



"""simple docstring"""






import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer


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


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

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


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

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


def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case	)						->   Optional[int]:
			__lowerCAmelCase      :  Dict												=       []

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

			__lowerCAmelCase      :  Union[str, Any]												=       []
			for i in range(0					,len(__snake_case	)					,100	):
						__lowerCAmelCase      :  Tuple												=       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      :  List[str]												=       []
			for input_ids, chinese_word in zip(__snake_case					,__snake_case	):
						__lowerCAmelCase      :  Union[str, Any]												=       []
						for id in input_ids:
									__lowerCAmelCase      :  Any												=       bert_tokenizer._convert_id_to_token(__snake_case	)
									input_tokens.append(__snake_case	)
						__lowerCAmelCase      :  Any												=       add_sub_symbol(__snake_case					,__snake_case	)
						__lowerCAmelCase      :  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] == "##":
												__lowerCAmelCase      :  Union[str, Any]												=       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	)						->   Tuple:
			# 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      :  Tuple												=       f.readlines()
			__lowerCAmelCase      :  Dict												=       [line.strip() for line in data if len(__snake_case	) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
			__lowerCAmelCase      :  Optional[Any]												=       LTP(args.ltp	)  # faster in GPU device
			__lowerCAmelCase      :  str												=       BertTokenizer.from_pretrained(args.bert	)

			__lowerCAmelCase      :  int												=       prepare_ref(__snake_case					,__snake_case					,__snake_case	)

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


if __name__ == "__main__":
	__snake_case							:   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							:   List[Any]      =    parser.parse_args()
	main(args)




"""simple docstring"""






import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
	import optax
	from flax.training.common_utils import onehot

	from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
	from transformers.models.ta.modeling_flax_ta import shift_tokens_right



@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small")
				__lowerCAmelCase      :  Optional[Any]												=       AutoTokenizer.from_pretrained("google/mt5-small")

				__lowerCAmelCase      :  Tuple												=       tokenizer("Hello there"       ,			return_tensors="np").input_ids
				__lowerCAmelCase      :  Dict												=       tokenizer("Hi I am"       ,			return_tensors="np").input_ids

				__lowerCAmelCase      :  str												=       shift_tokens_right(_SCREAMING_SNAKE_CASE       ,			model.config.pad_token_id       ,			model.config.decoder_start_token_id)

				__lowerCAmelCase      :  Optional[int]												=       model(_SCREAMING_SNAKE_CASE       ,			decoder_input_ids=_SCREAMING_SNAKE_CASE).logits
				__lowerCAmelCase      :  int												=       optax.softmax_cross_entropy(_SCREAMING_SNAKE_CASE       ,			onehot(_SCREAMING_SNAKE_CASE       ,			logits.shape[-1])).mean()

				__lowerCAmelCase      :  List[str]												=       -(labels.shape[-1] * loss.item())

				__lowerCAmelCase      :  str												=       -84.9127
				self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)