



import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer






def      __lowercase       (			a__       )    ->			int:
							# 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 >= 0x4_E00 and cp <= 0x9_FFF)
							    or (cp >= 0x3_400 and cp <= 0x4_DBF)  #
							    or (cp >= 0x20_000 and cp <= 0x2A_6DF)  #
							    or (cp >= 0x2A_700 and cp <= 0x2B_73F)  #
							    or (cp >= 0x2B_740 and cp <= 0x2B_81F)  #
							    or (cp >= 0x2B_820 and cp <= 0x2C_EAF)  #
							    or (cp >= 0xF_900 and cp <= 0xF_AFF)
							    or (cp >= 0x2F_800 and cp <= 0x2F_A1F)  #
							):  #
														return True

							return False






def      __lowercase       (			a__       )    ->			List[str]:
							# word like '180' or '身高' or '神'
							for char in word:
														__SCREAMING_SNAKE_CASE									= ord(a__       )
														if not _is_chinese_char(a__       ):
																					return 0
							return 1






def      __lowercase       (			a__       )    ->			Optional[int]:
							__SCREAMING_SNAKE_CASE									= set()

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






def      __lowercase       (			a__							,       a__       )    ->			List[str]:
							if not chinese_word_set:
														return bert_tokens
							__SCREAMING_SNAKE_CASE									= max([len(a__       ) for w in chinese_word_set]       )

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






def      __lowercase       (			a__							,       a__							,       a__       )    ->			List[str]:
							__SCREAMING_SNAKE_CASE									= []

							for i in range(0							,       len(a__       )							,       1_00       ):
														__SCREAMING_SNAKE_CASE									= ltp_tokenizer.pipeline(lines[i : i + 1_00]							,       tasks=['cws']       ).cws
														__SCREAMING_SNAKE_CASE									= [get_chinese_word(a__       ) for r in res]
														ltp_res.extend(a__       )
							assert len(a__       ) == len(a__       )

							__SCREAMING_SNAKE_CASE									= []
							for i in range(0							,       len(a__       )							,       1_00       ):
														__SCREAMING_SNAKE_CASE									= bert_tokenizer(lines[i : i + 1_00]							,       add_special_tokens=a__							,       truncation=a__							,       max_length=5_12       )
														bert_res.extend(res['input_ids']       )
							assert len(a__       ) == len(a__       )

							__SCREAMING_SNAKE_CASE									= []
							for input_ids, chinese_word in zip(a__							,       a__       ):
														__SCREAMING_SNAKE_CASE									= []
														for id in input_ids:
																					__SCREAMING_SNAKE_CASE									= bert_tokenizer._convert_id_to_token(a__       )
																					input_tokens.append(a__       )
														__SCREAMING_SNAKE_CASE									= add_sub_symbol(a__							,       a__       )
														__SCREAMING_SNAKE_CASE									= []
														# 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] == "##":
																												__SCREAMING_SNAKE_CASE									= 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      __lowercase       (			a__       )    ->			Dict:
							# 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:
														__SCREAMING_SNAKE_CASE									= f.readlines()
							__SCREAMING_SNAKE_CASE									= [line.strip() for line in data if len(a__       ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
							__SCREAMING_SNAKE_CASE									= LTP(args.ltp       )  # faster in GPU device
							__SCREAMING_SNAKE_CASE									= BertTokenizer.from_pretrained(args.bert       )

							__SCREAMING_SNAKE_CASE									= prepare_ref(a__							,       a__							,       a__       )

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


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

			lowerCAmelCase__		:					Any							=parser.parse_args()
			main(args)











import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows






def      __lowercase       (			a__       )    ->			List[Any]:  # picklable for multiprocessing
							return i + 1






@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def      __lowercase       (			)    ->			Any:
							with parallel_backend('spark'       ):
														assert ParallelBackendConfig.backend_name == "spark"

							__SCREAMING_SNAKE_CASE									= [1, 2, 3]
							with pytest.raises(a__       ):
														with parallel_backend('unsupported backend'       ):
																					map_nested(a__							,       a__							,       num_proc=2       )

							with pytest.raises(a__       ):
														with parallel_backend('unsupported backend'       ):
																					map_nested(a__							,       a__							,       num_proc=-1       )






@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize('num_proc'							,       [2, -1]       )
def      __lowercase       (			a__       )    ->			Union[str, Any]:
							__SCREAMING_SNAKE_CASE									= [1, 2]
							__SCREAMING_SNAKE_CASE									= {'a': 1, 'b': 2}
							__SCREAMING_SNAKE_CASE									= {'a': [1, 2], 'b': [3, 4]}
							__SCREAMING_SNAKE_CASE									= {'a': {'1': 1}, 'b': 2}
							__SCREAMING_SNAKE_CASE									= {'a': 1, 'b': 2, 'c': 3, 'd': 4}
							__SCREAMING_SNAKE_CASE									= [2, 3]
							__SCREAMING_SNAKE_CASE									= {'a': 2, 'b': 3}
							__SCREAMING_SNAKE_CASE									= {'a': [2, 3], 'b': [4, 5]}
							__SCREAMING_SNAKE_CASE									= {'a': {'1': 2}, 'b': 3}
							__SCREAMING_SNAKE_CASE									= {'a': 2, 'b': 3, 'c': 4, 'd': 5}

							with parallel_backend('spark'       ):
														assert map_nested(a__							,       a__							,       num_proc=a__       ) == expected_map_nested_sa
														assert map_nested(a__							,       a__							,       num_proc=a__       ) == expected_map_nested_sa
														assert map_nested(a__							,       a__							,       num_proc=a__       ) == expected_map_nested_sa
														assert map_nested(a__							,       a__							,       num_proc=a__       ) == expected_map_nested_sa
														assert map_nested(a__							,       a__							,       num_proc=a__       ) == expected_map_nested_sa






