


import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Union[str, Any]:


						'''simple docstring'''






						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 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Optional[int]:


						'''simple docstring'''






						for char in word:
												__snake_case				=		ord(_lowerCAmelCase )
												if not _is_chinese_char(_lowerCAmelCase ):
																		return 0
						return 1





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Dict:


						'''simple docstring'''






						__snake_case				=		set()

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





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	Union[str, Any]:


						'''simple docstring'''






						if not chinese_word_set:
												return bert_tokens
						__snake_case				=		max([len(_lowerCAmelCase ) for w in chinese_word_set] )

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





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	Union[str, Any]:


						'''simple docstring'''






						__snake_case				=		[]

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

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

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

						assert len(_lowerCAmelCase ) == len(_lowerCAmelCase )

						return ref_ids





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Union[str, Any]:


						'''simple docstring'''






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

						__snake_case				=		prepare_ref(_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )

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


if __name__ == "__main__":
	A							:					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',
	)

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









from sklearn.metrics import mean_squared_error

import datasets


A							:					List[Any]     						=     '\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'

A							:					Optional[int]     						=     '\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'


A							:					str     						=     '\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        "raw_values" : Returns a full set of errors in case of multioutput input.\n\n        "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric("mse")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {\'mse\': 0.6123724356957945}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric("mse", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mse\': array([0.41666667, 1. ])}\n'






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,							_KWARGS_DESCRIPTION   )
class     UpperCamelCase(  datasets.Metric   ):


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	Union[str, Any]:




											'''simple docstring'''

											return datasets.MetricInfo(
											    description=_DESCRIPTION     ,    citation=_CITATION     ,    inputs_description=_KWARGS_DESCRIPTION     ,    features=datasets.Features(self._get_feature_types()						)     ,    reference_urls=[
											        "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
											    ]     ,    )


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Dict:




											'''simple docstring'''

											if self.config_name == "multilist":
																	return {
																	    "predictions": datasets.Sequence(datasets.Value("float"						)						),
																	    "references": datasets.Sequence(datasets.Value("float"						)						),
																	}
											else:
																	return {
																	    "predictions": datasets.Value("float"						),
																	    "references": datasets.Value("float"						),
																	}






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		str=None     ,    SCREAMING_SNAKE_CASE	:		int="uniform_average"     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=True						)			->	Tuple:




											'''simple docstring'''

											__snake_case				=		mean_squared_error(
											    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    sample_weight=SCREAMING_SNAKE_CASE     ,    multioutput=SCREAMING_SNAKE_CASE     ,    squared=SCREAMING_SNAKE_CASE						)

											return {"mse": mse}





