
'''simple docstring'''

import torch

from transformers import AutoModel






class  A  (						torch.nn.Module     ):





	def __init__(       self      :							str   ,				__magic_name__      :							List[Any]="sayef/fsner-bert-base-uncased"				):


				"""simple docstring"""

				super(__magic_name__   ,				self				).__init__()

				lowerCAmelCase__										=				AutoModel.from_pretrained(__magic_name__   ,				return_dict=__magic_name__				)
				lowerCAmelCase__										=				torch.nn.CosineSimilarity(3   ,				1E-08				)
				lowerCAmelCase__										=				torch.nn.Softmax(dim=1				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]   ,				**__magic_name__      :							Union[str, Any]				):


				"""simple docstring"""

				return self.bert(**__magic_name__				).last_hidden_state





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict   ,				__magic_name__      :							Dict				):


				"""simple docstring"""

				return token_embeddings.sum(2   ,				keepdim=__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]   ,				__magic_name__      :							List[str]   ,				__magic_name__      :							List[str]   ,				__magic_name__      :							Optional[int]=1				):


				"""simple docstring"""

				return self.softmax(T * self.cos(__magic_name__   ,				__magic_name__				)				)







	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[int]   ,				__magic_name__      :							int   ,				__magic_name__      :							Tuple				):


				"""simple docstring"""

				lowerCAmelCase__										=				W_supports["sizes"].tolist()
				lowerCAmelCase__										=				W_supports["start_token_id"].item()
				lowerCAmelCase__										=				W_supports["end_token_id"].item()

				del W_supports["sizes"]
				del W_supports["start_token_id"]
				del W_supports["end_token_id"]

				lowerCAmelCase__										=				self.BERT(**__magic_name__				)
				lowerCAmelCase__										=				self.BERT(**__magic_name__				)

				lowerCAmelCase__										=				None
				lowerCAmelCase__										=				None

				lowerCAmelCase__										=				W_supports["input_ids"] == start_token_id
				lowerCAmelCase__										=				W_supports["input_ids"] == end_token_id

				for i, size in enumerate(__magic_name__				):
							if i == 0:
										lowerCAmelCase__										=				0
							else:
										lowerCAmelCase__										=				support_sizes[i - 1]

							lowerCAmelCase__										=				S[s : s + size][start_token_masks[s : s + size]]
							lowerCAmelCase__										=				S[s : s + size][end_token_masks[s : s + size]]

							lowerCAmelCase__										=				torch.matmul(q[i]   ,				s_start.T				).sum(1				).softmax(0				)
							lowerCAmelCase__										=				torch.matmul(q[i]   ,				s_end.T				).sum(1				).softmax(0				)

							if p_starts is not None:
										lowerCAmelCase__										=				torch.vstack((p_starts, p_start)				)
										lowerCAmelCase__										=				torch.vstack((p_ends, p_end)				)
							else:
										lowerCAmelCase__										=				p_start
										lowerCAmelCase__										=				p_end

				return p_starts, p_ends









"""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,
   )