





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import OwlViTImageProcessor

class 				A    (unittest.TestCase				):



      def __init__(					self     ,  lowercase_     ,  lowercase_=7     ,  lowercase_=3     ,  lowercase_=18     ,  lowercase_=30     ,  lowercase_=400     ,  lowercase_=True     ,  lowercase_=None     ,  lowercase_=True     ,  lowercase_=None     ,  lowercase_=True     ,  lowercase_=[0.4814_5466, 0.457_8275, 0.4082_1073]     ,  lowercase_=[0.2686_2954, 0.2613_0258, 0.2757_7711]     ,  lowercase_=True     ,  )      ->   Union[str, Any]:



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						parent
             _snake_case      :							Any     				=						batch_size
             _snake_case      :							List[str]     				=						num_channels
             _snake_case      :							Optional[Any]     				=						image_size
             _snake_case      :							Tuple     				=						min_resolution
             _snake_case      :							Any     				=						max_resolution
             _snake_case      :							Dict     				=						do_resize
             _snake_case      :							Dict     				=						size if size is not None else {'''height''': 18, '''width''': 18}
             _snake_case      :							Dict     				=						do_center_crop
             _snake_case      :							Optional[int]     				=						crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
             _snake_case      :							int     				=						do_normalize
             _snake_case      :							List[str]     				=						image_mean
             _snake_case      :							Union[str, Any]     				=						image_std
             _snake_case      :							Optional[Any]     				=						do_convert_rgb



      def    __a (					self					)      ->   str:



             '''simple docstring'''

             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_center_crop": self.do_center_crop,
                 "crop_size": self.crop_size,
                 "do_normalize": self.do_normalize,
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_convert_rgb": self.do_convert_rgb,
             }


@require_torch
@require_vision
class 				A    (__UpperCAmelCase       ,unittest.TestCase				):
      _SCREAMING_SNAKE_CASE					=							OwlViTImageProcessor if is_vision_available() else None



      def    __a (					self					)      ->   Tuple:



             '''simple docstring'''

             _snake_case      :							Any     				=						OwlViTImageProcessingTester(self					)



      @property
      def    __a (					self					)      ->   Optional[int]:



             '''simple docstring'''

             return self.image_processor_tester.prepare_image_processor_dict()



      def    __a (					self					)      ->   Any:



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						self.image_processing_class(**self.image_processor_dict					)
             self.assertTrue(hasattr(lowercase_     ,  '''do_resize'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''size'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''do_center_crop'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''center_crop'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''do_normalize'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''image_mean'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''image_std'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''do_convert_rgb'''					)					)



      def    __a (					self					)      ->   Optional[Any]:



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						self.image_processing_class.from_dict(self.image_processor_dict					)
             self.assertEqual(image_processor.size     ,  {'''height''': 18, '''width''': 18}					)
             self.assertEqual(image_processor.crop_size     ,  {'''height''': 18, '''width''': 18}					)

             _snake_case      :							str     				=						self.image_processing_class.from_dict(self.image_processor_dict     ,  size=42     ,  crop_size=84					)
             self.assertEqual(image_processor.size     ,  {'''height''': 42, '''width''': 42}					)
             self.assertEqual(image_processor.crop_size     ,  {'''height''': 84, '''width''': 84}					)



      def    __a (					self					)      ->   Any:



             '''simple docstring'''

             _snake_case      :							Tuple     				=						self.image_processing_class(**self.image_processor_dict					)
             # create random PIL images
             _snake_case      :							Tuple     				=						prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_					)

             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  Image.Image					)

             # Test not batched input
             _snake_case      :							Optional[int]     				=						image_processing(image_inputs[0]     ,  return_tensors='''pt'''					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['''height'''],
                     self.image_processor_tester.crop_size['''width'''],
                 )     ,  )

             # Test batched
             _snake_case      :							Any     				=						image_processing(lowercase_     ,  return_tensors='''pt'''					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['''height'''],
                     self.image_processor_tester.crop_size['''width'''],
                 )     ,  )



      def    __a (					self					)      ->   List[str]:



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						self.image_processing_class(**self.image_processor_dict					)
             # create random numpy tensors
             _snake_case      :							Tuple     				=						prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_     ,  numpify=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  np.ndarray					)

             # Test not batched input
             _snake_case      :							List[str]     				=						image_processing(image_inputs[0]     ,  return_tensors='''pt'''					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['''height'''],
                     self.image_processor_tester.crop_size['''width'''],
                 )     ,  )

             # Test batched
             _snake_case      :							List[Any]     				=						image_processing(lowercase_     ,  return_tensors='''pt'''					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['''height'''],
                     self.image_processor_tester.crop_size['''width'''],
                 )     ,  )



      def    __a (					self					)      ->   Dict:



             '''simple docstring'''

             _snake_case      :							Optional[Any]     				=						self.image_processing_class(**self.image_processor_dict					)
             # create random PyTorch tensors
             _snake_case      :							List[Any]     				=						prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_     ,  torchify=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  torch.Tensor					)

             # Test not batched input
             _snake_case      :							List[str]     				=						image_processing(image_inputs[0]     ,  return_tensors='''pt'''					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['''height'''],
                     self.image_processor_tester.crop_size['''width'''],
                 )     ,  )

             # Test batched
             _snake_case      :							Any     				=						image_processing(lowercase_     ,  return_tensors='''pt'''					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['''height'''],
                     self.image_processor_tester.crop_size['''width'''],
                 )     ,  )













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 				A    :



      def __init__(					self     ,  lowercase_     ,  lowercase_=13     ,  lowercase_=7     ,  lowercase_=True     ,  lowercase_=True     ,  lowercase_=True     ,  lowercase_=True     ,  lowercase_=99     ,  lowercase_=32     ,  lowercase_=5     ,  lowercase_=4     ,  lowercase_=37     ,  lowercase_="gelu"     ,  lowercase_=0.1     ,  lowercase_=0.1     ,  lowercase_=512     ,  lowercase_=16     ,  lowercase_=2     ,  lowercase_=0.02     ,  lowercase_=3     ,  lowercase_=None     ,  lowercase_=20     ,  lowercase_=30     ,  lowercase_=2     ,  lowercase_=2     ,  lowercase_=10     ,  )      ->   Optional[int]:



             '''simple docstring'''

             _snake_case      :							Union[str, Any]     				=						parent
             _snake_case      :							List[str]     				=						batch_size
             _snake_case      :							str     				=						seq_length
             _snake_case      :							str     				=						is_training
             _snake_case      :							Optional[Any]     				=						use_input_mask
             _snake_case      :							Any     				=						use_token_type_ids
             _snake_case      :							str     				=						use_labels
             _snake_case      :							int     				=						vocab_size
             _snake_case      :							Tuple     				=						hidden_size
             _snake_case      :							str     				=						num_hidden_layers
             _snake_case      :							str     				=						num_attention_heads
             _snake_case      :							Optional[int]     				=						intermediate_size
             _snake_case      :							Tuple     				=						hidden_act
             _snake_case      :							Any     				=						hidden_dropout_prob
             _snake_case      :							Tuple     				=						attention_probs_dropout_prob
             _snake_case      :							str     				=						max_position_embeddings
             _snake_case      :							List[Any]     				=						type_vocab_size
             _snake_case      :							str     				=						type_sequence_label_size
             _snake_case      :							Any     				=						initializer_range
             _snake_case      :							Optional[Any]     				=						num_labels
             _snake_case      :							Union[str, Any]     				=						scope
             _snake_case      :							int     				=						max_xpath_tag_unit_embeddings
             _snake_case      :							Any     				=						max_xpath_subs_unit_embeddings
             _snake_case      :							Union[str, Any]     				=						tag_pad_id
             _snake_case      :							Union[str, Any]     				=						subs_pad_id
             _snake_case      :							int     				=						max_depth



      def    __a (					self					)      ->   Tuple:



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size					)

             _snake_case      :							Any     				=						ids_tensor(
                 [self.batch_size, self.seq_length, self.max_depth]     ,  self.max_xpath_tag_unit_embeddings					)

             _snake_case      :							Tuple     				=						ids_tensor(
                 [self.batch_size, self.seq_length, self.max_depth]     ,  self.max_xpath_subs_unit_embeddings					)

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

             _snake_case      :							int     				=						None
             if self.use_token_type_ids:
                    _snake_case      :							List[Any]     				=						ids_tensor([self.batch_size, self.seq_length]     ,  self.type_vocab_size					)

             _snake_case      :							List[Any]     				=						None
             _snake_case      :							int     				=						None
             if self.use_labels:
                    _snake_case      :							Optional[int]     				=						ids_tensor([self.batch_size]     ,  self.type_sequence_label_size					)
                    _snake_case      :							Optional[int]     				=						ids_tensor([self.batch_size, self.seq_length]     ,  self.num_labels					)

             _snake_case      :							Tuple     				=						self.get_config()

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



      def    __a (					self					)      ->   Tuple:



             '''simple docstring'''

             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    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  )      ->   Optional[Any]:



             '''simple docstring'''

             _snake_case      :							Optional[Any]     				=						MarkupLMModel(config=lowercase_					)
             model.to(lowercase_					)
             model.eval()
             print('''Configs:'''     ,  model.config.tag_pad_id     ,  model.config.subs_pad_id					)
             _snake_case      :							int     				=						model(lowercase_     ,  attention_mask=lowercase_     ,  token_type_ids=lowercase_					)
             _snake_case      :							Dict     				=						model(lowercase_     ,  token_type_ids=lowercase_					)
             _snake_case      :							str     				=						model(lowercase_					)
             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    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  )      ->   Any:



             '''simple docstring'''

             _snake_case      :							Optional[Any]     				=						self.num_labels
             _snake_case      :							List[str]     				=						MarkupLMForSequenceClassification(lowercase_					)
             model.to(lowercase_					)
             model.eval()
             _snake_case      :							Optional[int]     				=						model(
                 lowercase_     ,  xpath_tags_seq=lowercase_     ,  xpath_subs_seq=lowercase_     ,  attention_mask=lowercase_     ,  token_type_ids=lowercase_     ,  labels=lowercase_     ,  )
             self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.num_labels)					)



      def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  )      ->   Optional[Any]:



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						self.num_labels
             _snake_case      :							Dict     				=						MarkupLMForTokenClassification(config=lowercase_					)
             model.to(lowercase_					)
             model.eval()
             _snake_case      :							List[str]     				=						model(
                 lowercase_     ,  xpath_tags_seq=lowercase_     ,  xpath_subs_seq=lowercase_     ,  attention_mask=lowercase_     ,  token_type_ids=lowercase_     ,  labels=lowercase_     ,  )
             self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.seq_length, self.num_labels)					)



      def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  )      ->   Tuple:



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						MarkupLMForQuestionAnswering(config=lowercase_					)
             model.to(lowercase_					)
             model.eval()
             _snake_case      :							Optional[int]     				=						model(
                 lowercase_     ,  xpath_tags_seq=lowercase_     ,  xpath_subs_seq=lowercase_     ,  attention_mask=lowercase_     ,  token_type_ids=lowercase_     ,  start_positions=lowercase_     ,  end_positions=lowercase_     ,  )
             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    __a (					self					)      ->   Optional[Any]:



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						self.prepare_config_and_inputs()
             (
                 (
                 _snake_case
             )				,    (
                 _snake_case
             )				,    (
                 _snake_case
             )				,    (
                 _snake_case
             )				,    (
                 _snake_case
             )				,    (
                 _snake_case
             )				,    (
                 _snake_case
             )				,    (
                 _snake_case
             )				,    
             )      :							Tuple     				=						config_and_inputs
             _snake_case      :							str     				=						{
                 '''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 				A    (__UpperCAmelCase       ,__UpperCAmelCase       ,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    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_					)      ->   List[str]:



             '''simple docstring'''

             return True



      def    __a (					self					)      ->   Optional[Any]:



             '''simple docstring'''

             _snake_case      :							Any     				=						MarkupLMModelTester(self					)
             _snake_case      :							Dict     				=						ConfigTester(self     ,  config_class=lowercase_     ,  hidden_size=37					)



      def    __a (					self					)      ->   Optional[int]:



             '''simple docstring'''

             self.config_tester.run_common_tests()



      def    __a (					self					)      ->   List[Any]:



             '''simple docstring'''

             _snake_case      :							Union[str, Any]     				=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*lowercase_					)



      def    __a (					self					)      ->   Optional[Any]:



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(*lowercase_					)



      def    __a (					self					)      ->   Any:



             '''simple docstring'''

             _snake_case      :							Optional[Any]     				=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*lowercase_					)



      def    __a (					self					)      ->   str:



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*lowercase_					)




def 						A_			(  )			->						Any:
       _snake_case      :							Tuple     				=						'''
    <!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 				A    (unittest.TestCase				):



      @cached_property
      def    __a (					self					)      ->   int:



             '''simple docstring'''

             _snake_case      :							List[str]     				=						MarkupLMFeatureExtractor()
             _snake_case      :							Any     				=						MarkupLMTokenizer.from_pretrained('''microsoft/markuplm-base'''					)

             return MarkupLMProcessor(lowercase_     ,  lowercase_					)



      @slow
      def    __a (					self					)      ->   Optional[int]:



             '''simple docstring'''

             _snake_case      :							Any     				=						MarkupLMModel.from_pretrained('''microsoft/markuplm-base'''					).to(lowercase_					)

             _snake_case      :							Tuple     				=						self.default_processor

             _snake_case      :							Union[str, Any]     				=						processor(prepare_html_string()     ,  return_tensors='''pt'''					)
             _snake_case      :							List[Any]     				=						inputs.to(lowercase_					)

             # forward pass
             with torch.no_grad():
                    _snake_case      :							List[str]     				=						model(**lowercase_					)

             # verify the last hidden states
             _snake_case      :							Union[str, Any]     				=						torch.Size([1, 14, 768]					)
             self.assertEqual(outputs.last_hidden_state.shape     ,  lowercase_					)

             _snake_case      :							List[str]     				=						torch.tensor(
                 [[0.0675, -0.0052, 0.5001], [-0.2281, 0.0802, 0.2192], [-0.0583, -0.3311, 0.1185]]					).to(lowercase_					)

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






