





"""simple docstring"""
import logging
import os
from typing import List, Tuple

import numpy as np
import psutil
import torch
import torch.distributed as dist

from transformers import RagRetriever


__UpperCamelCase     :						int						       =  logging.getLogger(__name__)







class       a							(   a__  ):



 def __init__(     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case=None     ):







  """simple docstring"""






  super().__init__(
      _snake_case				,       question_encoder_tokenizer=_snake_case				,       generator_tokenizer=_snake_case				,       index=_snake_case				,       init_retrieval=_snake_case				,       )
  lowerCAmelCase    				= None



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  logger.info('initializing retrieval'     )

  # initializing a separate process group for retrieval as the default
  # nccl backend doesn't support gather/scatter operations while gloo
  # is too slow to replace nccl for the core gpu communication
  if dist.is_initialized():
   logger.info('dist initialized'     )
   # needs to be set manually
   lowerCAmelCase    				= self._infer_socket_ifname()
   # avoid clash with the NCCL port
   lowerCAmelCase    				= str(distributed_port + 1     )
   lowerCAmelCase    				= dist.new_group(ranks=_snake_case				,       backend='gloo'     )

  # initialize retriever only on the main worker
  if not dist.is_initialized() or self._is_main():
   logger.info('dist not initialized / main'     )
   self.index.init_index()

  # all processes wait untill the retriever is initialized by the main process
  if dist.is_initialized():
   torch.distributed.barrier(group=self.process_group     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return dist.get_rank(group=self.process_group     ) == 0



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case=torch.floataa     ):







  """simple docstring"""






  lowerCAmelCase    				= torch.empty(_snake_case				,       dtype=_snake_case     )
  dist.scatter(_snake_case				,       src=0				,       scatter_list=_snake_case				,       group=self.process_group     )
  return target_tensor



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= psutil.net_if_addrs()
  # a hacky way to deal with varying network interface names
  lowerCAmelCase    				= next((addr for addr in addrs if addr.startswith('e'     ))				,       _snake_case     )
  return ifname



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  if not dist.is_initialized():
   lowerCAmelCase				,lowerCAmelCase    				= self._main_retrieve(_snake_case				,       _snake_case     )
   return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_snake_case     )

  # distributed training
  lowerCAmelCase    				= dist.get_world_size(group=self.process_group     )

  # gather logic
  lowerCAmelCase    				= None
  if self._is_main():
   lowerCAmelCase    				= [torch.empty(question_hidden_states.shape				,       dtype=torch.floataa     ) for _ in range(_snake_case     )]
  dist.gather(torch.tensor(_snake_case     )				,       dst=0				,       gather_list=_snake_case				,       group=self.process_group     )

  # scatter logic
  lowerCAmelCase    				= question_hidden_states.shape[0]
  lowerCAmelCase    				= []
  lowerCAmelCase    				= []
  if self._is_main():
   assert len(_snake_case     ) == world_size
   lowerCAmelCase				,lowerCAmelCase    				= self._main_retrieve(torch.cat(_snake_case     ).numpy()				,       _snake_case     )
   lowerCAmelCase				,lowerCAmelCase    				= torch.tensor(_snake_case     ), torch.tensor(_snake_case     )
   lowerCAmelCase    				= self._chunk_tensor(_snake_case				,       _snake_case     )
   lowerCAmelCase    				= self._chunk_tensor(_snake_case				,       _snake_case     )
  lowerCAmelCase    				= self._scattered(_snake_case				,       [n_queries, n_docs]				,       target_type=torch.intaa     )
  lowerCAmelCase    				= self._scattered(_snake_case				,       [n_queries, n_docs, question_hidden_states.shape[1]]     )

  return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(_snake_case     )









"""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,
   )