



import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
					from transformers.models.bert.modeling_flax_bert import (
					    FlaxBertForMaskedLM,
					    FlaxBertForMultipleChoice,
					    FlaxBertForNextSentencePrediction,
					    FlaxBertForPreTraining,
					    FlaxBertForQuestionAnswering,
					    FlaxBertForSequenceClassification,
					    FlaxBertForTokenClassification,
					    FlaxBertModel,
					)
class      UpperCamelCase (				unittest.TestCase ):







		"""simple docstring"""



		def __init__(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       Dict=1_3						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=7						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=True						,_SCREAMING_SNAKE_CASE		:       Any=True						,_SCREAMING_SNAKE_CASE		:       str=True						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=True						,_SCREAMING_SNAKE_CASE		:       Dict=9_9						,_SCREAMING_SNAKE_CASE		:       Tuple=3_2						,_SCREAMING_SNAKE_CASE		:       str=5						,_SCREAMING_SNAKE_CASE		:       List[str]=4						,_SCREAMING_SNAKE_CASE		:       List[str]=3_7						,_SCREAMING_SNAKE_CASE		:       Optional[int]="gelu"						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=0.1						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=0.1						,_SCREAMING_SNAKE_CASE		:       Optional[int]=5_1_2						,_SCREAMING_SNAKE_CASE		:       List[Any]=1_6						,_SCREAMING_SNAKE_CASE		:       str=2						,_SCREAMING_SNAKE_CASE		:       Dict=0.02						,_SCREAMING_SNAKE_CASE		:       Any=4						,)    ->   Tuple:


						'''simple docstring'''
						A	 =      parent
						A	 =      batch_size
						A	 =      seq_length
						A	 =      is_training
						A	 =      use_attention_mask
						A	 =      use_token_type_ids
						A	 =      use_labels
						A	 =      vocab_size
						A	 =      hidden_size
						A	 =      num_hidden_layers
						A	 =      num_attention_heads
						A	 =      intermediate_size
						A	 =      hidden_act
						A	 =      hidden_dropout_prob
						A	 =      attention_probs_dropout_prob
						A	 =      max_position_embeddings
						A	 =      type_vocab_size
						A	 =      type_sequence_label_size
						A	 =      initializer_range
						A	 =      num_choices
		def       A(  self		:       Tuple		)    ->   List[Any]:


						'''simple docstring'''
						A	 =      ids_tensor([self.batch_size, self.seq_length]						,self.vocab_size		)

						A	 =      None
						if self.use_attention_mask:
										A	 =      random_attention_mask([self.batch_size, self.seq_length]		)

						A	 =      None
						if self.use_token_type_ids:
										A	 =      ids_tensor([self.batch_size, self.seq_length]						,self.type_vocab_size		)

						A	 =      BertConfig(
						    vocab_size=self.vocab_size						,hidden_size=self.hidden_size						,num_hidden_layers=self.num_hidden_layers						,num_attention_heads=self.num_attention_heads						,intermediate_size=self.intermediate_size						,hidden_act=self.hidden_act						,hidden_dropout_prob=self.hidden_dropout_prob						,attention_probs_dropout_prob=self.attention_probs_dropout_prob						,max_position_embeddings=self.max_position_embeddings						,type_vocab_size=self.type_vocab_size						,is_decoder=_SCREAMING_SNAKE_CASE						,initializer_range=self.initializer_range						,)

						return config, input_ids, token_type_ids, attention_mask
		def       A(  self		:       Optional[Any]		)    ->   str:


						'''simple docstring'''
						A	 =      self.prepare_config_and_inputs()
						A ,		A ,		A ,		A	 =      config_and_inputs
						A	 =      {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
						return config, inputs_dict







		def       A(  self		:       List[Any]		)    ->   Optional[int]:


						'''simple docstring'''
						A	 =      self.prepare_config_and_inputs()
						A ,		A ,		A ,		A	 =      config_and_inputs

						A	 =      True
						A	 =      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
						A	 =      ids_tensor([self.batch_size, self.seq_length]						,vocab_size=2		)

						return (
						    config,
						    input_ids,
						    attention_mask,
						    encoder_hidden_states,
						    encoder_attention_mask,
						)


@require_flax
class      UpperCamelCase (				snake_case__			,       unittest.TestCase ):







		"""simple docstring"""



		snake_case    =   True

		snake_case    =   (
		    (
		        FlaxBertModel,
		        FlaxBertForPreTraining,
		        FlaxBertForMaskedLM,
		        FlaxBertForMultipleChoice,
		        FlaxBertForQuestionAnswering,
		        FlaxBertForNextSentencePrediction,
		        FlaxBertForSequenceClassification,
		        FlaxBertForTokenClassification,
		        FlaxBertForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)
		def       A(  self		:       List[Any]		)    ->   Union[str, Any]:


						'''simple docstring'''
						A	 =      FlaxBertModelTester(self		)







		@slow
		def       A(  self		:       List[Any]		)    ->   Optional[int]:


						'''simple docstring'''
						A	 =      FlaxBertModel.from_pretrained('bert-base-cased'		)
						A	 =      model(np.ones((1, 1)		)		)
						self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)













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)





