


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


SCREAMING_SNAKE_CASE__					:     Optional[int]       =     logging.getLogger(__name__)


class 		_SCREAMING_SNAKE_CASE     (						A     ):


     def __init__(  self							,					A_							,					A_							,					A_							,					A_=None    ):
       super().__init__(
           A_							,					question_encoder_tokenizer=A_							,					generator_tokenizer=A_							,					index=A_							,					init_retrieval=A_							,					)
       _UpperCAmelCase		:							int							=     None


     def        __snake_case(  self							,					A_    ):
       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
         _UpperCAmelCase		:							List[str]							=     self._infer_socket_ifname()
         # avoid clash with the NCCL port
         _UpperCAmelCase		:							str							=     str(distributed_port + 1    )
         _UpperCAmelCase		:							List[Any]							=     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        __snake_case(  self    ):
       return dist.get_rank(group=self.process_group    ) == 0


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


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




     def        __snake_case(  self							,					A_							,					A_    ):

       # single GPU training
       if not dist.is_initialized():
         _UpperCAmelCase,_UpperCAmelCase		:							int							=     self._main_retrieve(A_							,					A_    )
         return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(A_    )

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

       # gather logic
       _UpperCAmelCase		:							int							=     None
       if self._is_main():
         _UpperCAmelCase		:							List[Any]							=     [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
       _UpperCAmelCase		:							Optional[int]							=     question_hidden_states.shape[0]
       _UpperCAmelCase		:							Tuple							=     []
       _UpperCAmelCase		:							Optional[int]							=     []
       if self._is_main():
         assert len(A_    ) == world_size
         _UpperCAmelCase,_UpperCAmelCase		:							int							=     self._main_retrieve(torch.cat(A_    ).numpy()							,					A_    )
         _UpperCAmelCase,_UpperCAmelCase		:							Optional[Any]							=     torch.tensor(A_    ), torch.tensor(A_    )
         _UpperCAmelCase		:							List[str]							=     self._chunk_tensor(A_							,					A_    )
         _UpperCAmelCase		:							Union[str, Any]							=     self._chunk_tensor(A_							,					A_    )
       _UpperCAmelCase		:							Union[str, Any]							=     self._scattered(A_							,					[n_queries, n_docs]							,					target_type=torch.intaa    )
       _UpperCAmelCase		:							Tuple							=     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_    )









from random import randint, random




def    a__    (		snake_case__					:							int						,   snake_case__					:							int						,   snake_case__					:							int						,   snake_case__					:							bool = False						,   snake_case__					:							bool = False						,   snake_case__					:							int = 5						,   ):
  _UpperCAmelCase		:							Optional[int]							=     [[-1] * number_of_cells]  # Create a highway without any car
  _UpperCAmelCase		:							Optional[Any]							=     0
  _UpperCAmelCase		:							Dict							=     max(snake_case__						,   0     )
  while i < number_of_cells:
    _UpperCAmelCase		:							int							=     (
        randint(0						,   snake_case__     ) if random_speed else initial_speed
    )  # Place the cars
    i += (
        randint(1						,   max_speed * 2     ) if random_frequency else frequency
    )  # Arbitrary number, may need tuning
  return highway




def    a__    (		snake_case__					:							list						,   snake_case__					:							int     ):
  _UpperCAmelCase		:							List[Any]							=     0
  _UpperCAmelCase		:							int							=     highway_now[car_index + 1 :]
  for cell in range(len(snake_case__     )     ):  # May need a better name for this
    if cells[cell] != -1:  # If the cell is not empty then
      return distance  # we have the distance we wanted
    distance += 1
  # Here if the car is near the end of the highway
  return distance + get_distance(snake_case__						,   -1     )




def    a__    (		snake_case__					:							list						,   snake_case__					:							float						,   snake_case__					:							int     ):
  _UpperCAmelCase		:							Optional[Any]							=     len(snake_case__     )
  # Beforce calculations, the highway is empty
  _UpperCAmelCase		:							Dict							=     [-1] * number_of_cells

  for car_index in range(snake_case__     ):
    if highway_now[car_index] != -1:
      # Add 1 to the current speed of the car and cap the speed
      _UpperCAmelCase		:							Dict							=     min(highway_now[car_index] + 1						,   snake_case__     )
      # Number of empty cell before the next car
      _UpperCAmelCase		:							List[str]							=     get_distance(snake_case__						,   snake_case__     ) - 1
      # We can't have the car causing an accident
      _UpperCAmelCase		:							List[str]							=     min(next_highway[car_index]						,   snake_case__     )
      if random() < probability:
        # Randomly, a driver will slow down
        _UpperCAmelCase		:							Dict							=     max(next_highway[car_index] - 1						,   0     )
  return next_highway




def    a__    (		snake_case__					:							list						,   snake_case__					:							int						,   snake_case__					:							float						,   snake_case__					:							int     ):
  _UpperCAmelCase		:							Union[str, Any]							=     len(highway[0]     )

  for i in range(snake_case__     ):
    _UpperCAmelCase		:							Tuple							=     update(highway[i]						,   snake_case__						,   snake_case__     )
    _UpperCAmelCase		:							int							=     [-1] * number_of_cells

    for car_index in range(snake_case__     ):
      _UpperCAmelCase		:							List[Any]							=     next_speeds_calculated[car_index]
      if speed != -1:
        # Change the position based on the speed (with % to create the loop)
        _UpperCAmelCase		:							Optional[Any]							=     (car_index + speed) % number_of_cells
        # Commit the change of position
        _UpperCAmelCase		:							Optional[Any]							=     speed
    highway.append(snake_case__     )

  return highway


if __name__ == "__main__":
 import doctest

 doctest.testmod()





