






import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


SCREAMING_SNAKE_CASE__      :			List[str]							=		datasets.utils.logging.get_logger(__name__)
@dataclass
class 						lowerCamelCase_				(      datasets.BuilderConfig ):
				a__					       =  1_00_00
				a__					       =  None
				a__					       =  None







class 						lowerCamelCase_				(      datasets.ArrowBasedBuilder ):
				a__					       =  ParquetConfig







				def 			A	(						self     ):
								"""simple docstring"""
								return datasets.DatasetInfo(features=self.config.features     )







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								if not self.config.data_files:
												raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''     )
								__magic_name__		:Optional[int]        =      dl_manager.download_and_extract(self.config.data_files     )
								if isinstance(__lowerCAmelCase       ,					(str, list, tuple)     ):
												__magic_name__		:Tuple        =      data_files
												if isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ):
																__magic_name__		:List[str]        =      [files]
												# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
												__magic_name__		:Tuple        =      [dl_manager.iter_files(__lowerCAmelCase     ) for file in files]
												return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,					gen_kwargs={'''files''': files}     )]
								__magic_name__		:List[str]        =      []
								for split_name, files in data_files.items():
												if isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ):
																__magic_name__		:str        =      [files]
												# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
												__magic_name__		:List[str]        =      [dl_manager.iter_files(__lowerCAmelCase     ) for file in files]
												# Infer features is they are stoed in the arrow schema
												if self.info.features is None:
																for file in itertools.chain.from_iterable(__lowerCAmelCase     ):
																				with open(__lowerCAmelCase       ,					'''rb'''     ) as f:
																								__magic_name__		:Any        =      datasets.Features.from_arrow_schema(pq.read_schema(__lowerCAmelCase     )     )
																				break
												splits.append(datasets.SplitGenerator(name=__lowerCAmelCase       ,					gen_kwargs={'''files''': files}     )     )
								return splits







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								if self.info.features is not None:
												# more expensive cast to support nested features with keys in a different order
												# allows str <-> int/float or str to Audio for example
												__magic_name__		:Optional[int]        =      table_cast(__lowerCAmelCase       ,					self.info.features.arrow_schema     )
								return pa_table







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      self.info.features.arrow_schema if self.info.features is not None else None
								if self.info.features is not None and self.config.columns is not None:
												if sorted(field.name for field in schema     ) != sorted(self.config.columns     ):
																raise ValueError(
																    F'''Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\''''     )
								for file_idx, file in enumerate(itertools.chain.from_iterable(__lowerCAmelCase     )     ):
												with open(__lowerCAmelCase       ,					'''rb'''     ) as f:
																__magic_name__		:Union[str, Any]        =      pq.ParquetFile(__lowerCAmelCase     )
																try:
																				for batch_idx, record_batch in enumerate(
																				    parquet_file.iter_batches(batch_size=self.config.batch_size       ,					columns=self.config.columns     )     ):
																								__magic_name__		:List[str]        =      pa.Table.from_batches([record_batch]     )
																								# Uncomment for debugging (will print the Arrow table size and elements)
																								# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																								# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																								yield F'''{file_idx}_{batch_idx}''', self._cast_table(__lowerCAmelCase     )
																except ValueError as e:
																				logger.error(F'''Failed to read file \'{file}\' with error {type(__lowerCAmelCase     )}: {e}'''     )
																				raise














import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer

def      __lowercase					( snake_case ):







				"""simple docstring"""






				if (
				    (cp >= 0x4_e_0_0 and cp <= 0x9_f_f_f)
				    or (cp >= 0x3_4_0_0 and cp <= 0x4_d_b_f)  #
				    or (cp >= 0x2_0_0_0_0 and cp <= 0x2_a_6_d_f)  #
				    or (cp >= 0x2_a_7_0_0 and cp <= 0x2_b_7_3_f)  #
				    or (cp >= 0x2_b_7_4_0 and cp <= 0x2_b_8_1_f)  #
				    or (cp >= 0x2_b_8_2_0 and cp <= 0x2_c_e_a_f)  #
				    or (cp >= 0xf_9_0_0 and cp <= 0xf_a_f_f)
				    or (cp >= 0x2_f_8_0_0 and cp <= 0x2_f_a_1_f)  #
				):  #
								return True

				return False

def      __lowercase					( snake_case ):







				"""simple docstring"""






				for char in word:
								__magic_name__		:Union[str, Any]        =      ord(snake_case )
								if not _is_chinese_char(snake_case ):
												return 0
				return 1

def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Any        =      set()

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

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				if not chinese_word_set:
								return bert_tokens
				__magic_name__		:int        =      max([len(snake_case ) for w in chinese_word_set] )

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

def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Optional[int]        =      []

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

				__magic_name__		:Union[str, Any]        =      []
				for i in range(0,  len(snake_case ),  1_0_0 ):
								__magic_name__		:str        =      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 )

				__magic_name__		:Union[str, Any]        =      []
				for input_ids, chinese_word in zip(snake_case,  snake_case ):
								__magic_name__		:Any        =      []
								for id in input_ids:
												__magic_name__		:List[Any]        =      bert_tokenizer._convert_id_to_token(snake_case )
												input_tokens.append(snake_case )
								__magic_name__		:Dict        =      add_sub_symbol(snake_case,  snake_case )
								__magic_name__		:Optional[Any]        =      []
								# 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] == "##":
																__magic_name__		:Optional[int]        =      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      __lowercase					( snake_case ):







				"""simple docstring"""






				with open(args.file_name,  '''r''',  encoding='''utf-8''' ) as f:
								__magic_name__		:Union[str, Any]        =      f.readlines()
				__magic_name__		:int        =      [line.strip() for line in data if len(snake_case ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
				__magic_name__		:List[Any]        =      LTP(args.ltp )  # faster in GPU device
				__magic_name__		:List[str]        =      BertTokenizer.from_pretrained(args.bert )

				__magic_name__		:Optional[int]        =      prepare_ref(snake_case,  snake_case,  snake_case )

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      :			Tuple							=		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""")

					SCREAMING_SNAKE_CASE__      :			int							=		parser.parse_args()
					main(args)






