



"""simple docstring"""






import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer


def      _lowercase							(    __snake_case      )		->  Any:
			# 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 >= 0X20000 and cp <= 0X2a6df)  #
			    or (cp >= 0X2a700 and cp <= 0X2b73f)  #
			    or (cp >= 0X2b740 and cp <= 0X2b81f)  #
			    or (cp >= 0X2b820 and cp <= 0X2ceaf)  #
			    or (cp >= 0Xf900 and cp <= 0Xfaff)
			    or (cp >= 0X2f800 and cp <= 0X2fa1f)  #
			):  #
						return True

			return False


def      _lowercase							(    __snake_case      )		->  str:
			# word like '180' or '身高' or '神'
			for char in word:
						__lowerCAmelCase :    Optional[int]				=			ord(__snake_case      )
						if not _is_chinese_char(__snake_case      ):
									return 0
			return 1


def      _lowercase							(    __snake_case      )		->  Dict:
			__lowerCAmelCase :    int				=			set()

			for token in tokens:
						__lowerCAmelCase :    Dict				=			len(__snake_case      ) > 1 and is_chinese(__snake_case      )
						if chinese_word:
									word_set.add(__snake_case      )
			__lowerCAmelCase :    List[str]				=			list(__snake_case      )
			return word_list


def      _lowercase							(    __snake_case   ,__snake_case      )		->  List[str]:
			if not chinese_word_set:
						return bert_tokens
			__lowerCAmelCase :    Any				=			max([len(__snake_case      ) for w in chinese_word_set]      )

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


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case      )		->  List[str]:
			__lowerCAmelCase :    Dict				=			[]

			for i in range(0   ,len(__snake_case      )   ,100      ):
						__lowerCAmelCase :    Dict				=			ltp_tokenizer.seg(lines[i : i + 100]      )[0]
						__lowerCAmelCase :    List[str]				=			[get_chinese_word(__snake_case      ) for r in res]
						ltp_res.extend(__snake_case      )
			assert len(__snake_case      ) == len(__snake_case      )

			__lowerCAmelCase :    List[str]				=			[]
			for i in range(0   ,len(__snake_case      )   ,100      ):
						__lowerCAmelCase :    str				=			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      )

			__lowerCAmelCase :    Union[str, Any]				=			[]
			for input_ids, chinese_word in zip(__snake_case   ,__snake_case      ):
						__lowerCAmelCase :    Union[str, Any]				=			[]
						for id in input_ids:
									__lowerCAmelCase :    Optional[int]				=			bert_tokenizer._convert_id_to_token(__snake_case      )
									input_tokens.append(__snake_case      )
						__lowerCAmelCase :    Tuple				=			add_sub_symbol(__snake_case   ,__snake_case      )
						__lowerCAmelCase :    str				=			[]
						# 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] == "##":
												__lowerCAmelCase :    Tuple				=			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      )		->  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:
						__lowerCAmelCase :    Any				=			f.readlines()
			__lowerCAmelCase :    Dict				=			[line.strip() for line in data if len(__snake_case      ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
			__lowerCAmelCase :    Dict				=			LTP(args.ltp      )  # faster in GPU device
			__lowerCAmelCase :    Union[str, Any]				=			BertTokenizer.from_pretrained(args.bert      )

			__lowerCAmelCase :    List[Any]				=			prepare_ref(__snake_case   ,__snake_case   ,__snake_case      )

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


if __name__ == "__main__":
	__snake_case				:  Optional[int] =       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')

	__snake_case				:  Optional[int] =       parser.parse_args()
	main(args)




"""simple docstring"""






import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

__snake_case				:  Union[str, Any] =       collections.namedtuple('_Datasets', ['train', 'validation', 'test'])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
__snake_case				:  Any =       'https://storage.googleapis.com/cvdf-datasets/mnist/'


def      _lowercase							(    __snake_case      )		->  List[Any]:
			__lowerCAmelCase :    int				=			numpy.dtype(numpy.uintaa      ).newbyteorder(">"      )
			return numpy.frombuffer(bytestream.read(4      )   ,dtype=__snake_case      )[0]


@deprecated(__snake_case   ,"Please use tf.data to implement this functionality."      )
def      _lowercase							(    __snake_case      )		->  Union[str, Any]:
			print("Extracting"   ,f.name      )
			with gzip.GzipFile(fileobj=__snake_case      ) as bytestream:
						__lowerCAmelCase :    Union[str, Any]				=			_readaa(__snake_case      )
						if magic != 2_051:
									raise ValueError(
									    "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)      )
						__lowerCAmelCase :    List[str]				=			_readaa(__snake_case      )
						__lowerCAmelCase :    Union[str, Any]				=			_readaa(__snake_case      )
						__lowerCAmelCase :    int				=			_readaa(__snake_case      )
						__lowerCAmelCase :    int				=			bytestream.read(rows * cols * num_images      )
						__lowerCAmelCase :    Optional[Any]				=			numpy.frombuffer(__snake_case   ,dtype=numpy.uinta      )
						__lowerCAmelCase :    str				=			data.reshape(__snake_case   ,__snake_case   ,__snake_case   ,1      )
						return data


@deprecated(__snake_case   ,"Please use tf.one_hot on tensors."      )
def      _lowercase							(    __snake_case   ,__snake_case      )		->  Any:
			__lowerCAmelCase :    Union[str, Any]				=			labels_dense.shape[0]
			__lowerCAmelCase :    Optional[int]				=			numpy.arange(__snake_case      ) * num_classes
			__lowerCAmelCase :    int				=			numpy.zeros((num_labels, num_classes)      )
			__lowerCAmelCase :    str				=			1
			return labels_one_hot


@deprecated(__snake_case   ,"Please use tf.data to implement this functionality."      )
def      _lowercase							(    __snake_case   ,__snake_case=False   ,__snake_case=10      )		->  str:
			print("Extracting"   ,f.name      )
			with gzip.GzipFile(fileobj=__snake_case      ) as bytestream:
						__lowerCAmelCase :    List[str]				=			_readaa(__snake_case      )
						if magic != 2_049:
									raise ValueError(
									    "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)      )
						__lowerCAmelCase :    Union[str, Any]				=			_readaa(__snake_case      )
						__lowerCAmelCase :    Union[str, Any]				=			bytestream.read(__snake_case      )
						__lowerCAmelCase :    Dict				=			numpy.frombuffer(__snake_case   ,dtype=numpy.uinta      )
						if one_hot:
									return _dense_to_one_hot(__snake_case   ,__snake_case      )
						return labels



class      A__  :




	'''simple docstring'''





	@deprecated(
	    _SCREAMING_SNAKE_CASE			,     "Please use alternatives such as official/mnist/_DataSet.py"
	    " from tensorflow/models."			,     )
	def __init__(	self:						Dict			,     _SCREAMING_SNAKE_CASE:						Any			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						Any=False			,     _SCREAMING_SNAKE_CASE:						int=False			,     _SCREAMING_SNAKE_CASE:						str=dtypes.floataa			,     _SCREAMING_SNAKE_CASE:						Any=True			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=None			,     )							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase		,      __lowerCAmelCase :    Optional[Any]				=			random_seed.get_seed(_SCREAMING_SNAKE_CASE)
				# If op level seed is not set, use whatever graph level seed is returned
				numpy.random.seed(seeda if seed is None else seeda)
				__lowerCAmelCase :    Optional[Any]				=			dtypes.as_dtype(_SCREAMING_SNAKE_CASE).base_dtype
				if dtype not in (dtypes.uinta, dtypes.floataa):
							raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype)
				if fake_data:
							__lowerCAmelCase :    Tuple				=			1_0000
							__lowerCAmelCase :    Optional[Any]				=			one_hot
				else:
							assert (
							    images.shape[0] == labels.shape[0]
							), F"""images.shape: {images.shape} labels.shape: {labels.shape}"""
							__lowerCAmelCase :    Union[str, Any]				=			images.shape[0]

							# Convert shape from [num examples, rows, columns, depth]
							# to [num examples, rows*columns] (assuming depth == 1)
							if reshape:
										assert images.shape[3] == 1
										__lowerCAmelCase :    List[str]				=			images.reshape(
										    images.shape[0]			,     images.shape[1] * images.shape[2])
							if dtype == dtypes.floataa:
										# Convert from [0, 255] -> [0.0, 1.0].
										__lowerCAmelCase :    Dict				=			images.astype(numpy.floataa)
										__lowerCAmelCase :    int				=			numpy.multiply(_SCREAMING_SNAKE_CASE			,     1.0 / 255.0)
				__lowerCAmelCase :    Optional[Any]				=			images
				__lowerCAmelCase :    int				=			labels
				__lowerCAmelCase :    Optional[Any]				=			0
				__lowerCAmelCase :    Optional[int]				=			0

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			Tuple:

				"""simple docstring"""

				return self._images

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			int:

				"""simple docstring"""

				return self._labels

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			str:

				"""simple docstring"""

				return self._num_examples

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any])							->			List[str]:

				"""simple docstring"""

				return self._epochs_completed







	def 				_SCREAMING_SNAKE_CASE  (	self:						str			,     _SCREAMING_SNAKE_CASE:						List[str]			,     _SCREAMING_SNAKE_CASE:						int=False			,     _SCREAMING_SNAKE_CASE:						List[str]=True)							->			int:

				"""simple docstring"""

				if fake_data:
							__lowerCAmelCase :    Dict				=			[1] * 784
							__lowerCAmelCase :    str				=			[1] + [0] * 9 if self.one_hot else 0
							return (
							    [fake_image for _ in range(_SCREAMING_SNAKE_CASE)],
							    [fake_label for _ in range(_SCREAMING_SNAKE_CASE)],
							)
				__lowerCAmelCase :    Tuple				=			self._index_in_epoch
				# Shuffle for the first epoch
				if self._epochs_completed == 0 and start == 0 and shuffle:
							__lowerCAmelCase :    Any				=			numpy.arange(self._num_examples)
							numpy.random.shuffle(_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase :    Union[str, Any]				=			self.images[perma]
							__lowerCAmelCase :    Dict				=			self.labels[perma]
				# Go to the next epoch
				if start + batch_size > self._num_examples:
							# Finished epoch
							self._epochs_completed += 1
							# Get the rest examples in this epoch
							__lowerCAmelCase :    Tuple				=			self._num_examples - start
							__lowerCAmelCase :    List[str]				=			self._images[start : self._num_examples]
							__lowerCAmelCase :    Tuple				=			self._labels[start : self._num_examples]
							# Shuffle the data
							if shuffle:
										__lowerCAmelCase :    Tuple				=			numpy.arange(self._num_examples)
										numpy.random.shuffle(_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase :    Optional[Any]				=			self.images[perm]
										__lowerCAmelCase :    Dict				=			self.labels[perm]
							# Start next epoch
							__lowerCAmelCase :    str				=			0
							__lowerCAmelCase :    Dict				=			batch_size - rest_num_examples
							__lowerCAmelCase :    str				=			self._index_in_epoch
							__lowerCAmelCase :    Optional[Any]				=			self._images[start:end]
							__lowerCAmelCase :    Optional[Any]				=			self._labels[start:end]
							return (
							    numpy.concatenate((images_rest_part, images_new_part)			,     axis=0),
							    numpy.concatenate((labels_rest_part, labels_new_part)			,     axis=0),
							)
				else:
							self._index_in_epoch += batch_size
							__lowerCAmelCase :    int				=			self._index_in_epoch
							return self._images[start:end], self._labels[start:end]






@deprecated(__snake_case   ,"Please write your own downloading logic."      )
def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case      )		->  Any:
			if not gfile.Exists(__snake_case      ):
						gfile.MakeDirs(__snake_case      )
			__lowerCAmelCase :    Tuple				=			os.path.join(__snake_case   ,__snake_case      )
			if not gfile.Exists(__snake_case      ):
						urllib.request.urlretrieve(__snake_case   ,__snake_case      )  # noqa: S310
						with gfile.GFile(__snake_case      ) as f:
									__lowerCAmelCase :    Union[str, Any]				=			f.size()
						print("Successfully downloaded"   ,__snake_case   ,__snake_case   ,"bytes."      )
			return filepath





@deprecated(
    __snake_case   ,"Please use alternatives such as:" " tensorflow_datasets.load('mnist')"      )
def      _lowercase							(    __snake_case   ,__snake_case=False   ,__snake_case=False   ,__snake_case=dtypes.floataa   ,__snake_case=True   ,__snake_case=5_000   ,__snake_case=None   ,__snake_case=DEFAULT_SOURCE_URL   ,)		->  Tuple:
			if fake_data:

						def fake():
									return _DataSet(
									    []   ,[]   ,fake_data=__snake_case   ,one_hot=__snake_case   ,dtype=__snake_case   ,seed=__snake_case      )

						__lowerCAmelCase :    Union[str, Any]				=			fake()
						__lowerCAmelCase :    Optional[Any]				=			fake()
						__lowerCAmelCase :    List[Any]				=			fake()
						return _Datasets(train=__snake_case   ,validation=__snake_case   ,test=__snake_case      )

			if not source_url:  # empty string check
						__lowerCAmelCase :    Optional[Any]				=			DEFAULT_SOURCE_URL

			__lowerCAmelCase :    Dict				=			"train-images-idx3-ubyte.gz"
			__lowerCAmelCase :    int				=			"train-labels-idx1-ubyte.gz"
			__lowerCAmelCase :    List[str]				=			"t10k-images-idx3-ubyte.gz"
			__lowerCAmelCase :    Any				=			"t10k-labels-idx1-ubyte.gz"

			__lowerCAmelCase :    Any				=			_maybe_download(
			    __snake_case   ,__snake_case   ,source_url + train_images_file      )
			with gfile.Open(__snake_case   ,"rb"      ) as f:
						__lowerCAmelCase :    Union[str, Any]				=			_extract_images(__snake_case      )

			__lowerCAmelCase :    Optional[int]				=			_maybe_download(
			    __snake_case   ,__snake_case   ,source_url + train_labels_file      )
			with gfile.Open(__snake_case   ,"rb"      ) as f:
						__lowerCAmelCase :    Optional[int]				=			_extract_labels(__snake_case   ,one_hot=__snake_case      )

			__lowerCAmelCase :    Optional[int]				=			_maybe_download(
			    __snake_case   ,__snake_case   ,source_url + test_images_file      )
			with gfile.Open(__snake_case   ,"rb"      ) as f:
						__lowerCAmelCase :    List[Any]				=			_extract_images(__snake_case      )

			__lowerCAmelCase :    str				=			_maybe_download(
			    __snake_case   ,__snake_case   ,source_url + test_labels_file      )
			with gfile.Open(__snake_case   ,"rb"      ) as f:
						__lowerCAmelCase :    List[Any]				=			_extract_labels(__snake_case   ,one_hot=__snake_case      )

			if not 0 <= validation_size <= len(__snake_case      ):
						__lowerCAmelCase :    Tuple				=			(
						    "Validation size should be between 0 and "
						    F"""{len(__snake_case      )}. Received: {validation_size}."""
						)
						raise ValueError(__snake_case      )

			__lowerCAmelCase :    Any				=			train_images[:validation_size]
			__lowerCAmelCase :    Any				=			train_labels[:validation_size]
			__lowerCAmelCase :    List[str]				=			train_images[validation_size:]
			__lowerCAmelCase :    Optional[Any]				=			train_labels[validation_size:]

			__lowerCAmelCase :    Dict				=			{"dtype": dtype, "reshape": reshape, "seed": seed}

			__lowerCAmelCase :    str				=			_DataSet(__snake_case   ,__snake_case   ,**__snake_case      )
			__lowerCAmelCase :    Dict				=			_DataSet(__snake_case   ,__snake_case   ,**__snake_case      )
			__lowerCAmelCase :    Union[str, Any]				=			_DataSet(__snake_case   ,__snake_case   ,**__snake_case      )

			return _Datasets(train=__snake_case   ,validation=__snake_case   ,test=__snake_case      )