

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    :Any      =			logging.getLogger(__name__)

class   _a					(			snake_case_				):




      """simple docstring"""

      def __init__(   self							:				Dict						,			UpperCAmelCase							:				List[Any]						,			UpperCAmelCase							:				Optional[int]						,			UpperCAmelCase							:				Optional[Any]						,			UpperCAmelCase							:				str=None	):
            super().__init__(
                UpperCAmelCase						,			question_encoder_tokenizer=UpperCAmelCase						,			generator_tokenizer=UpperCAmelCase						,			index=UpperCAmelCase						,			init_retrieval=UpperCAmelCase						,			)
            A_						  =				None





      def 		__A	(   self							:				Dict						,			UpperCAmelCase							:				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
                  A_						  =				self._infer_socket_ifname()
                  # avoid clash with the NCCL port
                  A_						  =				str(distributed_port + 1	)
                  A_						  =				dist.new_group(ranks=UpperCAmelCase						,			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							:				List[str]	):
            return dist.get_rank(group=self.process_group	) == 0





      def 		__A	(   self							:				Any						,			UpperCAmelCase							:				List[Any]						,			UpperCAmelCase							:				Union[str, Any]						,			UpperCAmelCase							:				Dict=torch.floataa	):
            A_						  =				torch.empty(UpperCAmelCase						,			dtype=UpperCAmelCase	)
            dist.scatter(UpperCAmelCase						,			src=0						,			scatter_list=UpperCAmelCase						,			group=self.process_group	)
            return target_tensor





      def 		__A	(   self							:				Any	):
            A_						  =				psutil.net_if_addrs()
            # a hacky way to deal with varying network interface names
            A_						  =				next((addr for addr in addrs if addr.startswith("e"	))						,			UpperCAmelCase	)
            return ifname







      def 		__A	(   self							:				Tuple						,			UpperCAmelCase							:				np.ndarray						,			UpperCAmelCase							:				int	):

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

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

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

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

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






import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey


class       __lowerCAmelCase					:


	'''simple docstring'''





	def __init__(						self:						List[str],					lowerCamelCase_:						Optional[Any],					lowerCamelCase_:						int,					lowerCamelCase_:						int			):
			if dst_width < 0 or dst_height < 0:
					raise ValueError('Destination width/height should be > 0'			)

			lowercase__					:      str      =		img
			lowercase__					:      Tuple      =		img.shape[1]
			lowercase__					:      str      =		img.shape[0]
			lowercase__					:      Tuple      =		dst_width
			lowercase__					:      Any      =		dst_height

			lowercase__					:      Tuple      =		self.src_w / self.dst_w
			lowercase__					:      Union[str, Any]      =		self.src_h / self.dst_h

			lowercase__					:      List[Any]      =		(
			    np.ones((self.dst_h, self.dst_w, 3),					np.uinta			) * 255
			)

	def    snake_case__(						self:						Dict			):
			for i in range(self.dst_h			):
					for j in range(self.dst_w			):
							lowercase__					:      Tuple      =		self.img[self.get_y(lowerCamelCase_			)][self.get_x(lowerCamelCase_			)]

	def    snake_case__(						self:						List[str],					lowerCamelCase_:						int			):
			return int(self.ratio_x * x			)





	def    snake_case__(						self:						int,					lowerCamelCase_:						int			):
			return int(self.ratio_y * y			)


if __name__ == "__main__":
			__UpperCamelCase,	__UpperCamelCase:	List[Any] = 8_0_0, 6_0_0
			__UpperCamelCase:	Optional[Any] = imread("""image_data/lena.jpg""", 1)
			__UpperCamelCase:	Union[str, Any] = NearestNeighbour(im, dst_w, dst_h)
			n.process()

			imshow(
			    F'Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}', n.output
			)
			waitKey(0)
			destroyAllWindows()


