

"""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  __a				:





       """simple docstring"""


       def __init__(      self ,				snake_case ,				snake_case=13 ,				snake_case=7 ,				snake_case=True ,				snake_case=True ,				snake_case=True ,				snake_case=True ,				snake_case=99 ,				snake_case=32 ,				snake_case=5 ,				snake_case=4 ,				snake_case=37 ,				snake_case="gelu" ,				snake_case=0.1 ,				snake_case=0.1 ,				snake_case=512 ,				snake_case=16 ,				snake_case=2 ,				snake_case=0.02 ,				snake_case=3 ,				snake_case=None ,				snake_case=20 ,				snake_case=30 ,				snake_case=2 ,				snake_case=2 ,				snake_case=10 ,				):





        """simple docstring"""

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






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[Any]    =							ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size      )

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

        lowerCAmelCase__      :							Optional[int]    =							ids_tensor(
            [self.batch_size, self.seq_length, self.max_depth] ,				self.max_xpath_subs_unit_embeddings      )

        lowerCAmelCase__      :							Dict    =							None
        if self.use_input_mask:
         lowerCAmelCase__      :							Optional[Any]    =							ids_tensor([self.batch_size, self.seq_length] ,				vocab_size=2      )

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

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

        lowerCAmelCase__      :							Tuple    =							self.get_config()

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






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """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  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				):





        """simple docstring"""

        lowerCAmelCase__      :							Tuple    =							MarkupLMModel(config=snake_case      )
        model.to(snake_case      )
        model.eval()
        print("Configs:" ,				model.config.tag_pad_id ,				model.config.subs_pad_id      )
        lowerCAmelCase__      :							Tuple    =							model(snake_case ,				attention_mask=snake_case ,				token_type_ids=snake_case      )
        lowerCAmelCase__      :							Any    =							model(snake_case ,				token_type_ids=snake_case      )
        lowerCAmelCase__      :							str    =							model(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  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				):





        """simple docstring"""

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






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				):





        """simple docstring"""

        lowerCAmelCase__      :							Any    =							self.num_labels
        lowerCAmelCase__      :							Any    =							MarkupLMForTokenClassification(config=snake_case      )
        model.to(snake_case      )
        model.eval()
        lowerCAmelCase__      :							Dict    =							model(
            snake_case ,				xpath_tags_seq=snake_case ,				xpath_subs_seq=snake_case ,				attention_mask=snake_case ,				token_type_ids=snake_case ,				labels=snake_case ,				)
        self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.num_labels)      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[Any]    =							MarkupLMForQuestionAnswering(config=snake_case      )
        model.to(snake_case      )
        model.eval()
        lowerCAmelCase__      :							Tuple    =							model(
            snake_case ,				xpath_tags_seq=snake_case ,				xpath_subs_seq=snake_case ,				attention_mask=snake_case ,				token_type_ids=snake_case ,				start_positions=snake_case ,				end_positions=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  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Union[str, Any]    =							self.prepare_config_and_inputs()
        (
            lowerCAmelCase__	
        )      :							Optional[int]    =							config_and_inputs
        lowerCAmelCase__      :							List[Any]    =							{
            "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				(	__magic_name__					,  __magic_name__					,  unittest.TestCase					):





       """simple docstring"""


       __UpperCamelCase				:			Any          =	(
           (
               MarkupLMModel,
               MarkupLMForSequenceClassification,
               MarkupLMForTokenClassification,
               MarkupLMForQuestionAnswering,
           )
           if is_torch_available()
           else None
       )
       __UpperCamelCase				:			List[Any]          =	(
           {
               'feature-extraction': MarkupLMModel,
               'question-answering': MarkupLMForQuestionAnswering,
               'text-classification': MarkupLMForSequenceClassification,
               'token-classification': MarkupLMForTokenClassification,
               'zero-shot': MarkupLMForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case      ):





        """simple docstring"""

        return True






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Tuple    =							MarkupLMModelTester(self      )
        lowerCAmelCase__      :							Optional[int]    =							ConfigTester(self ,				config_class=snake_case ,				hidden_size=37      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        self.config_tester.run_common_tests()






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Tuple    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Union[str, Any]    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*snake_case      )




def 			SCREAMING_SNAKE_CASE       (   ) ->	Tuple:
 lowerCAmelCase__      :							str    =							"\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  __a				(	unittest.TestCase					):





       """simple docstring"""


       @cached_property
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							int    =							MarkupLMFeatureExtractor()
        lowerCAmelCase__      :							int    =							MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base"      )

        return MarkupLMProcessor(snake_case ,				snake_case      )






       @slow
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							MarkupLMModel.from_pretrained("microsoft/markuplm-base"      ).to(snake_case      )

        lowerCAmelCase__      :							List[str]    =							self.default_processor

        lowerCAmelCase__      :							Any    =							processor(prepare_html_string() ,				return_tensors="pt"      )
        lowerCAmelCase__      :							Optional[int]    =							inputs.to(snake_case      )

        # forward pass
        with torch.no_grad():
         lowerCAmelCase__      :							int    =							model(**snake_case      )

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

        lowerCAmelCase__      :							str    =							torch.tensor(
            [[0.0_675, -0.0_052, 0.5_001], [-0.2_281, 0.0_802, 0.2_192], [-0.0_583, -0.3_311, 0.1_185]]      ).to(snake_case      )

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









"""simple docstring"""

# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
from queue import Queue



def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	List[str]:
 lowerCAmelCase__      :							Tuple    =							Queue()
 lowerCAmelCase__      :							Optional[int]    =							[False] * len(lowercase__      )
 lowerCAmelCase__      :							Dict    =							[-1] * len(lowercase__      )

 def bfs():
  while not queue.empty():
   lowerCAmelCase__      :							Optional[Any]    =							queue.get()
   lowerCAmelCase__      :							Union[str, Any]    =							True

   for neighbour in graph[u]:
    if neighbour == u:
     return False

    if color[neighbour] == -1:
     lowerCAmelCase__      :							Any    =							1 - color[u]
     queue.put(lowercase__      )

    elif color[neighbour] == color[u]:
     return False

  return True

 for i in range(len(lowercase__      )      ):
  if not visited[i]:
   queue.put(lowercase__      )
   lowerCAmelCase__      :							int    =							0
   if bfs() is False:
    return False

 return True


if __name__ == "__main__":
    # Adjacency List of graph
    print(check_bipartite({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}))






