






import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
							import jax.numpy as jnp

							from transformers.models.distilbert.modeling_flax_distilbert import (
							    FlaxDistilBertForMaskedLM,
							    FlaxDistilBertForMultipleChoice,
							    FlaxDistilBertForQuestionAnswering,
							    FlaxDistilBertForSequenceClassification,
							    FlaxDistilBertForTokenClassification,
							    FlaxDistilBertModel,
							)
class 						lowerCamelCase_				(      unittest.TestCase ):







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase=1_3       ,					__lowerCAmelCase=7       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=9_9       ,					__lowerCAmelCase=3_2       ,					__lowerCAmelCase=5       ,					__lowerCAmelCase=4       ,					__lowerCAmelCase=3_7       ,					__lowerCAmelCase="gelu"       ,					__lowerCAmelCase=0.1       ,					__lowerCAmelCase=0.1       ,					__lowerCAmelCase=5_1_2       ,					__lowerCAmelCase=1_6       ,					__lowerCAmelCase=2       ,					__lowerCAmelCase=0.02       ,					__lowerCAmelCase=4       ,					):
								"""simple docstring"""
								__magic_name__		:str        =      parent
								__magic_name__		:Tuple        =      batch_size
								__magic_name__		:Optional[int]        =      seq_length
								__magic_name__		:Dict        =      is_training
								__magic_name__		:int        =      use_attention_mask
								__magic_name__		:List[Any]        =      use_token_type_ids
								__magic_name__		:Dict        =      use_labels
								__magic_name__		:int        =      vocab_size
								__magic_name__		:List[Any]        =      hidden_size
								__magic_name__		:List[Any]        =      num_hidden_layers
								__magic_name__		:int        =      num_attention_heads
								__magic_name__		:Tuple        =      intermediate_size
								__magic_name__		:str        =      hidden_act
								__magic_name__		:Optional[int]        =      hidden_dropout_prob
								__magic_name__		:Any        =      attention_probs_dropout_prob
								__magic_name__		:int        =      max_position_embeddings
								__magic_name__		:str        =      type_vocab_size
								__magic_name__		:List[str]        =      type_sequence_label_size
								__magic_name__		:Union[str, Any]        =      initializer_range
								__magic_name__		:Union[str, Any]        =      num_choices







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      ids_tensor([self.batch_size, self.seq_length]       ,					self.vocab_size     )

								__magic_name__		:Optional[int]        =      None
								if self.use_attention_mask:
												__magic_name__		:Optional[int]        =      random_attention_mask([self.batch_size, self.seq_length]     )

								__magic_name__		:Optional[int]        =      DistilBertConfig(
								    vocab_size=self.vocab_size       ,					dim=self.hidden_size       ,					n_layers=self.num_hidden_layers       ,					n_heads=self.num_attention_heads       ,					hidden_dim=self.intermediate_size       ,					hidden_act=self.hidden_act       ,					dropout=self.hidden_dropout_prob       ,					attention_dropout=self.attention_probs_dropout_prob       ,					max_position_embeddings=self.max_position_embeddings       ,					initializer_range=self.initializer_range       ,					tie_weights_=__UpperCamelCase       ,					)

								return config, input_ids, attention_mask







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:str        =      self.prepare_config_and_inputs()
								__magic_name__     ,				__magic_name__     ,				__magic_name__		:List[str]        =      config_and_inputs
								__magic_name__		:List[str]        =      {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
								return config, inputs_dict







@require_flax
class 						lowerCamelCase_				(      lowerCamelCase			,				unittest.TestCase ):
				a__					       =  (
				    (
				        FlaxDistilBertModel,
				        FlaxDistilBertForMaskedLM,
				        FlaxDistilBertForMultipleChoice,
				        FlaxDistilBertForQuestionAnswering,
				        FlaxDistilBertForSequenceClassification,
				        FlaxDistilBertForTokenClassification,
				        FlaxDistilBertForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      FlaxDistilBertModelTester(self     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								for model_class_name in self.all_model_classes:
												__magic_name__		:str        =      model_class_name.from_pretrained('''distilbert-base-uncased'''     )
												__magic_name__		:int        =      model(np.ones((1, 1)     )     )
												self.assertIsNotNone(__UpperCamelCase     )







@require_flax
class 						lowerCamelCase_				(      unittest.TestCase ):







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      FlaxDistilBertModel.from_pretrained('''distilbert-base-uncased'''     )
								__magic_name__		:Tuple        =      np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]     )
								__magic_name__		:List[Any]        =      np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]     )
								__magic_name__		:Optional[Any]        =      model(__UpperCamelCase       ,					attention_mask=__UpperCamelCase     )[0]
								__magic_name__		:str        =      (1, 1_1, 7_6_8)
								self.assertEqual(output.shape       ,					__UpperCamelCase     )
								__magic_name__		:Any        =      np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]]     )

								self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]       ,					__UpperCamelCase       ,					atol=1E-4     )     )














import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer

def      __lowercase					( snake_case ):







				"""simple docstring"""






				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      __lowercase					( snake_case ):







				"""simple docstring"""






				for char in word:
								__magic_name__		:Union[str, Any]        =      ord(snake_case )
								if not _is_chinese_char(snake_case ):
												return 0
				return 1

def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Any        =      set()

				for token in tokens:
								__magic_name__		:Dict        =      len(snake_case ) > 1 and is_chinese(snake_case )
								if chinese_word:
												word_set.add(snake_case )
				__magic_name__		:str        =      list(snake_case )
				return word_list

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				if not chinese_word_set:
								return bert_tokens
				__magic_name__		:int        =      max([len(snake_case ) for w in chinese_word_set] )

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

def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Optional[int]        =      []

				for i in range(0,  len(snake_case ),  1_0_0 ):
								__magic_name__		:List[str]        =      ltp_tokenizer.seg(lines[i : i + 1_0_0] )[0]
								__magic_name__		:List[str]        =      [get_chinese_word(snake_case ) for r in res]
								ltp_res.extend(snake_case )
				assert len(snake_case ) == len(snake_case )

				__magic_name__		:Union[str, Any]        =      []
				for i in range(0,  len(snake_case ),  1_0_0 ):
								__magic_name__		:str        =      bert_tokenizer(lines[i : i + 1_0_0],  add_special_tokens=snake_case,  truncation=snake_case,  max_length=5_1_2 )
								bert_res.extend(res['''input_ids'''] )
				assert len(snake_case ) == len(snake_case )

				__magic_name__		:Union[str, Any]        =      []
				for input_ids, chinese_word in zip(snake_case,  snake_case ):
								__magic_name__		:Any        =      []
								for id in input_ids:
												__magic_name__		:List[Any]        =      bert_tokenizer._convert_id_to_token(snake_case )
												input_tokens.append(snake_case )
								__magic_name__		:Dict        =      add_sub_symbol(snake_case,  snake_case )
								__magic_name__		:Optional[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] == "##":
																__magic_name__		:Optional[int]        =      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 ):







				"""simple docstring"""






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

				__magic_name__		:Optional[int]        =      prepare_ref(snake_case,  snake_case,  snake_case )

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      :			Tuple							=		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""")

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






