

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


a		=logging.getLogger(__name__)






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							def __init__(		self		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Tuple=None):
								super().__init__(
								    SCREAMING_SNAKE_CASE__       ,question_encoder_tokenizer=SCREAMING_SNAKE_CASE__       ,generator_tokenizer=SCREAMING_SNAKE_CASE__       ,index=SCREAMING_SNAKE_CASE__       ,init_retrieval=SCREAMING_SNAKE_CASE__       ,)
								__lowerCamelCase    :    Any 	=			None
							def    lowerCAmelCase					(		self		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      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
									__lowerCamelCase    :    Dict 	=			self._infer_socket_ifname()
									# avoid clash with the NCCL port
									__lowerCamelCase    :    Tuple 	=			str(distributed_port + 1)
									__lowerCamelCase    :    Optional[Any] 	=			dist.new_group(ranks=SCREAMING_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    lowerCAmelCase					(		self		:      Dict):
								return dist.get_rank(group=self.process_group) == 0
							def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=torch.floataa):
								__lowerCamelCase    :    List[Any] 	=			torch.empty(SCREAMING_SNAKE_CASE__       ,dtype=SCREAMING_SNAKE_CASE__)
								dist.scatter(SCREAMING_SNAKE_CASE__       ,src=0       ,scatter_list=SCREAMING_SNAKE_CASE__       ,group=self.process_group)
								return target_tensor
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								__lowerCamelCase    :    Dict 	=			psutil.net_if_addrs()
								# a hacky way to deal with varying network interface names
								__lowerCamelCase    :    Any 	=			next((addr for addr in addrs if addr.startswith('e'))       ,SCREAMING_SNAKE_CASE__)
								return ifname


							def    lowerCAmelCase					(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      np.ndarray       ,SCREAMING_SNAKE_CASE__		:      int):

								# single GPU training
								if not dist.is_initialized():
									__lowerCamelCase	,			__lowerCamelCase    :    str 	=			self._main_retrieve(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
									return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(SCREAMING_SNAKE_CASE__)

								# distributed training
								__lowerCamelCase    :    str 	=			dist.get_world_size(group=self.process_group)

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

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

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a		={
    """configuration_mask2former""": [
        """MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Mask2FormerConfig""",
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=["""Mask2FormerImageProcessor"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """Mask2FormerForUniversalSegmentation""",
					    """Mask2FormerModel""",
					    """Mask2FormerPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure)




