
import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer




def 							a		(				snake_case__:   Tuple   ):


				'''simple docstring'''
				# 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 							a		(				snake_case__:   str   ):


				'''simple docstring'''
				# word like '180' or '身高' or '神'
				for char in word:
								lowercase_	    =   ord(snake_case__   )
								if not _is_chinese_char(snake_case__   ):
												return 0
				return 1




def 							a		(				snake_case__:   List[str]   ):


				'''simple docstring'''
				lowercase_	    =   set()

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




def 							a		(				snake_case__:   List[str]		,  snake_case__:   set()   ):


				'''simple docstring'''
				if not chinese_word_set:
								return bert_tokens
				lowercase_	    =   max([len(snake_case__   ) for w in chinese_word_set]   )

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




def 							a		(				snake_case__:   List[str]		,  snake_case__:   LTP		,  snake_case__:   BertTokenizer   ):


				'''simple docstring'''
				lowercase_	    =   []

				for i in range(0		,  len(snake_case__   )		,  100   ):
								lowercase_	    =   ltp_tokenizer.pipeline(lines[i : i + 100]		,  tasks=['''cws''']   ).cws
								lowercase_	    =   [get_chinese_word(snake_case__   ) for r in res]
								ltp_res.extend(snake_case__   )
				assert len(snake_case__   ) == len(snake_case__   )

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

				lowercase_	    =   []
				for input_ids, chinese_word in zip(snake_case__		,  snake_case__   ):
								lowercase_	    =   []
								for id in input_ids:
												lowercase_	    =   bert_tokenizer._convert_id_to_token(snake_case__   )
												input_tokens.append(snake_case__   )
								lowercase_	    =   add_sub_symbol(snake_case__		,  snake_case__   )
								lowercase_	    =   []
								# 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] == "##":
																lowercase_	    =   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 							a		(				snake_case__:   Optional[int]   ):


				'''simple docstring'''
				# 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:
								lowercase_	    =   f.readlines()
				lowercase_	    =   [line.strip() for line in data if len(snake_case__   ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
				lowercase_	    =   LTP(args.ltp   )  # faster in GPU device
				lowercase_	    =   BertTokenizer.from_pretrained(args.bert   )

				lowercase_	    =   prepare_ref(snake_case__		,  snake_case__		,  snake_case__   )

				with open(args.save_path		,  '''w'''		,  encoding='''utf-8'''   ) as f:
								lowercase_	    =   [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',
						    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',
						)

						__a         =   parser.parse_args()
						main(args)










from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


__A								=       logging.get_logger(__name__)  # pylint: disable=invalid-name







def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			Tuple:





				"""simple docstring"""


				if not path:
								return "pipe"

				for ext in PipelineDataFormat.SUPPORTED_FORMATS:
								if path.endswith(UpperCamelCase__ ):
												return ext

				raise Exception(
				    F"""Unable to determine file format from file extension {path}. """
				    F"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}""" )







def      lowerCamelCase_						(			UpperCamelCase__							: Dict )	->			List[str]:





				"""simple docstring"""


				__lowerCamelCase									=       pipeline(
				    task=args.task					,	model=args.model if args.model else None					,	config=args.config					,	tokenizer=args.tokenizer					,	device=args.device					,	)
				__lowerCamelCase									=       try_infer_format_from_ext(args.input ) if args.format == 'infer' else args.format
				__lowerCamelCase									=       PipelineDataFormat.from_str(
				    format=UpperCamelCase__					,	output_path=args.output					,	input_path=args.input					,	column=args.column if args.column else nlp.default_input_names					,	overwrite=args.overwrite					,	)
				return RunCommand(UpperCamelCase__					,	UpperCamelCase__ )







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			def __init__( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> List[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       nlp
							__lowerCamelCase									=       reader


			@staticmethod
			def  lowercase_       ( lowerCamelCase__				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       parser.add_parser('run'     ,		help='Run a pipeline through the CLI'				)
							run_parser.add_argument('--task'     ,		choices=get_supported_tasks()     ,		help='Task to run'				)
							run_parser.add_argument('--input'     ,		type=lowerCamelCase__     ,		help='Path to the file to use for inference'				)
							run_parser.add_argument('--output'     ,		type=lowerCamelCase__     ,		help='Path to the file that will be used post to write results.'				)
							run_parser.add_argument('--model'     ,		type=lowerCamelCase__     ,		help='Name or path to the model to instantiate.'				)
							run_parser.add_argument('--config'     ,		type=lowerCamelCase__     ,		help='Name or path to the model\'s config to instantiate.'				)
							run_parser.add_argument(
							    '--tokenizer'     ,		type=lowerCamelCase__     ,		help='Name of the tokenizer to use. (default: same as the model name)'				)
							run_parser.add_argument(
							    '--column'     ,		type=lowerCamelCase__     ,		help='Name of the column to use as input. (For multi columns input as QA use column1,columns2)'     ,		)
							run_parser.add_argument(
							    '--format'     ,		type=lowerCamelCase__     ,		default='infer'     ,		choices=PipelineDataFormat.SUPPORTED_FORMATS     ,		help='Input format to read from'     ,		)
							run_parser.add_argument(
							    '--device'     ,		type=lowerCamelCase__     ,		default=-1     ,		help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'     ,		)
							run_parser.add_argument('--overwrite'     ,		action='store_true'     ,		help='Allow overwriting the output file.'				)
							run_parser.set_defaults(func=lowerCamelCase__				)






			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase			,		__lowerCamelCase									=       self._nlp, []

							for entry in self._reader:
											__lowerCamelCase									=       nlp(**lowerCamelCase__				) if self._reader.is_multi_columns else nlp(lowerCamelCase__				)
											if isinstance(lowerCamelCase__     ,		lowerCamelCase__				):
															outputs.append(lowerCamelCase__				)
											else:
															outputs += output

        # Saving data
							if self._nlp.binary_output:
											__lowerCamelCase									=       self._reader.save_binary(lowerCamelCase__				)
											logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""				)
							else:
											self._reader.save(lowerCamelCase__				)


