





'''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 					snake_case					:




	"""simple docstring"""



	def __init__(	self	:							int							,     __A	:							Dict							,     __A	:							Tuple=1_3							,     __A	:							Optional[int]=7							,     __A	:							Optional[int]=True							,     __A	:							List[Any]=True							,     __A	:							Dict=True							,     __A	:							Optional[int]=True							,     __A	:							Optional[int]=9_9							,     __A	:							str=3_2							,     __A	:							Tuple=5							,     __A	:							Dict=4							,     __A	:							Any=3_7							,     __A	:							List[Any]="gelu"							,     __A	:							Tuple=0.1							,     __A	:							Dict=0.1							,     __A	:							int=5_1_2							,     __A	:							List[str]=1_6							,     __A	:							Dict=2							,     __A	:							Tuple=0.02							,     __A	:							List[str]=3							,     __A	:							int=None							,     __A	:							Tuple=2_0							,     __A	:							List[str]=3_0							,     __A	:							Tuple=2							,     __A	:							Optional[int]=2							,     __A	:							Any=1_0							,     ):
								__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 	_lowerCamelCase (	self	:							List[Any]     ):
								__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 	_lowerCamelCase (	self	:							Tuple     ):
								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 	_lowerCamelCase (	self	:							Union[str, Any]							,     __A	:							Any							,     __A	:							Dict							,     __A	:							Union[str, Any]							,     __A	:							Any							,     __A	:							Dict							,     __A	:							Optional[Any]							,     __A	:							Any							,     __A	:							Tuple							,     ):
								__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 	_lowerCamelCase (	self	:							Any							,     __A	:							Tuple							,     __A	:							Tuple							,     __A	:							Optional[int]							,     __A	:							Any							,     __A	:							Union[str, Any]							,     __A	:							Any							,     __A	:							int							,     __A	:							List[Any]							,     ):
								__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 	_lowerCamelCase (	self	:							Optional[Any]							,     __A	:							Optional[int]							,     __A	:							Dict							,     __A	:							List[Any]							,     __A	:							Optional[int]							,     __A	:							List[str]							,     __A	:							Any							,     __A	:							Optional[Any]							,     __A	:							Optional[int]							,     ):
								__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 	_lowerCamelCase (	self	:							List[Any]							,     __A	:							List[str]							,     __A	:							int							,     __A	:							List[str]							,     __A	:							Any							,     __A	:							Optional[int]							,     __A	:							Any							,     __A	:							Union[str, Any]							,     __A	:							Optional[Any]							,     ):
								__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 	_lowerCamelCase (	self	:							List[str]     ):
								__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 					snake_case					(   __lowerCamelCase      ,	__lowerCamelCase      ,	unittest.TestCase	):




	"""simple docstring"""



	SCREAMING_SNAKE_CASE_     : List[str]      		=(
	    (
	        MarkupLMModel,
	        MarkupLMForSequenceClassification,
	        MarkupLMForTokenClassification,
	        MarkupLMForQuestionAnswering,
	    )
	    if is_torch_available()
	    else None
	)
	SCREAMING_SNAKE_CASE_     : Optional[int]      		=(
	    {
	        "feature-extraction": MarkupLMModel,
	        "question-answering": MarkupLMForQuestionAnswering,
	        "text-classification": MarkupLMForSequenceClassification,
	        "token-classification": MarkupLMForTokenClassification,
	        "zero-shot": MarkupLMForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)






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






	def 	_lowerCamelCase (	self	:							int     ):
								__UpperCamelCase	=  MarkupLMModelTester(self     )
								__UpperCamelCase	=  ConfigTester(self							,     config_class=__A							,     hidden_size=3_7     )






	def 	_lowerCamelCase (	self	:							Union[str, Any]     ):
								self.config_tester.run_common_tests()






	def 	_lowerCamelCase (	self	:							Optional[int]     ):
								__UpperCamelCase	=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__A     )






	def 	_lowerCamelCase (	self	:							str     ):
								__UpperCamelCase	=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*__A     )






	def 	_lowerCamelCase (	self	:							Optional[int]     ):
								__UpperCamelCase	=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*__A     )






	def 	_lowerCamelCase (	self	:							List[Any]     ):
								__UpperCamelCase	=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*__A     )






def     lowercase__       (       )			->				Optional[Any]:



							"""simple docstring"""






							__UpperCamelCase	=  '\n    <!DOCTYPE html>\n    <html>\n    <head>\n    <title>Page Title</title>\n    </head>\n    <body>\n\n    <h1>This is a Heading</h1>\n    <p>This is a paragraph.</p>\n\n    </body>\n    </html>\n    '

							return html_string


@require_torch
class 					snake_case					(   unittest.TestCase	):




	"""simple docstring"""



	@cached_property
	def 	_lowerCamelCase (	self	:							Any     ):
								# TODO use from_pretrained here
								__UpperCamelCase	=  MarkupLMFeatureExtractor()
								__UpperCamelCase	=  MarkupLMTokenizer.from_pretrained('microsoft/markuplm-base'     )

								return MarkupLMProcessor(__A							,     __A     )






	@slow
	def 	_lowerCamelCase (	self	:							Any     ):
								__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, 1_4, 7_6_8]     )
								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 numpy as np
from PIL import Image
def     lowercase__       (       __lowercase						:  np.array					)			->				np.array:



       """simple docstring"""






       __UpperCamelCase	,						__UpperCamelCase	,						__UpperCamelCase	=  rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
       return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_4_0 * b
def     lowercase__       (       __lowercase						:  np.array					)			->				np.array:



       """simple docstring"""






       return (gray > 127) & (gray <= 255)
def     lowercase__       (       __lowercase						:  np.array    ,  __lowercase						:  np.array					)			->				np.array:



       """simple docstring"""






       __UpperCamelCase	=  np.zeros_like(__lowercase					)
       __UpperCamelCase	=  np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)					)

       # Copy image to padded image
       __UpperCamelCase	=  image

       # Iterate over image & apply kernel
       for x in range(image.shape[1]					):
              for y in range(image.shape[0]					):
                     __UpperCamelCase	=  (
                         kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
                     ).sum()
                     __UpperCamelCase	=  int(summation == 5					)
       return output


# kernel to be applied
a__							:							Dict			=np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
    # read original image
    a__							:							Any			=np.array(Image.open(r'''..\image_data\lena.jpg'''))
    # Apply erosion operation to a binary image
    a__							:							Union[str, Any]			=erosion(grayabinary(rgbagray(image)), structuring_element)
    # Save the output image
    a__							:							Tuple			=Image.fromarray(output).convert('''RGB''')
    pil_img.save('''result_erosion.png''')
