

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__								=		logging.getLogger(__name__)







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




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


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


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case=torch.floataa):
			_UpperCamelCase   :		List[Any]			=						torch.empty(__snake_case	,       dtype=__snake_case)
			dist.scatter(__snake_case	,       src=0	,       scatter_list=__snake_case	,       group=self.process_group)
			return target_tensor


		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						psutil.net_if_addrs()
			# a hacky way to deal with varying network interface names
			_UpperCamelCase   :		int			=						next((addr for addr in addrs if addr.startswith('e'))	,       __snake_case)
			return ifname






		def 		A__					(				self	,       __snake_case	,       __snake_case):

			# single GPU training
			if not dist.is_initialized():
				_UpperCamelCase	,		_UpperCamelCase   :		Optional[int]			=						self._main_retrieve(__snake_case	,       __snake_case)
				return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__snake_case)

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

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

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

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







import logging

from transformers.configuration_utils import PretrainedConfig


lowerCAmelCase__								=		logging.getLogger(__name__)







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "masked_bert"


		def __init__(				self	,       __snake_case=3_05_22	,       __snake_case=7_68	,       __snake_case=12	,       __snake_case=12	,       __snake_case=30_72	,       __snake_case="gelu"	,       __snake_case=0.1	,       __snake_case=0.1	,       __snake_case=5_12	,       __snake_case=2	,       __snake_case=0.0_2	,       __snake_case=1e-12	,       __snake_case=0	,       __snake_case="topK"	,       __snake_case="constant"	,       __snake_case=0.0	,       **__snake_case	,       ):
			super().__init__(pad_token_id=__snake_case	,       **__snake_case)

			_UpperCamelCase   :		List[Any]			=						vocab_size
			_UpperCamelCase   :		Union[str, Any]			=						hidden_size
			_UpperCamelCase   :		Optional[int]			=						num_hidden_layers
			_UpperCamelCase   :		Any			=						num_attention_heads
			_UpperCamelCase   :		int			=						hidden_act
			_UpperCamelCase   :		str			=						intermediate_size
			_UpperCamelCase   :		str			=						hidden_dropout_prob
			_UpperCamelCase   :		Any			=						attention_probs_dropout_prob
			_UpperCamelCase   :		Tuple			=						max_position_embeddings
			_UpperCamelCase   :		Dict			=						type_vocab_size
			_UpperCamelCase   :		str			=						initializer_range
			_UpperCamelCase   :		List[Any]			=						layer_norm_eps
			_UpperCamelCase   :		Tuple			=						pruning_method
			_UpperCamelCase   :		Tuple			=						mask_init
			_UpperCamelCase   :		Dict			=						mask_scale




