




import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				Dict:
						# 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 >= 0x4e00 and cp <= 0x9fff)
						    or (cp >= 0x3400 and cp <= 0x4dbf)  #
						    or (cp >= 0x2_0000 and cp <= 0x2_a6df)  #
						    or (cp >= 0x2_a700 and cp <= 0x2_b73f)  #
						    or (cp >= 0x2_b740 and cp <= 0x2_b81f)  #
						    or (cp >= 0x2_b820 and cp <= 0x2_ceaf)  #
						    or (cp >= 0xf900 and cp <= 0xfaff)
						    or (cp >= 0x2_f800 and cp <= 0x2_fa1f)  #
						):  #
												return True

						return False




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				int:
						# word like '180' or '身高' or '神'
						for char in word:
												_UpperCAmelCase      =			ord(snake_case       )
												if not _is_chinese_char(snake_case       ):
																		return 0
						return 1




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				str:
						_UpperCAmelCase      =			set()

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




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				str:
						if not chinese_word_set:
												return bert_tokens
						_UpperCAmelCase      =			max([len(snake_case       ) for w in chinese_word_set]       )

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




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				Any:
						_UpperCAmelCase      =			[]

						for i in range(0			,     len(snake_case       )			,     1_0_0       ):
												_UpperCAmelCase      =			ltp_tokenizer.seg(lines[i : i + 1_0_0]       )[0]
												_UpperCAmelCase      =			[get_chinese_word(snake_case       ) for r in res]
												ltp_res.extend(snake_case       )
						assert len(snake_case       ) == len(snake_case       )

						_UpperCAmelCase      =			[]
						for i in range(0			,     len(snake_case       )			,     1_0_0       ):
												_UpperCAmelCase      =			bert_tokenizer(lines[i : i + 1_0_0]			,     add_special_tokens=snake_case			,     truncation=snake_case			,     max_length=5_1_2       )
												bert_res.extend(res["""input_ids"""]       )
						assert len(snake_case       ) == len(snake_case       )

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

						assert len(snake_case       ) == len(snake_case       )

						return ref_ids




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				Union[str, 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:
												_UpperCAmelCase      =			f.readlines()
						_UpperCAmelCase      =			[line.strip() for line in data if len(snake_case       ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
						_UpperCAmelCase      =			LTP(args.ltp       )  # faster in GPU device
						_UpperCAmelCase      =			BertTokenizer.from_pretrained(args.bert       )

						_UpperCAmelCase      =			prepare_ref(snake_case			,     snake_case			,     snake_case       )

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


if __name__ == "__main__":
				a													=			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")

				a													=			parser.parse_args()
				main(args)





import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				int:
						assert isinstance(snake_case			,     snake_case       )
						assert dataset.num_rows == 4
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_1", "col_2", "col_3"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("""keep_in_memory"""			,     [False, True]       )
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				Optional[int]:
						_UpperCAmelCase      =			tmp_path / """cache"""
						_UpperCAmelCase      =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												_UpperCAmelCase      =			JsonDatasetReader(snake_case			,     cache_dir=snake_case			,     keep_in_memory=snake_case       ).read()
						_check_json_dataset(snake_case			,     snake_case       )




@pytest.mark.parametrize(
    """features"""			,     [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]			,     )
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				Optional[Any]:
						_UpperCAmelCase      =			tmp_path / """cache"""
						_UpperCAmelCase      =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						_UpperCAmelCase      =			features.copy() if features else default_expected_features
						_UpperCAmelCase      =			(
						    Features({feature: Value(snake_case       ) for feature, dtype in features.items()}       ) if features is not None else None
						)
						_UpperCAmelCase      =			JsonDatasetReader(snake_case			,     features=snake_case			,     cache_dir=snake_case       ).read()
						_check_json_dataset(snake_case			,     snake_case       )




@pytest.mark.parametrize(
    """features"""			,     [
        None,
        {"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""},
    ]			,     )
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				int:
						_UpperCAmelCase      =			tmp_path / """cache"""
						_UpperCAmelCase      =			{"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""}
						_UpperCAmelCase      =			features.copy() if features else default_expected_features
						_UpperCAmelCase      =			(
						    Features({feature: Value(snake_case       ) for feature, dtype in features.items()}       ) if features is not None else None
						)
						_UpperCAmelCase      =			JsonDatasetReader(snake_case			,     features=snake_case			,     cache_dir=snake_case       ).read()
						assert isinstance(snake_case			,     snake_case       )
						assert dataset.num_rows == 2
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_3", "col_1", "col_2"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				Optional[int]:
						# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
						_UpperCAmelCase      =			{"""col_2""": """int64""", """col_3""": """float64""", """col_1""": """string"""}
						_UpperCAmelCase      =			features.copy()
						_UpperCAmelCase      =			(
						    Features({feature: Value(snake_case       ) for feature, dtype in features.items()}       ) if features is not None else None
						)
						_UpperCAmelCase      =			tmp_path / """cache"""
						_UpperCAmelCase      =			JsonDatasetReader(snake_case			,     features=snake_case			,     cache_dir=snake_case       ).read()
						assert isinstance(snake_case			,     snake_case       )
						assert dataset.num_rows == 2
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_2", "col_3", "col_1"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("""split"""			,     [None, NamedSplit("""train"""       ), """train""", """test"""]       )
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				List[Any]:
						_UpperCAmelCase      =			tmp_path / """cache"""
						_UpperCAmelCase      =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						_UpperCAmelCase      =			JsonDatasetReader(snake_case			,     cache_dir=snake_case			,     split=snake_case       ).read()
						_check_json_dataset(snake_case			,     snake_case       )
						assert dataset.split == split if split else "train"




@pytest.mark.parametrize("""path_type"""			,     [str, list]       )
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				Any:
						if issubclass(snake_case			,     snake_case       ):
												_UpperCAmelCase      =			jsonl_path
						elif issubclass(snake_case			,     snake_case       ):
												_UpperCAmelCase      =			[jsonl_path]
						_UpperCAmelCase      =			tmp_path / """cache"""
						_UpperCAmelCase      =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						_UpperCAmelCase      =			JsonDatasetReader(snake_case			,     cache_dir=snake_case       ).read()
						_check_json_dataset(snake_case			,     snake_case       )




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case=("train",)       )						->				str:
						assert isinstance(snake_case			,     snake_case       )
						for split in splits:
												_UpperCAmelCase      =			dataset_dict[split]
												assert dataset.num_rows == 4
												assert dataset.num_columns == 3
												assert dataset.column_names == ["col_1", "col_2", "col_3"]
												for feature, expected_dtype in expected_features.items():
																		assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("""keep_in_memory"""			,     [False, True]       )
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				str:
						_UpperCAmelCase      =			tmp_path / """cache"""
						_UpperCAmelCase      =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												_UpperCAmelCase      =			JsonDatasetReader({"""train""": jsonl_path}			,     cache_dir=snake_case			,     keep_in_memory=snake_case       ).read()
						_check_json_datasetdict(snake_case			,     snake_case       )




@pytest.mark.parametrize(
    """features"""			,     [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]			,     )
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				Tuple:
						_UpperCAmelCase      =			tmp_path / """cache"""
						_UpperCAmelCase      =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						_UpperCAmelCase      =			features.copy() if features else default_expected_features
						_UpperCAmelCase      =			(
						    Features({feature: Value(snake_case       ) for feature, dtype in features.items()}       ) if features is not None else None
						)
						_UpperCAmelCase      =			JsonDatasetReader({"""train""": jsonl_path}			,     features=snake_case			,     cache_dir=snake_case       ).read()
						_check_json_datasetdict(snake_case			,     snake_case       )




@pytest.mark.parametrize("""split"""			,     [None, NamedSplit("""train"""       ), """train""", """test"""]       )
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				Optional[Any]:
						if split:
												_UpperCAmelCase      =			{split: jsonl_path}
						else:
												_UpperCAmelCase      =			"""train"""
												_UpperCAmelCase      =			{"""train""": jsonl_path, """test""": jsonl_path}
						_UpperCAmelCase      =			tmp_path / """cache"""
						_UpperCAmelCase      =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						_UpperCAmelCase      =			JsonDatasetReader(snake_case			,     cache_dir=snake_case       ).read()
						_check_json_datasetdict(snake_case			,     snake_case			,     splits=list(path.keys()       )       )
						assert all(dataset[split].split == split for split in path.keys()       )




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				List[Any]:
						return json.load(snake_case       )




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				List[Any]:
						return [json.loads(snake_case       ) for line in buffer]




class       _A				:






		@pytest.mark.parametrize("""lines, load_json_function"""						,		[(True, load_json_lines), (False, load_json)]			)
		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								with io.BytesIO() as buffer:
														JsonDatasetWriter(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		lines=_SCREAMING_SNAKE_CASE			).write()
														buffer.seek(0			)
														_UpperCAmelCase      =			load_json_function(_SCREAMING_SNAKE_CASE			)
								assert isinstance(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
								assert isinstance(exported_content[0]						,		_SCREAMING_SNAKE_CASE			)
								assert len(_SCREAMING_SNAKE_CASE			) == 10






		@pytest.mark.parametrize(
		    """orient, container, keys, len_at"""						,		[
		        ("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
		        ("""split""", dict, {"""columns""", """data"""}, """data"""),
		        ("""index""", dict, set("""0123456789"""			), None),
		        ("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
		        ("""values""", list, None, None),
		        ("""table""", dict, {"""schema""", """data"""}, """data"""),
		    ]						,		)
		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								with io.BytesIO() as buffer:
														JsonDatasetWriter(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		lines=_SCREAMING_SNAKE_CASE						,		orient=_SCREAMING_SNAKE_CASE			).write()
														buffer.seek(0			)
														_UpperCAmelCase      =			load_json(_SCREAMING_SNAKE_CASE			)
								assert isinstance(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
								if keys:
														if container is dict:
																				assert exported_content.keys() == keys
														else:
																				assert exported_content[0].keys() == keys
								else:
														assert not hasattr(_SCREAMING_SNAKE_CASE						,		"""keys"""			) and not hasattr(exported_content[0]						,		"""keys"""			)
								if len_at:
														assert len(exported_content[len_at]			) == 10
								else:
														assert len(_SCREAMING_SNAKE_CASE			) == 10






		@pytest.mark.parametrize("""lines, load_json_function"""						,		[(True, load_json_lines), (False, load_json)]			)
		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								with io.BytesIO() as buffer:
														JsonDatasetWriter(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		lines=_SCREAMING_SNAKE_CASE						,		num_proc=2			).write()
														buffer.seek(0			)
														_UpperCAmelCase      =			load_json_function(_SCREAMING_SNAKE_CASE			)
								assert isinstance(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
								assert isinstance(exported_content[0]						,		_SCREAMING_SNAKE_CASE			)
								assert len(_SCREAMING_SNAKE_CASE			) == 10






		@pytest.mark.parametrize(
		    """orient, container, keys, len_at"""						,		[
		        ("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
		        ("""split""", dict, {"""columns""", """data"""}, """data"""),
		        ("""index""", dict, set("""0123456789"""			), None),
		        ("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
		        ("""values""", list, None, None),
		        ("""table""", dict, {"""schema""", """data"""}, """data"""),
		    ]						,		)
		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								with io.BytesIO() as buffer:
														JsonDatasetWriter(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		lines=_SCREAMING_SNAKE_CASE						,		orient=_SCREAMING_SNAKE_CASE						,		num_proc=2			).write()
														buffer.seek(0			)
														_UpperCAmelCase      =			load_json(_SCREAMING_SNAKE_CASE			)
								assert isinstance(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
								if keys:
														if container is dict:
																				assert exported_content.keys() == keys
														else:
																				assert exported_content[0].keys() == keys
								else:
														assert not hasattr(_SCREAMING_SNAKE_CASE						,		"""keys"""			) and not hasattr(exported_content[0]						,		"""keys"""			)
								if len_at:
														assert len(exported_content[len_at]			) == 10
								else:
														assert len(_SCREAMING_SNAKE_CASE			) == 10






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								with pytest.raises(_SCREAMING_SNAKE_CASE			):
														with io.BytesIO() as buffer:
																				JsonDatasetWriter(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		num_proc=0			)






		@pytest.mark.parametrize("""compression, extension"""						,		[("""gzip""", """gz"""), ("""bz2""", """bz2"""), ("""xz""", """xz""")]			)
		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			tmp_path_factory.mktemp("""data"""			) / F"test.json.{extension}"
								_UpperCAmelCase      =			str(shared_datadir / F"test_file.json.{extension}"			)
								JsonDatasetWriter(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		compression=_SCREAMING_SNAKE_CASE			).write()

								with fsspec.open(_SCREAMING_SNAKE_CASE						,		"""rb"""						,		compression="""infer"""			) as f:
														_UpperCAmelCase      =			f.read()
								with fsspec.open(_SCREAMING_SNAKE_CASE						,		"""rb"""						,		compression="""infer"""			) as f:
														_UpperCAmelCase      =			f.read()
								assert exported_content == original_content