


'''simple docstring'''






import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer


def       _snake_case     (  A )		->					Tuple:
							# This defines a "chinese character" as anything in the CJK Unicode block:
							#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
							#
							# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
							# despite its name. The modern Korean Hangul alphabet is a different block,
							# as is Japanese Hiragana and Katakana. Those alphabets are used to write
							# space-separated words, so they are not treated specially and handled
							# like the all of the other languages.
							if (
							    (cp >= 0X4E_00 and cp <= 0X9F_FF)
							    or (cp >= 0X34_00 and cp <= 0X4D_BF)  #
							    or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF)  #
							    or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F)  #
							    or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F)  #
							    or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF)  #
							    or (cp >= 0XF9_00 and cp <= 0XFA_FF)
							    or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F)  #
							):  #
														return True

							return False


def       _snake_case     (  A )		->					Any:
							# word like '180' or '身高' or '神'
							for char in word:
														lowerCAmelCase__      =			ord(A )
														if not _is_chinese_char(A ):
																					return 0
							return 1


def       _snake_case     (  A )		->					Union[str, Any]:
							lowerCAmelCase__      =			set()

							for token in tokens:
														lowerCAmelCase__      =			len(A ) > 1 and is_chinese(A )
														if chinese_word:
																					word_set.add(A )
							lowerCAmelCase__      =			list(A )
							return word_list


def       _snake_case     (  A				,					A )		->					Optional[Any]:
							if not chinese_word_set:
														return bert_tokens
							lowerCAmelCase__      =			max([len(A ) for w in chinese_word_set] )

							lowerCAmelCase__      =			bert_tokens
							lowerCAmelCase__     ,      lowerCAmelCase__      =			0, len(A )
							while start < end:
														lowerCAmelCase__      =			True
														if is_chinese(bert_word[start] ):
																					lowerCAmelCase__      =			min(end - start				,					A )
																					for i in range(A				,					1				,					-1 ):
																												lowerCAmelCase__      =			''''''.join(bert_word[start : start + i] )
																												if whole_word in chinese_word_set:
																																			for j in range(start + 1				,					start + i ):
																																										lowerCAmelCase__      =			'''##''' + bert_word[j]
																																			lowerCAmelCase__      =			start + i
																																			lowerCAmelCase__      =			False
																																			break
														if single_word:
																					start += 1
							return bert_word


def       _snake_case     (  A				,					A				,					A )		->					Union[str, Any]:
							lowerCAmelCase__      =			[]

							for i in range(0				,					len(A )				,					100 ):
														lowerCAmelCase__      =			ltp_tokenizer.seg(lines[i : i + 100] )[0]
														lowerCAmelCase__      =			[get_chinese_word(A ) for r in res]
														ltp_res.extend(A )
							assert len(A ) == len(A )

							lowerCAmelCase__      =			[]
							for i in range(0				,					len(A )				,					100 ):
														lowerCAmelCase__      =			bert_tokenizer(lines[i : i + 100]				,					add_special_tokens=A				,					truncation=A				,					max_length=512 )
														bert_res.extend(res['''input_ids'''] )
							assert len(A ) == len(A )

							lowerCAmelCase__      =			[]
							for input_ids, chinese_word in zip(A				,					A ):
														lowerCAmelCase__      =			[]
														for id in input_ids:
																					lowerCAmelCase__      =			bert_tokenizer._convert_id_to_token(A )
																					input_tokens.append(A )
														lowerCAmelCase__      =			add_sub_symbol(A				,					A )
														lowerCAmelCase__      =			[]
														# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
														for i, token in enumerate(A ):
																					if token[:2] == "##":
																												lowerCAmelCase__      =			token[2:]
																												# save chinese tokens' pos
																												if len(A ) == 1 and _is_chinese_char(ord(A ) ):
																																			ref_id.append(A )
														ref_ids.append(A )

							assert len(A ) == len(A )

							return ref_ids


def       _snake_case     (  A )		->					List[Any]:
							# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
							# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
							with open(args.file_name				,					'''r'''				,					encoding='''utf-8''' ) as f:
														lowerCAmelCase__      =			f.readlines()
							lowerCAmelCase__      =			[line.strip() for line in data if len(A ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
							lowerCAmelCase__      =			LTP(args.ltp )  # faster in GPU device
							lowerCAmelCase__      =			BertTokenizer.from_pretrained(args.bert )

							lowerCAmelCase__      =			prepare_ref(A				,					A				,					A )

							with open(args.save_path				,					'''w'''				,					encoding='''utf-8''' ) as f:
														lowerCAmelCase__      =			[json.dumps(A ) + '''\n''' for ref in ref_ids]
														f.writelines(A )


if __name__ == "__main__":
		__UpperCAmelCase					       =							argparse.ArgumentParser(description='''prepare_chinese_ref''')
		parser.add_argument(
		    '''--file_name''',
		    type=str,
		    default='''./resources/chinese-demo.txt''',
		    help='''file need process, same as training data in lm''',
		)
		parser.add_argument(
		    '''--ltp''', type=str, default='''./resources/ltp''', help='''resources for LTP tokenizer, usually a path'''
		)
		parser.add_argument('''--bert''', type=str, default='''./resources/robert''', help='''resources for Bert tokenizer''')
		parser.add_argument('''--save_path''', type=str, default='''./resources/ref.txt''', help='''path to save res''')

		__UpperCAmelCase					       =							parser.parse_args()
		main(args)



'''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


__UpperCAmelCase					       =							logging.getLogger(__name__)




class 	a__       (							a__						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_=None     ) ->							Dict:
														super().__init__(
														    lowerCamelCase_      ,					question_encoder_tokenizer=lowerCamelCase_      ,					generator_tokenizer=lowerCamelCase_      ,					index=lowerCamelCase_      ,					init_retrieval=lowerCamelCase_      ,					)
														lowerCAmelCase__      =			None







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							str:
														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__      =			self._infer_socket_ifname()
																					# avoid clash with the NCCL port
																					lowerCAmelCase__      =			str(distributed_port + 1     )
																					lowerCAmelCase__      =			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 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
														return dist.get_rank(group=self.process_group     ) == 0







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_=torch.floataa     ) ->							Union[str, Any]:
														lowerCAmelCase__      =			torch.empty(lowerCamelCase_      ,					dtype=lowerCamelCase_     )
														dist.scatter(lowerCamelCase_      ,					src=0      ,					scatter_list=lowerCamelCase_      ,					group=self.process_group     )
														return target_tensor







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														lowerCAmelCase__      =			psutil.net_if_addrs()
														# a hacky way to deal with varying network interface names
														lowerCAmelCase__      =			next((addr for addr in addrs if addr.startswith('''e'''     ))      ,					lowerCamelCase_     )
														return ifname







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Tuple[np.ndarray, List[dict]]:

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

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

														# gather logic
														lowerCAmelCase__      =			None
														if self._is_main():
																					lowerCAmelCase__      =			[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
														lowerCAmelCase__      =			question_hidden_states.shape[0]
														lowerCAmelCase__      =			[]
														lowerCAmelCase__      =			[]
														if self._is_main():
																					assert len(lowerCamelCase_     ) == world_size
																					lowerCAmelCase__     ,      lowerCAmelCase__      =			self._main_retrieve(torch.cat(lowerCamelCase_     ).numpy()      ,					lowerCamelCase_     )
																					lowerCAmelCase__     ,      lowerCAmelCase__      =			torch.tensor(lowerCamelCase_     ), torch.tensor(lowerCamelCase_     )
																					lowerCAmelCase__      =			self._chunk_tensor(lowerCamelCase_      ,					lowerCamelCase_     )
																					lowerCAmelCase__      =			self._chunk_tensor(lowerCamelCase_      ,					lowerCamelCase_     )
														lowerCAmelCase__      =			self._scattered(lowerCamelCase_      ,					[n_queries, n_docs]      ,					target_type=torch.intaa     )
														lowerCAmelCase__      =			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_     )