





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	:       Any    	=							logging.getLogger(__name__)







class    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       def __init__(  self			:   Dict							,    A			:   Any							,    A			:   Tuple							,    A			:   List[str]							,    A			:   Dict=None		):
         super().__init__(
             A							,    question_encoder_tokenizer=A							,    generator_tokenizer=A							,    index=A							,    init_retrieval=A							,    )
         __snake_case:						List[str]			  =						None




       def 							UpperCAmelCase__			(  self			:   List[str]							,    A			:   int		):
         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
           __snake_case:						Union[str, Any]			  =						self._infer_socket_ifname()
           # avoid clash with the NCCL port
           __snake_case:						Optional[int]			  =						str(distributed_port + 1		)
           __snake_case:						Tuple			  =						dist.new_group(ranks=A							,    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			:   Optional[Any]		):
         return dist.get_rank(group=self.process_group		) == 0




       def 							UpperCAmelCase__			(  self			:   Tuple							,    A			:   Union[str, Any]							,    A			:   Tuple							,    A			:   int=torch.floataa		):
         __snake_case:						Tuple			  =						torch.empty(A							,    dtype=A		)
         dist.scatter(A							,    src=0							,    scatter_list=A							,    group=self.process_group		)
         return target_tensor




       def 							UpperCAmelCase__			(  self			:   Any		):
         __snake_case:						Union[str, Any]			  =						psutil.net_if_addrs()
         # a hacky way to deal with varying network interface names
         __snake_case:						Optional[Any]			  =						next((addr for addr in addrs if addr.startswith("""e"""		))							,    A		)
         return ifname






       def 							UpperCAmelCase__			(  self			:   Dict							,    A			:   np.ndarray							,    A			:   int		):

         # single GPU training
         if not dist.is_initialized():
           __snake_case  ,       __snake_case:						str			  =						self._main_retrieve(A							,    A		)
           return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(A		)

         # distributed training
         __snake_case:						Union[str, Any]			  =						dist.get_world_size(group=self.process_group		)

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

         # scatter logic
         __snake_case:						List[Any]			  =						question_hidden_states.shape[0]
         __snake_case:						Dict			  =						[]
         __snake_case:						List[str]			  =						[]
         if self._is_main():
           assert len(A		) == world_size
           __snake_case  ,       __snake_case:						Dict			  =						self._main_retrieve(torch.cat(A		).numpy()							,    A		)
           __snake_case  ,       __snake_case:						Optional[Any]			  =						torch.tensor(A		), torch.tensor(A		)
           __snake_case:						List[str]			  =						self._chunk_tensor(A							,    A		)
           __snake_case:						str			  =						self._chunk_tensor(A							,    A		)
         __snake_case:						Optional[int]			  =						self._scattered(A							,    [n_queries, n_docs]							,    target_type=torch.intaa		)
         __snake_case:						Optional[Any]			  =						self._scattered(A							,    [n_queries, n_docs, question_hidden_states.shape[1]]		)

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













import argparse
from collections import defaultdict

import yaml


__UpperCAmelCase	:       int    	=							"docs/source/en/_toctree.yml"







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   Dict:
  __snake_case:						Union[str, Any]			  =						defaultdict(SCREAMING_SNAKE_CASE__)
  for doc in model_doc:
    counts[doc["local"]] += 1
  __snake_case:						Dict			  =						[key for key, value in counts.items() if value > 1]

  __snake_case:						Optional[Any]			  =						[]
  for duplicate_key in duplicates:
    __snake_case:						Tuple			  =						list({doc["""title"""] for doc in model_doc if doc["""local"""] == duplicate_key})
    if len(SCREAMING_SNAKE_CASE__) > 1:
      raise ValueError(
          F'''{duplicate_key} is present several times in the documentation table of content at '''
          """`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
          """others.""")
    # Only add this once
    new_doc.append({"""local""": duplicate_key, """title""": titles[0]})

  # Add none duplicate-keys
  new_doc.extend([doc for doc in model_doc if counts[doc["""local"""]] == 1])

  # Sort
  return sorted(SCREAMING_SNAKE_CASE__ , key=lambda SCREAMING_SNAKE_CASE__: s["title"].lower())







def       A__							(   SCREAMING_SNAKE_CASE__=False)       ->   List[str]:
  with open(SCREAMING_SNAKE_CASE__ , encoding="""utf-8""") as f:
    __snake_case:						Optional[int]			  =						yaml.safe_load(f.read())

  # Get to the API doc
  __snake_case:						Dict			  =						0
  while content[api_idx]["title"] != "API":
    api_idx += 1
  __snake_case:						str			  =						content[api_idx]["""sections"""]

  # Then to the model doc
  __snake_case:						List[Any]			  =						0
  while api_doc[model_idx]["title"] != "Models":
    model_idx += 1

  __snake_case:						Dict			  =						api_doc[model_idx]["""sections"""]

  __snake_case:						int			  =						[(idx, section) for idx, section in enumerate(SCREAMING_SNAKE_CASE__) if """sections""" in section]
  __snake_case:						Optional[int]			  =						False
  for idx, modality_doc in modalities_docs:
    __snake_case:						Dict			  =						modality_doc["""sections"""]
    __snake_case:						List[str]			  =						clean_model_doc_toc(SCREAMING_SNAKE_CASE__)

    if old_modality_doc != new_modality_doc:
      __snake_case:						List[str]			  =						True
      if overwrite:
        __snake_case:						Dict			  =						new_modality_doc

  if diff:
    if overwrite:
      __snake_case:						Dict			  =						model_doc
      __snake_case:						int			  =						api_doc
      with open(SCREAMING_SNAKE_CASE__ , """w""" , encoding="""utf-8""") as f:
        f.write(yaml.dump(SCREAMING_SNAKE_CASE__ , allow_unicode=SCREAMING_SNAKE_CASE__))
    else:
      raise ValueError(
          """The model doc part of the table of content is not properly sorted, run `make style` to fix this.""")


if __name__ == "__main__":
      __UpperCAmelCase	:       Optional[Any]    	=							argparse.ArgumentParser()
      parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
      __UpperCAmelCase	:       str    	=							parser.parse_args()

      check_model_doc(args.fix_and_overwrite)






