




"""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


SCREAMING_SNAKE_CASE__      						=  logging.getLogger(__name__)



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

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






										"""simple docstring"""

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

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""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
																snake_case								=			self._infer_socket_ifname()
																# avoid clash with the NCCL port
																snake_case								=			str(distributed_port + 1    )
																snake_case								=			dist.new_group(ranks=lowerCAmelCase		,    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    ):






										"""simple docstring"""

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

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase=torch.floataa    ):






										"""simple docstring"""

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

				def 		snake_case (				self    ):






										"""simple docstring"""

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

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

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

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

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

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

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



"""simple docstring"""
import math





def    lowerCAmelCase__    (			_UpperCamelCase       :				int					)					->				bool:





						"""simple docstring"""






						return math.sqrt(_UpperCamelCase					) * math.sqrt(_UpperCamelCase					) == num





def    lowerCAmelCase__    (			_UpperCamelCase       :				int					)					->				bool:





						"""simple docstring"""






						snake_case								=			0
						snake_case								=			n
						while left <= right:
												snake_case								=			(left + right) // 2
												if mid**2 == n:
																		return True
												elif mid**2 > n:
																		snake_case								=			mid - 1
												else:
																		snake_case								=			mid + 1
						return False


if __name__ == "__main__":
					import doctest

					doctest.testmod()


