




"""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


__A   :  Dict					=					logging.getLogger(__name__)




class       lowerCAmelCase__       (			lowerCAmelCase_    ):


				"""simple docstring"""





				def __init__(     self					:	List[str]    ,   lowercase__					:	Tuple    ,   lowercase__					:	Optional[int]    ,   lowercase__					:	List[Any]    ,   lowercase__					:	Tuple=None						):
						super().__init__(
						    lowercase__    ,   question_encoder_tokenizer=lowercase__    ,   generator_tokenizer=lowercase__    ,   index=lowercase__    ,   init_retrieval=lowercase__    ,   )
						__lowercase				:		Optional[int]												=				None



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



				def       snake_case    (     self					:	Union[str, Any]    ,   lowercase__					:	Tuple    ,   lowercase__					:	Dict    ,   lowercase__					:	Any=torch.floataa						):
						__lowercase				:		str												=				torch.empty(lowercase__    ,   dtype=lowercase__						)
						dist.scatter(lowercase__    ,   src=0    ,   scatter_list=lowercase__    ,   group=self.process_group						)
						return target_tensor



				def       snake_case    (     self					:	int						):
						__lowercase				:		Union[str, Any]												=				psutil.net_if_addrs()
						# a hacky way to deal with varying network interface names
						__lowercase				:		Optional[Any]												=				next((addr for addr in addrs if addr.startswith("e"						))    ,   lowercase__						)
						return ifname



				def       snake_case    (     self					:	Union[str, Any]    ,   lowercase__					:	np.ndarray    ,   lowercase__					:	int						):

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

						# distributed training
						__lowercase				:		List[str]												=				dist.get_world_size(group=self.process_group						)

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

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

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






"""simple docstring"""







import argparse
from collections import defaultdict

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->List[str]:






		"""simple docstring"""


		__lowercase				:		Tuple												=				F'{file}_{class_name}_{test_name}'
		done_test[_id] += 1

		with open(_lowerCamelCase,  "r"				) as f:
				__lowercase				:		Union[str, Any]												=				f.readlines()

		__lowercase				:		str												=				F'class {class_name}('
		__lowercase				:		Union[str, Any]												=				F'{4 * " "}def {test_name}('
		__lowercase				:		List[Any]												=				F'{8 * " "}{correct_line.split()[0]}'
		__lowercase				:		Optional[int]												=				F'{16 * " "}{correct_line.split()[0]}'
		__lowercase				:		List[Any]												=				False
		__lowercase				:		int												=				False
		__lowercase				:		int												=				False
		__lowercase				:		List[Any]												=				False
		__lowercase				:		str												=				0
		__lowercase				:		Dict												=				0

		__lowercase				:		Optional[int]												=				[]
		for line in lines:
				if line.startswith(_lowerCamelCase				):
						__lowercase				:		Optional[int]												=				True
				elif in_class and line.startswith(_lowerCamelCase				):
						__lowercase				:		Optional[int]												=				True
				elif in_class and in_func and (line.startswith(_lowerCamelCase				) or line.startswith(_lowerCamelCase				)):
						__lowercase				:		int												=				len(line.split(correct_line.split()[0]				)[0]				)
						count += 1

						if count == done_test[_id]:
								__lowercase				:		Any												=				True

				if in_class and in_func and in_line:
						if ")" not in line:
								continue
						else:
								__lowercase				:		int												=				True

				if in_class and in_func and in_line and insert_line:
						new_lines.append(F'{spaces * " "}{correct_line}'				)
						__lowercase				:		List[Any]												=				False
				else:
						new_lines.append(_lowerCamelCase				)

		with open(_lowerCamelCase,  "w"				) as f:
				for line in new_lines:
						f.write(_lowerCamelCase				)

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase=None				)			->List[Any]:






		"""simple docstring"""


		if fail is not None:
				with open(_lowerCamelCase,  "r"				) as f:
						__lowercase				:		str												=				{l.strip() for l in f.readlines()}
		else:
				__lowercase				:		List[Any]												=				None

		with open(_lowerCamelCase,  "r"				) as f:
				__lowercase				:		str												=				f.readlines()

		__lowercase				:		List[str]												=				defaultdict(_lowerCamelCase				)
		for line in correct_lines:
				__lowercase						,__lowercase						,__lowercase						,__lowercase				:		str												=				line.split(";"				)
				if test_failures is None or "::".join([file, class_name, test_name]				) in test_failures:
						overwrite_file(_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)


if __name__ == "__main__":
						__A   :  str					=      argparse.ArgumentParser()
						parser.add_argument('--correct_filename', help='filename of tests with expected result')
						parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
						__A   :  Union[str, Any]					=      parser.parse_args()

						main(args.correct_filename, args.fail_filename)
