
'''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


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


class 		__UpperCAmelCase   (      _lowerCamelCase		):


							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 				lowerCamelCase       ( 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 				lowerCamelCase       ( self						):





								"""simple docstring"""






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


							def 				lowerCamelCase       ( 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 				lowerCamelCase       ( 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 				lowerCamelCase       ( 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 torch

from transformers import CamembertForMaskedLM, CamembertTokenizer







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A=5			) ->			Dict:
	# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
	assert masked_input.count('<mask>'			) == 1
	_snake_case     		=	torch.tensor(tokenizer.encode(__A    ,       add_special_tokens=__A			)			).unsqueeze(0			)  # Batch size 1
	_snake_case     		=	model(__A			)[0]  # The last hidden-state is the first element of the output tuple
	_snake_case     		=	(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
	_snake_case     		=	logits[0, masked_index, :]
	_snake_case     		=	logits.softmax(dim=0			)
	_snake_case		,				_snake_case     		=	prob.topk(k=__A    ,       dim=0			)
	_snake_case     		=	' '.join(
	    [tokenizer.convert_ids_to_tokens(indices[i].item()			) for i in range(len(__A			)			)]			)
	_snake_case     		=	tokenizer.mask_token
	_snake_case     		=	[]
	for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' '			)			):
		_snake_case     		=	predicted_token_bpe.replace('\u2581'    ,       ' '			)
		if " {0}".format(__A			) in masked_input:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(' {0}'.format(__A			)    ,       __A			),
			        values[index].item(),
			        predicted_token,
			    )			)
		else:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(__A    ,       __A			),
			        values[index].item(),
			        predicted_token,
			    )			)
	return topk_filled_outputs


lowercase					:							str   =      CamembertTokenizer.from_pretrained("camembert-base")
lowercase					:							str   =      CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

lowercase					:							Tuple   =      "Le camembert est <mask> :)"





print(fill_mask(masked_input, model, tokenizer, topk=3))

