





'''simple docstring'''

import argparse
from pathlib import Path

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







def    a__							(							lowercase   :	Dict,   lowercase   :	str,   lowercase   :	str,   lowercase   :	Path,   lowercase   :	str = None,   lowercase   :	str = None,   lowercase   :	str = None,   )						->						str:





     """simple docstring"""

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

     if generator_tokenizer_name_or_path is None:
          _UpperCamelCase							       =			generator_name_or_path

     if question_encoder_tokenizer_name_or_path is None:
          _UpperCamelCase							       =			question_encoder_name_or_path

     _UpperCamelCase							       =			RagTokenForGeneration if model_type == '''rag_token''' else RagSequenceForGeneration

     # Save model.
     _UpperCamelCase							       =			RagConfig.from_pretrained(lowercase )
     _UpperCamelCase							       =			AutoConfig.from_pretrained(lowercase )
     _UpperCamelCase							       =			AutoConfig.from_pretrained(lowercase )

     _UpperCamelCase							       =			gen_config
     _UpperCamelCase							       =			question_encoder_config

     _UpperCamelCase							       =			model_class.from_pretrained_question_encoder_generator(
         lowercase,   lowercase,   config=lowercase )
     rag_model.save_pretrained(lowercase )

     # Sanity check.
     model_class.from_pretrained(lowercase )

     # Save tokenizers.
     _UpperCamelCase							       =			AutoTokenizer.from_pretrained(lowercase )
     gen_tokenizer.save_pretrained(dest_dir / '''generator_tokenizer/''' )
     _UpperCamelCase							       =			AutoTokenizer.from_pretrained(lowercase )
     question_encoder_tokenizer.save_pretrained(dest_dir / '''question_encoder_tokenizer/''' )


if __name__ == "__main__":
   lowercase__		:     Optional[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``'
       ),
   )

   lowercase__		:     Optional[int]			=							parser.parse_args()

   lowercase__		:     Union[str, Any]			=							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,
   )






from collections.abc import Sequence
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Sequence[float]			,a__    :   float       )					->				float:
	return sum(c * (x**i) for i, c in enumerate(a__       )       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Sequence[float]			,a__    :   float       )					->				float:
	__A :			Any		    =			0.0
	for coeff in reversed(a__       ):
		__A :			List[str]		    =			result * x + coeff
	return result


if __name__ == "__main__":
			UpperCAmelCase_							:					List[str]		 =   (0.0, 0.0, 5.0, 9.3, 7.0)
			UpperCAmelCase_							:					str		 =   10.0
			print(evaluate_poly(poly, x))
			print(horner(poly, x))





