






"""simple docstring"""







import unittest

from transformers import MarkupLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property

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 (
      MarkupLMForQuestionAnswering,
      MarkupLMForSequenceClassification,
      MarkupLMForTokenClassification,
      MarkupLMModel,
  )

# TODO check dependencies
from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer







class 					lowerCamelCase__					:






    def __init__( self	,A	,A=13	,A=7	,A=True	,A=True	,A=True	,A=True	,A=99	,A=32	,A=5	,A=4	,A=37	,A="gelu"	,A=0.1	,A=0.1	,A=512	,A=16	,A=2	,A=0.02	,A=3	,A=None	,A=20	,A=30	,A=2	,A=2	,A=10	,):
          UpperCAmelCase							=       parent
          UpperCAmelCase							=       batch_size
          UpperCAmelCase							=       seq_length
          UpperCAmelCase							=       is_training
          UpperCAmelCase							=       use_input_mask
          UpperCAmelCase							=       use_token_type_ids
          UpperCAmelCase							=       use_labels
          UpperCAmelCase							=       vocab_size
          UpperCAmelCase							=       hidden_size
          UpperCAmelCase							=       num_hidden_layers
          UpperCAmelCase							=       num_attention_heads
          UpperCAmelCase							=       intermediate_size
          UpperCAmelCase							=       hidden_act
          UpperCAmelCase							=       hidden_dropout_prob
          UpperCAmelCase							=       attention_probs_dropout_prob
          UpperCAmelCase							=       max_position_embeddings
          UpperCAmelCase							=       type_vocab_size
          UpperCAmelCase							=       type_sequence_label_size
          UpperCAmelCase							=       initializer_range
          UpperCAmelCase							=       num_labels
          UpperCAmelCase							=       scope
          UpperCAmelCase							=       max_xpath_tag_unit_embeddings
          UpperCAmelCase							=       max_xpath_subs_unit_embeddings
          UpperCAmelCase							=       tag_pad_id
          UpperCAmelCase							=       subs_pad_id
          UpperCAmelCase							=       max_depth






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       ids_tensor([self.batch_size, self.seq_length]	,self.vocab_size					)

          UpperCAmelCase							=       ids_tensor(
              [self.batch_size, self.seq_length, self.max_depth]	,self.max_xpath_tag_unit_embeddings					)

          UpperCAmelCase							=       ids_tensor(
              [self.batch_size, self.seq_length, self.max_depth]	,self.max_xpath_subs_unit_embeddings					)

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

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

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

          UpperCAmelCase							=       self.get_config()

          return (
              config,
              input_ids,
              xpath_tags_seq,
              xpath_subs_seq,
              token_type_ids,
              input_mask,
              sequence_labels,
              token_labels,
          )






    def 				_UpperCamelCase							( self					):
          return MarkupLMConfig(
              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	,max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings	,max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings	,tag_pad_id=self.tag_pad_id	,subs_pad_id=self.subs_pad_id	,max_depth=self.max_depth	,)






    def 				_UpperCamelCase							( self	,A	,A	,A	,A	,A	,A	,A	,A	,):
          UpperCAmelCase							=       MarkupLMModel(config=A					)
          model.to(A					)
          model.eval()
          print("""Configs:"""	,model.config.tag_pad_id	,model.config.subs_pad_id					)
          UpperCAmelCase							=       model(A	,attention_mask=A	,token_type_ids=A					)
          UpperCAmelCase							=       model(A	,token_type_ids=A					)
          UpperCAmelCase							=       model(A					)
          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	,A	,A	,A	,A	,A	,A	,A	,A	,):
          UpperCAmelCase							=       self.num_labels
          UpperCAmelCase							=       MarkupLMForSequenceClassification(A					)
          model.to(A					)
          model.eval()
          UpperCAmelCase							=       model(
              A	,xpath_tags_seq=A	,xpath_subs_seq=A	,attention_mask=A	,token_type_ids=A	,labels=A	,)
          self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.num_labels)					)






    def 				_UpperCamelCase							( self	,A	,A	,A	,A	,A	,A	,A	,A	,):
          UpperCAmelCase							=       self.num_labels
          UpperCAmelCase							=       MarkupLMForTokenClassification(config=A					)
          model.to(A					)
          model.eval()
          UpperCAmelCase							=       model(
              A	,xpath_tags_seq=A	,xpath_subs_seq=A	,attention_mask=A	,token_type_ids=A	,labels=A	,)
          self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.seq_length, self.num_labels)					)






    def 				_UpperCamelCase							( self	,A	,A	,A	,A	,A	,A	,A	,A	,):
          UpperCAmelCase							=       MarkupLMForQuestionAnswering(config=A					)
          model.to(A					)
          model.eval()
          UpperCAmelCase							=       model(
              A	,xpath_tags_seq=A	,xpath_subs_seq=A	,attention_mask=A	,token_type_ids=A	,start_positions=A	,end_positions=A	,)
          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					):
          UpperCAmelCase							=       self.prepare_config_and_inputs()
          (
              (
              UpperCAmelCase
          )			,					(
              UpperCAmelCase
          )			,					(
              UpperCAmelCase
          )			,					(
              UpperCAmelCase
          )			,					(
              UpperCAmelCase
          )			,					(
              UpperCAmelCase
          )			,					(
              UpperCAmelCase
          )			,					(
              UpperCAmelCase
          )			,					
          )							=       config_and_inputs
          UpperCAmelCase							=       {
              """input_ids""": input_ids,
              """xpath_tags_seq""": xpath_tags_seq,
              """xpath_subs_seq""": xpath_subs_seq,
              """token_type_ids""": token_type_ids,
              """attention_mask""": input_mask,
          }
          return config, inputs_dict







@require_torch
class 					lowerCamelCase__					(       snake_case    ,     snake_case    ,     unittest.TestCase ):
    SCREAMING_SNAKE_CASE		=			(
        (
            MarkupLMModel,
            MarkupLMForSequenceClassification,
            MarkupLMForTokenClassification,
            MarkupLMForQuestionAnswering,
        )
        if is_torch_available()
        else None
    )
    SCREAMING_SNAKE_CASE		=			(
        {
            '''feature-extraction''': MarkupLMModel,
            '''question-answering''': MarkupLMForQuestionAnswering,
            '''text-classification''': MarkupLMForSequenceClassification,
            '''token-classification''': MarkupLMForTokenClassification,
            '''zero-shot''': MarkupLMForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )






    def 				_UpperCamelCase							( self	,A	,A	,A	,A	,A					):
          # ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
          # (batch of pretokenized examples).
          return True






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       MarkupLMModelTester(self					)
          UpperCAmelCase							=       ConfigTester(self	,config_class=A	,hidden_size=37					)






    def 				_UpperCamelCase							( self					):
          self.config_tester.run_common_tests()






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A					)






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*A					)






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*A					)






    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*A					)







def _a (		):







      """simple docstring"""






      UpperCAmelCase							=       """
    <!DOCTYPE html>
    <html>
    <head>
    <title>Page Title</title>
    </head>
    <body>

    <h1>This is a Heading</h1>
    <p>This is a paragraph.</p>

    </body>
    </html>
    """

      return html_string







@require_torch
class 					lowerCamelCase__					(       unittest.TestCase ):






    @cached_property
    def 				_UpperCamelCase							( self					):
          # TODO use from_pretrained here
          UpperCAmelCase							=       MarkupLMFeatureExtractor()
          UpperCAmelCase							=       MarkupLMTokenizer.from_pretrained("""microsoft/markuplm-base"""					)

          return MarkupLMProcessor(A	,A					)






    @slow
    def 				_UpperCamelCase							( self					):
          UpperCAmelCase							=       MarkupLMModel.from_pretrained("""microsoft/markuplm-base"""					).to(A					)

          UpperCAmelCase							=       self.default_processor

          UpperCAmelCase							=       processor(prepare_html_string()	,return_tensors="""pt"""					)
          UpperCAmelCase							=       inputs.to(A					)

          # forward pass
          with torch.no_grad():
                UpperCAmelCase							=       model(**A					)

          # verify the last hidden states
          UpperCAmelCase							=       torch.Size([1, 14, 768]					)
          self.assertEqual(outputs.last_hidden_state.shape	,A					)

          UpperCAmelCase							=       torch.tensor(
              [[0.0675, -0.0052, 0.5001], [-0.2281, 0.0802, 0.2192], [-0.0583, -0.3311, 0.1185]]					).to(A					)

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











"""simple docstring"""







import cva
import numpy as np

def _a (		_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      try:
            return int(image[x_coordinate][y_coordinate] >= center							)
      except (IndexError, TypeError):
            return 0

def _a (		_snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      UpperCAmelCase							=       image[x_coordinate][y_coordinate]
      UpperCAmelCase							=       [1, 2, 4, 8, 16, 32, 64, 128]

      # skip get_neighbors_pixel if center is null
      if center is None:
            return 0

      # Starting from the top right, assigning value to pixels clockwise
      UpperCAmelCase							=       [
          get_neighbors_pixel(_snake_case      ,       x_coordinate - 1      ,       y_coordinate + 1      ,       _snake_case							),
          get_neighbors_pixel(_snake_case      ,       _snake_case      ,       y_coordinate + 1      ,       _snake_case							),
          get_neighbors_pixel(_snake_case      ,       x_coordinate - 1      ,       _snake_case      ,       _snake_case							),
          get_neighbors_pixel(_snake_case      ,       x_coordinate + 1      ,       y_coordinate + 1      ,       _snake_case							),
          get_neighbors_pixel(_snake_case      ,       x_coordinate + 1      ,       _snake_case      ,       _snake_case							),
          get_neighbors_pixel(_snake_case      ,       x_coordinate + 1      ,       y_coordinate - 1      ,       _snake_case							),
          get_neighbors_pixel(_snake_case      ,       _snake_case      ,       y_coordinate - 1      ,       _snake_case							),
          get_neighbors_pixel(_snake_case      ,       x_coordinate - 1      ,       y_coordinate - 1      ,       _snake_case							),
      ]

      # Converting the binary value to decimal.
      return sum(
          binary_value * power for binary_value, power in zip(_snake_case      ,       _snake_case							)							)


if __name__ == "__main__":
  # Reading the image and converting it to grayscale.
  _UpperCamelCase     			=  cva.imread(
      """digital_image_processing/image_data/lena.jpg""", cva.IMREAD_GRAYSCALE
  )

  # Create a numpy array as the same height and width of read image
  _UpperCamelCase     			=  np.zeros((image.shape[0], image.shape[1]))

  # Iterating through the image and calculating the
  # local binary pattern value for each pixel.
  for i in range(0, image.shape[0]):
    for j in range(0, image.shape[1]):
      _UpperCamelCase     			=  local_binary_value(image, i, j)

  cva.imshow("""local binary pattern""", lbp_image)
  cva.waitKey(0)
  cva.destroyAllWindows()



