

'''simple docstring'''



import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer







def   a__			(    a__		):




     """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   a__			(    a__		):




     """simple docstring"""


     for char in word:
          __SCREAMING_SNAKE_CASE      = ord(a__		)
          if not _is_chinese_char(a__		):
               return 0
     return 1







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = set()

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







def   a__			(    a__ ,       a__		):




     """simple docstring"""


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

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







def   a__			(    a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = []

     for i in range(0 ,       len(a__		) ,       1_00		):
          __SCREAMING_SNAKE_CASE      = ltp_tokenizer.pipeline(lines[i : i + 1_00] ,       tasks=["""cws"""]		).cws
          __SCREAMING_SNAKE_CASE      = [get_chinese_word(a__		) for r in res]
          ltp_res.extend(a__		)
     assert len(a__		) == len(a__		)

     __SCREAMING_SNAKE_CASE      = []
     for i in range(0 ,       len(a__		) ,       1_00		):
          __SCREAMING_SNAKE_CASE      = bert_tokenizer(lines[i : i + 1_00] ,       add_special_tokens=a__ ,       truncation=a__ ,       max_length=5_12		)
          bert_res.extend(res["""input_ids"""]		)
     assert len(a__		) == len(a__		)

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

     assert len(a__		) == len(a__		)

     return ref_ids







def   a__			(    a__		):




     """simple docstring"""


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

     __SCREAMING_SNAKE_CASE      = prepare_ref(a__ ,       a__ ,       a__		)

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


if __name__ == "__main__":
  UpperCAmelCase					:			List[Any]       =	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',
  )

  UpperCAmelCase					:			Union[str, Any]       =	parser.parse_args()
  main(args)









'''simple docstring'''



import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      LiltForQuestionAnswering,
      LiltForSequenceClassification,
      LiltForTokenClassification,
      LiltModel,
  )
  from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST







class 		lowerCAmelCase__  :



 """simple docstring"""



 def __init__(						self   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			str=13   ,				__SCREAMING_SNAKE_CASE   :			int=7   ,				__SCREAMING_SNAKE_CASE   :			List[str]=True   ,				__SCREAMING_SNAKE_CASE   :			Any=True   ,				__SCREAMING_SNAKE_CASE   :			str=True   ,				__SCREAMING_SNAKE_CASE   :			str=True   ,				__SCREAMING_SNAKE_CASE   :			Dict=99   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=24   ,				__SCREAMING_SNAKE_CASE   :			Any=2   ,				__SCREAMING_SNAKE_CASE   :			Any=6   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=37   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]="gelu"   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=0.1   ,				__SCREAMING_SNAKE_CASE   :			int=512   ,				__SCREAMING_SNAKE_CASE   :			str=16   ,				__SCREAMING_SNAKE_CASE   :			Any=2   ,				__SCREAMING_SNAKE_CASE   :			List[str]=0.02   ,				__SCREAMING_SNAKE_CASE   :			int=3   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=None   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=1_000   ,				)    ->					int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = parent
      __SCREAMING_SNAKE_CASE      = batch_size
      __SCREAMING_SNAKE_CASE      = seq_length
      __SCREAMING_SNAKE_CASE      = is_training
      __SCREAMING_SNAKE_CASE      = use_input_mask
      __SCREAMING_SNAKE_CASE      = use_token_type_ids
      __SCREAMING_SNAKE_CASE      = use_labels
      __SCREAMING_SNAKE_CASE      = vocab_size
      __SCREAMING_SNAKE_CASE      = hidden_size
      __SCREAMING_SNAKE_CASE      = num_hidden_layers
      __SCREAMING_SNAKE_CASE      = num_attention_heads
      __SCREAMING_SNAKE_CASE      = intermediate_size
      __SCREAMING_SNAKE_CASE      = hidden_act
      __SCREAMING_SNAKE_CASE      = hidden_dropout_prob
      __SCREAMING_SNAKE_CASE      = attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE      = max_position_embeddings
      __SCREAMING_SNAKE_CASE      = type_vocab_size
      __SCREAMING_SNAKE_CASE      = type_sequence_label_size
      __SCREAMING_SNAKE_CASE      = initializer_range
      __SCREAMING_SNAKE_CASE      = num_labels
      __SCREAMING_SNAKE_CASE      = scope
      __SCREAMING_SNAKE_CASE      = range_bbox
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size     )

      __SCREAMING_SNAKE_CASE      = ids_tensor([self.batch_size, self.seq_length, 4]   ,				self.range_bbox     )
      # Ensure that bbox is legal
      for i in range(bbox.shape[0]     ):
           for j in range(bbox.shape[1]     ):
                if bbox[i, j, 3] < bbox[i, j, 1]:
                     __SCREAMING_SNAKE_CASE      = bbox[i, j, 3]
                     __SCREAMING_SNAKE_CASE      = bbox[i, j, 1]
                     __SCREAMING_SNAKE_CASE      = t
                if bbox[i, j, 2] < bbox[i, j, 0]:
                     __SCREAMING_SNAKE_CASE      = bbox[i, j, 2]
                     __SCREAMING_SNAKE_CASE      = bbox[i, j, 0]
                     __SCREAMING_SNAKE_CASE      = t

      __SCREAMING_SNAKE_CASE      = None
      if self.use_input_mask:
           __SCREAMING_SNAKE_CASE      = ids_tensor([self.batch_size, self.seq_length]   ,				vocab_size=2     )

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

      __SCREAMING_SNAKE_CASE      = None
      __SCREAMING_SNAKE_CASE      = None
      if self.use_labels:
           __SCREAMING_SNAKE_CASE      = ids_tensor([self.batch_size]   ,				self.type_sequence_label_size     )
           __SCREAMING_SNAKE_CASE      = ids_tensor([self.batch_size, self.seq_length]   ,				self.num_labels     )

      __SCREAMING_SNAKE_CASE      = self.get_config()

      return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
 def  UpperCAmelCase__						(						self   :			str     )    ->					int:


      """simple docstring"""




      return LiltConfig(
          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   ,				initializer_range=self.initializer_range   ,				)
 def  UpperCAmelCase__						(						self   :			int   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			str   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			int   ,				)    ->					Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = LiltModel(config=__SCREAMING_SNAKE_CASE     )
      model.to(__SCREAMING_SNAKE_CASE     )
      model.eval()
      __SCREAMING_SNAKE_CASE      = model(__SCREAMING_SNAKE_CASE   ,				bbox=__SCREAMING_SNAKE_CASE   ,				attention_mask=__SCREAMING_SNAKE_CASE   ,				token_type_ids=__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = model(__SCREAMING_SNAKE_CASE   ,				bbox=__SCREAMING_SNAKE_CASE   ,				token_type_ids=__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = model(__SCREAMING_SNAKE_CASE   ,				bbox=__SCREAMING_SNAKE_CASE     )

      self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)     )
      self.parent.assertEqual(result.pooler_output.shape   ,				(self.batch_size, self.hidden_size)     )
 def  UpperCAmelCase__						(						self   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			List[Any]   ,				)    ->					List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.num_labels
      __SCREAMING_SNAKE_CASE      = LiltForTokenClassification(config=__SCREAMING_SNAKE_CASE     )
      model.to(__SCREAMING_SNAKE_CASE     )
      model.eval()
      __SCREAMING_SNAKE_CASE      = model(
          __SCREAMING_SNAKE_CASE   ,				bbox=__SCREAMING_SNAKE_CASE   ,				attention_mask=__SCREAMING_SNAKE_CASE   ,				token_type_ids=__SCREAMING_SNAKE_CASE   ,				labels=__SCREAMING_SNAKE_CASE     )
      self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.num_labels)     )
 def  UpperCAmelCase__						(						self   :			int   ,				__SCREAMING_SNAKE_CASE   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			Any   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]   ,				)    ->					Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = LiltForQuestionAnswering(config=__SCREAMING_SNAKE_CASE     )
      model.to(__SCREAMING_SNAKE_CASE     )
      model.eval()
      __SCREAMING_SNAKE_CASE      = model(
          __SCREAMING_SNAKE_CASE   ,				bbox=__SCREAMING_SNAKE_CASE   ,				attention_mask=__SCREAMING_SNAKE_CASE   ,				token_type_ids=__SCREAMING_SNAKE_CASE   ,				start_positions=__SCREAMING_SNAKE_CASE   ,				end_positions=__SCREAMING_SNAKE_CASE   ,				)
      self.parent.assertEqual(result.start_logits.shape   ,				(self.batch_size, self.seq_length)     )
      self.parent.assertEqual(result.end_logits.shape   ,				(self.batch_size, self.seq_length)     )


 def  UpperCAmelCase__						(						self   :			Dict     )    ->					str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.prepare_config_and_inputs()
      (
          (
          __SCREAMING_SNAKE_CASE
      )  ,     (
          __SCREAMING_SNAKE_CASE
      )  ,     (
          __SCREAMING_SNAKE_CASE
      )  ,     (
          __SCREAMING_SNAKE_CASE
      )  ,     (
          __SCREAMING_SNAKE_CASE
      )  ,     (
          __SCREAMING_SNAKE_CASE
      )  ,     (
          __SCREAMING_SNAKE_CASE
      )  ,     
      )      = config_and_inputs
      __SCREAMING_SNAKE_CASE      = {
          """input_ids""": input_ids,
          """bbox""": bbox,
          """token_type_ids""": token_type_ids,
          """attention_mask""": input_mask,
      }
      return config, inputs_dict







@require_torch
class 		lowerCAmelCase__  ( a					,   a					,   a					,   unittest.TestCase   ):



 """simple docstring"""



 lowerCAmelCase__       = (
     (
         LiltModel,
         LiltForSequenceClassification,
         LiltForTokenClassification,
         LiltForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 lowerCAmelCase__       = (
     {
         "feature-extraction": LiltModel,
         "question-answering": LiltForQuestionAnswering,
         "text-classification": LiltForSequenceClassification,
         "token-classification": LiltForTokenClassification,
         "zero-shot": LiltForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 lowerCAmelCase__       = False
 lowerCAmelCase__       = False
 def  UpperCAmelCase__						(						self   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			Any   ,				__SCREAMING_SNAKE_CASE   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			str   ,				__SCREAMING_SNAKE_CASE   :			int     )    ->					str:


      """simple docstring"""




      return True
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = LiltModelTester(self     )
      __SCREAMING_SNAKE_CASE      = ConfigTester(self   ,				config_class=__SCREAMING_SNAKE_CASE   ,				hidden_size=37     )
 def  UpperCAmelCase__						(						self   :			int     )    ->					Any:


      """simple docstring"""




      self.config_tester.run_common_tests()
 def  UpperCAmelCase__						(						self   :			Optional[Any]     )    ->					List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[str]     )    ->					Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
           __SCREAMING_SNAKE_CASE      = type
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			int     )    ->					List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Any     )    ->					List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE     )


 @slow
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					int:


      """simple docstring"""




      for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __SCREAMING_SNAKE_CASE      = LiltModel.from_pretrained(__SCREAMING_SNAKE_CASE     )
           self.assertIsNotNone(__SCREAMING_SNAKE_CASE     )







@require_torch
@slow
class 		lowerCAmelCase__  ( unittest.TestCase   ):



 """simple docstring"""



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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = LiltModel.from_pretrained("""SCUT-DLVCLab/lilt-roberta-en-base"""     ).to(__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = torch.tensor([[1, 2]]   ,				device=__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]   ,				device=__SCREAMING_SNAKE_CASE     )

      # forward pass
      with torch.no_grad():
           __SCREAMING_SNAKE_CASE      = model(input_ids=__SCREAMING_SNAKE_CASE   ,				bbox=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = torch.Size([1, 2, 768]     )
      __SCREAMING_SNAKE_CASE      = torch.tensor(
          [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]   ,				device=__SCREAMING_SNAKE_CASE   ,				)

      self.assertTrue(outputs.last_hidden_state.shape   ,				__SCREAMING_SNAKE_CASE     )
      self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]   ,				__SCREAMING_SNAKE_CASE   ,				atol=1E-3     )     )






