	:							float  ):
 return (
     round(effusion_rate * sqrt(molar_mass_a / molar_mass_a  )       ,		6  )
     if validate(lowerCamelCase__       ,		lowerCamelCase__       ,		lowerCamelCase__  )
     else ValueError(
         """Input Error: Molar mass and effusion rate values must greater than 0."""  )
 )


def    _lowerCamelCase   (     lowerCamelCase__	:							float       ,		lowerCamelCase__	:							float       ,		lowerCamelCase__	:							float  ):
 return (
     round(effusion_rate / sqrt(molar_mass_a / molar_mass_a  )       ,		6  )
     if validate(lowerCamelCase__       ,		lowerCamelCase__       ,		lowerCamelCase__  )
     else ValueError(
         """Input Error: Molar mass and effusion rate values must greater than 0."""  )
 )


def    _lowerCamelCase   (     lowerCamelCase__	:							float       ,		lowerCamelCase__	:							float       ,		lowerCamelCase__	:							float  ):
 return (
     round(molar_mass / pow(effusion_rate_a / effusion_rate_a       ,		2  )       ,		6  )
     if validate(lowerCamelCase__       ,		lowerCamelCase__       ,		lowerCamelCase__  )
     else ValueError(
         """Input Error: Molar mass and effusion rate values must greater than 0."""  )
 )






def    _lowerCamelCase   (     lowerCamelCase__	:							float       ,		lowerCamelCase__	:							float       ,		lowerCamelCase__	:							float  ):
 return (
     round(pow(effusion_rate_a / effusion_rate_a       ,		2  ) / molar_mass       ,		6  )
     if validate(lowerCamelCase__       ,		lowerCamelCase__       ,		lowerCamelCase__  )
     else ValueError(
         """Input Error: Molar mass and effusion rate values must greater than 0."""  )
 )



"""simple docstring"""


import argparse
from pathlib import Path

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


def    _lowerCamelCase   (     lowerCamelCase__	:							List[Any]       ,		lowerCamelCase__	:							str       ,		lowerCamelCase__	:							str       ,		lowerCamelCase__	:							Path       ,		lowerCamelCase__	:							str = None       ,		lowerCamelCase__	:							str = None       ,		lowerCamelCase__	:							str = None       ,		):
 if config_name_or_path is None:
  lowercase__       :       Tuple				=						"""facebook/rag-token-base""" if model_type == """rag_token""" else """facebook/rag-sequence-base"""

 if generator_tokenizer_name_or_path is None:
  lowercase__       :       List[Any]				=						generator_name_or_path

 if question_encoder_tokenizer_name_or_path is None:
  lowercase__       :       List[Any]				=						question_encoder_name_or_path

 lowercase__       :       Any				=						RagTokenForGeneration if model_type == """rag_token""" else RagSequenceForGeneration

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

 lowercase__       :       Union[str, Any]				=						gen_config
 lowercase__       :       Any				=						question_encoder_config

 lowercase__       :       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.
 lowercase__       :       Optional[Any]				=						AutoTokenizer.from_pretrained(lowerCamelCase__  )
 gen_tokenizer.save_pretrained(dest_dir / """generator_tokenizer/"""  )
 lowercase__       :       Optional[int]				=						AutoTokenizer.from_pretrained(lowerCamelCase__  )
 question_encoder_tokenizer.save_pretrained(dest_dir / """question_encoder_tokenizer/"""  )


if __name__ == "__main__":
   __snake_case    					=					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``'
       ),
   )

   __snake_case    					=					parser.parse_args()

   __snake_case    					=					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,
   )