

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


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



class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			def __init__( self  :     Tuple   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     Dict=None):
									super().__init__(
									    lowerCAmelCase__   ,						question_encoder_tokenizer=lowerCAmelCase__   ,						generator_tokenizer=lowerCAmelCase__   ,						index=lowerCAmelCase__   ,						init_retrieval=lowerCAmelCase__   ,						)
									SCREAMING_SNAKE_CASE_: Any  		=       None



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]   ,						lowerCAmelCase__  :     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
															SCREAMING_SNAKE_CASE_: Dict  		=       self._infer_socket_ifname()
															# avoid clash with the NCCL port
															SCREAMING_SNAKE_CASE_: List[Any]  		=       str(distributed_port + 1)
															SCREAMING_SNAKE_CASE_: int  		=       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        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									return dist.get_rank(group=self.process_group) == 0



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     Union[str, Any]   ,						lowerCAmelCase__  :     Optional[int]   ,						lowerCAmelCase__  :     Optional[int]=torch.floataa):
									SCREAMING_SNAKE_CASE_: str  		=       torch.empty(lowerCAmelCase__   ,						dtype=lowerCAmelCase__)
									dist.scatter(lowerCAmelCase__   ,						src=0   ,						scatter_list=lowerCAmelCase__   ,						group=self.process_group)
									return target_tensor



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: int  		=       psutil.net_if_addrs()
									# a hacky way to deal with varying network interface names
									SCREAMING_SNAKE_CASE_: Any  		=       next((addr for addr in addrs if addr.startswith("e"))   ,						lowerCAmelCase__)
									return ifname




			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     np.ndarray   ,						lowerCAmelCase__  :     int):

									# single GPU training
									if not dist.is_initialized():
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Dict  		=       self._main_retrieve(lowerCAmelCase__   ,						lowerCAmelCase__)
															return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(lowerCAmelCase__)

									# distributed training
									SCREAMING_SNAKE_CASE_: Dict  		=       dist.get_world_size(group=self.process_group)

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









def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase = False    ):

						if not isinstance(_UpperCAmelCase				,	_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: str  		=       f"Expected string as input, found {type(_UpperCAmelCase    )}"
												raise ValueError(_UpperCAmelCase    )
						if not isinstance(_UpperCAmelCase				,	_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       f"Expected boolean as use_pascal parameter, found {type(_UpperCAmelCase    )}"
												raise ValueError(_UpperCAmelCase    )

						SCREAMING_SNAKE_CASE_: Tuple  		=       input_str.split("_"    )

						SCREAMING_SNAKE_CASE_: str  		=       0 if use_pascal else 1

						SCREAMING_SNAKE_CASE_: int  		=       words[start_index:]

						SCREAMING_SNAKE_CASE_: List[str]  		=       [word[0].upper() + word[1:] for word in words_to_capitalize]

						SCREAMING_SNAKE_CASE_: List[Any]  		=       "" if use_pascal else words[0]

						return "".join([initial_word, *capitalized_words]    )


if __name__ == "__main__":
						from doctest import testmod

						testmod()






