

'''simple docstring'''






import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer


def 	_A  (					A__   ):






 """simple docstring"""



 if (
     (cp >= 0x4e00 and cp <= 0x9fff)
     or (cp >= 0x3400 and cp <= 0x4dbf)  #
     or (cp >= 0x2_0000 and cp <= 0x2_a6df)  #
     or (cp >= 0x2_a700 and cp <= 0x2_b73f)  #
     or (cp >= 0x2_b740 and cp <= 0x2_b81f)  #
     or (cp >= 0x2_b820 and cp <= 0x2_ceaf)  #
     or (cp >= 0xf900 and cp <= 0xfaff)
     or (cp >= 0x2_f800 and cp <= 0x2_fa1f)  #
 ):  #
  return True

 return False


def 	_A  (					A__   ):






 """simple docstring"""



 for char in word:
  __lowercase													=				ord(A__   )
  if not _is_chinese_char(A__   ):
   return 0
 return 1


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				set()

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


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



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

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


def 	_A  (					A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				[]

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

 __lowercase													=				[]
 for i in range(0  ,       len(A__   )  ,       100   ):
  __lowercase													=				bert_tokenizer(lines[i : i + 100]  ,       add_special_tokens=A__  ,       truncation=A__  ,       max_length=512   )
  bert_res.extend(res['''input_ids''']   )
 assert len(A__   ) == len(A__   )

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

 __lowercase													=				prepare_ref(A__  ,       A__  ,       A__   )

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


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)




'''simple docstring'''






import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import CvtForImageClassification, CvtModel
 from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   __lowercase													=				self.config_class(**self.inputs_dict    )
   self.parent.assertTrue(hasattr(lowercase__      ,'''embed_dim'''    )    )
   self.parent.assertTrue(hasattr(lowercase__      ,'''num_heads'''    )    )






class     lowercase_			:




  """simple docstring"""




  def __init__(   self     :  Optional[Any]      ,lowercase__     :  Optional[int]      ,lowercase__     :  Optional[int]=1_3      ,lowercase__     :  List[Any]=6_4      ,lowercase__     :  Optional[int]=3      ,lowercase__     :  Dict=[1_6, 4_8, 9_6]      ,lowercase__     :  Optional[Any]=[1, 3, 6]      ,lowercase__     :  Tuple=[1, 2, 1_0]      ,lowercase__     :  Optional[int]=[7, 3, 3]      ,lowercase__     :  str=[4, 2, 2]      ,lowercase__     :  Dict=[2, 1, 1]      ,lowercase__     :  Tuple=[2, 2, 2]      ,lowercase__     :  Tuple=[False, False, True]      ,lowercase__     :  int=[0.0, 0.0, 0.0]      ,lowercase__     :  str=0.0_2      ,lowercase__     :  Union[str, Any]=1e-1_2      ,lowercase__     :  Optional[int]=True      ,lowercase__     :  Union[str, Any]=True      ,lowercase__     :  Optional[Any]=2      ,):
   __lowercase													=				parent
   __lowercase													=				batch_size
   __lowercase													=				image_size
   __lowercase													=				patch_sizes
   __lowercase													=				patch_stride
   __lowercase													=				patch_padding
   __lowercase													=				is_training
   __lowercase													=				use_labels
   __lowercase													=				num_labels
   __lowercase													=				num_channels
   __lowercase													=				embed_dim
   __lowercase													=				num_heads
   __lowercase													=				stride_kv
   __lowercase													=				depth
   __lowercase													=				cls_token
   __lowercase													=				attention_drop_rate
   __lowercase													=				initializer_range
   __lowercase													=				layer_norm_eps






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

   __lowercase													=				None
   if self.use_labels:
    __lowercase													=				ids_tensor([self.batch_size]      ,self.num_labels    )

   __lowercase													=				self.get_config()
   return config, pixel_values, labels






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   return CvtConfig(
       image_size=self.image_size      ,num_labels=self.num_labels      ,num_channels=self.num_channels      ,embed_dim=self.embed_dim      ,num_heads=self.num_heads      ,patch_sizes=self.patch_sizes      ,patch_padding=self.patch_padding      ,patch_stride=self.patch_stride      ,stride_kv=self.stride_kv      ,depth=self.depth      ,cls_token=self.cls_token      ,attention_drop_rate=self.attention_drop_rate      ,initializer_range=self.initializer_range      ,)






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict      ,lowercase__     :  List[str]      ,lowercase__     :  Any      ,lowercase__     :  List[str]    ):
   __lowercase													=				CvtModel(config=lowercase__    )
   model.to(lowercase__    )
   model.eval()
   __lowercase													=				model(lowercase__    )
   __lowercase													=				(self.image_size, self.image_size)
   __lowercase						,			__lowercase													=				image_size[0], image_size[1]
   for i in range(len(self.depth    )    ):
    __lowercase													=				floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1    )
    __lowercase													=				floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1    )
   self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.embed_dim[-1], height, width)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict      ,lowercase__     :  Any      ,lowercase__     :  List[Any]      ,lowercase__     :  Dict    ):
   __lowercase													=				self.num_labels
   __lowercase													=				CvtForImageClassification(lowercase__    )
   model.to(lowercase__    )
   model.eval()
   __lowercase													=				model(lowercase__      ,labels=lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_labels)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				self.prepare_config_and_inputs()
   __lowercase						,			__lowercase						,			__lowercase													=				config_and_inputs
   __lowercase													=				{'''pixel_values''': pixel_values}
   return config, inputs_dict






@require_torch
class     lowercase_			(lowerCamelCase__       , lowerCamelCase__       , unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Any           =    (CvtModel, CvtForImageClassification) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE					:						Optional[int]           =    (
      {'feature-extraction': CvtModel, 'image-classification': CvtForImageClassification}
      if is_torch_available()
      else {}
  )

  SCREAMING_SNAKE_CASE					:						Optional[int]           =    False
  SCREAMING_SNAKE_CASE					:						Union[str, Any]           =    False
  SCREAMING_SNAKE_CASE					:						int           =    False
  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    False
  SCREAMING_SNAKE_CASE					:						str           =    False






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				CvtModelTester(self    )
   __lowercase													=				ConfigTester(self      ,config_class=lowercase__      ,has_text_modality=lowercase__      ,hidden_size=3_7    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   self.create_and_test_config_common_properties()
   self.config_tester.create_and_test_config_to_json_string()
   self.config_tester.create_and_test_config_to_json_file()
   self.config_tester.create_and_test_config_from_and_save_pretrained()
   self.config_tester.create_and_test_config_with_num_labels()
   self.config_tester.check_config_can_be_init_without_params()
   self.config_tester.check_config_arguments_init()






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   return






  @unittest.skip(reason='''Cvt does not output attentions'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   pass






  @unittest.skip(reason='''Cvt does not use inputs_embeds'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   pass






  @unittest.skip(reason='''Cvt does not support input and output embeddings'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   pass






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __lowercase													=				model_class(lowercase__    )
    __lowercase													=				inspect.signature(model.forward    )
    # signature.parameters is an OrderedDict => so arg_names order is deterministic
    __lowercase													=				[*signature.parameters.keys()]

    __lowercase													=				['''pixel_values''']
    self.assertListEqual(arg_names[:1]      ,lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   def check_hidden_states_output(lowercase__     :  Union[str, Any]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  str    ):
    __lowercase													=				model_class(lowercase__    )
    model.to(lowercase__    )
    model.eval()

    with torch.no_grad():
     __lowercase													=				model(**self._prepare_for_class(lowercase__      ,lowercase__    )    )

    __lowercase													=				outputs.hidden_states

    __lowercase													=				len(self.model_tester.depth    )
    self.assertEqual(len(lowercase__    )      ,lowercase__    )

    # verify the first hidden states (first block)
    self.assertListEqual(
        list(hidden_states[0].shape[-3:]    )      ,[
            self.model_tester.embed_dim[0],
            self.model_tester.image_size // 4,
            self.model_tester.image_size // 4,
        ]      ,)

   __lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __lowercase													=				True
    check_hidden_states_output(lowercase__      ,lowercase__      ,lowercase__    )

    # check that output_hidden_states also work using config
    del inputs_dict["output_hidden_states"]
    __lowercase													=				True

    check_hidden_states_output(lowercase__      ,lowercase__      ,lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*lowercase__    )






  @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   pass






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __lowercase													=				CvtModel.from_pretrained(lowercase__    )
    self.assertIsNotNone(lowercase__    )


def 	_A  (					):






 """simple docstring"""



 __lowercase													=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
 return image






@require_torch
@require_vision
class     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  @cached_property
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(lowercase__    )

   __lowercase													=				self.default_image_processor
   __lowercase													=				prepare_img()
   __lowercase													=				image_processor(images=lowercase__      ,return_tensors='''pt'''    ).to(lowercase__    )

   # forward pass
   with torch.no_grad():
    __lowercase													=				model(**lowercase__    )

   # verify the logits
   __lowercase													=				torch.Size((1, 1_0_0_0)    )
   self.assertEqual(outputs.logits.shape      ,lowercase__    )

   __lowercase													=				torch.tensor([0.9_2_8_5, 0.9_0_1_5, -0.3_1_5_0]    ).to(lowercase__    )

   self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,lowercase__      ,atol=1e-4    )    )

