



from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('3.8'):
					import importlib_metadata
else:
					import importlib.metadata as importlib_metadata


lowerCAmelCase_           = ''


if version.parse(importlib_metadata.version('jiwer')) < version.parse('2.3.0'):
					class      UpperCamelCase (				tr.AbstractTransform ):







							"""simple docstring"""



							def __init__(  self		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       str = " "		)    ->   Any:


											'''simple docstring'''
											A	 =      sentence_delimiter
							def       A(  self		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       str		)    ->   Optional[Any]:


											'''simple docstring'''
											return list(_SCREAMING_SNAKE_CASE		)







							def       A(  self		:       str						,_SCREAMING_SNAKE_CASE		:       List[str]		)    ->   Union[str, Any]:


											'''simple docstring'''
											A	 =      []
											for sent_idx, sentence in enumerate(_SCREAMING_SNAKE_CASE		):
															chars.extend(self.process_string(_SCREAMING_SNAKE_CASE		)		)
															if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(_SCREAMING_SNAKE_CASE		) - 1:
																			chars.append(self.sentence_delimiter		)
											return chars


					lowerCAmelCase_           = tr.Compose(
					    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
					)
else:
					lowerCAmelCase_           = tr.Compose(
					    [
					        tr.RemoveMultipleSpaces(),
					        tr.Strip(),
					        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
					        tr.ReduceToListOfListOfChars(),
					    ]
					)


lowerCAmelCase_           = '\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'

lowerCAmelCase_           = '\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER\'s output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the\nperformance of the ASR system with a CER of 0 being a perfect score.\n'

lowerCAmelCase_           = '\nComputes CER score of transcribed segments against references.\nArgs:\n    references: list of references for each speech input.\n    predictions: list of transcribtions to score.\n    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n    (float): the character error rate\n\nExamples:\n\n    >>> predictions = ["this is the prediction", "there is an other sample"]\n    >>> references = ["this is the reference", "there is another one"]\n    >>> cer = datasets.load_metric("cer")\n    >>> cer_score = cer.compute(predictions=predictions, references=references)\n    >>> print(cer_score)\n    0.34146341463414637\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,       _KWARGS_DESCRIPTION )
class      UpperCamelCase (				datasets.Metric ):







		"""simple docstring"""



		def       A(  self		:       List[str]		)    ->   str:


						'''simple docstring'''
						return datasets.MetricInfo(
						    description=_DESCRIPTION						,citation=_CITATION						,inputs_description=_KWARGS_DESCRIPTION						,features=datasets.Features(
						        {
						            'predictions': datasets.Value('string'						,id='sequence'		),
						            'references': datasets.Value('string'						,id='sequence'		),
						        }		)						,codebase_urls=['https://github.com/jitsi/jiwer/']						,reference_urls=[
						        'https://en.wikipedia.org/wiki/Word_error_rate',
						        'https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates',
						    ]						,)







		def       A(  self		:       Tuple						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       int=False		)    ->   Dict:


						'''simple docstring'''
						if concatenate_texts:
										return jiwer.compute_measures(
										    _SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,truth_transform=_SCREAMING_SNAKE_CASE						,hypothesis_transform=_SCREAMING_SNAKE_CASE						,)["wer"]

						A	 =      0
						A	 =      0
						for prediction, reference in zip(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      jiwer.compute_measures(
										    _SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,truth_transform=_SCREAMING_SNAKE_CASE						,hypothesis_transform=_SCREAMING_SNAKE_CASE						,)
										incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
										total += measures["substitutions"] + measures["deletions"] + measures["hits"]

						return incorrect / total













import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer







def      snake_case		(							UpperCAmelCase	:       List[str]   ):
				# 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_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      snake_case		(							UpperCAmelCase	:       str   ):
				# word like '180' or '身高' or '神'
				for char in word:
								A	 =      ord(UpperCAmelCase   )
								if not _is_chinese_char(UpperCAmelCase   ):
												return 0
				return 1







def      snake_case		(							UpperCAmelCase	:       List[str]   ):
				A	 =      set()

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







def      snake_case		(							UpperCAmelCase	:       List[str],						UpperCAmelCase	:       set()   ):
				if not chinese_word_set:
								return bert_tokens
				A	 =      max([len(UpperCAmelCase   ) for w in chinese_word_set]   )

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







def      snake_case		(							UpperCAmelCase	:       List[str],						UpperCAmelCase	:       LTP,						UpperCAmelCase	:       BertTokenizer   ):
				A	 =      []

				for i in range(0,						len(UpperCAmelCase   ),						1_00   ):
								A	 =      ltp_tokenizer.seg(lines[i : i + 1_00]   )[0]
								A	 =      [get_chinese_word(UpperCAmelCase   ) for r in res]
								ltp_res.extend(UpperCAmelCase   )
				assert len(UpperCAmelCase   ) == len(UpperCAmelCase   )

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

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

				assert len(UpperCAmelCase   ) == len(UpperCAmelCase   )

				return ref_ids







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

				A	 =      prepare_ref(UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase   )

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


if __name__ == "__main__":
					lowerCAmelCase_           = 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')

					lowerCAmelCase_           = parser.parse_args()
					main(args)





