


"""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  ,					__UpperCAmelCase  ,					__UpperCAmelCase=13  ,					__UpperCAmelCase=7  ,					__UpperCAmelCase=True  ,					__UpperCAmelCase=True  ,					__UpperCAmelCase=True  ,					__UpperCAmelCase=True  ,					__UpperCAmelCase=99  ,					__UpperCAmelCase=32  ,					__UpperCAmelCase=5  ,					__UpperCAmelCase=4  ,					__UpperCAmelCase=37  ,					__UpperCAmelCase="gelu"  ,					__UpperCAmelCase=0.1  ,					__UpperCAmelCase=0.1  ,					__UpperCAmelCase=5_12  ,					__UpperCAmelCase=16  ,					__UpperCAmelCase=2  ,					__UpperCAmelCase=0.02  ,					__UpperCAmelCase=3  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=20  ,					__UpperCAmelCase=30  ,					__UpperCAmelCase=2  ,					__UpperCAmelCase=2  ,					__UpperCAmelCase=10  ,					)				->					Optional[Any]:
     a   :	Optional[Any]						=    parent
     a   :	Tuple						=    batch_size
     a   :	List[str]						=    seq_length
     a   :	Optional[int]						=    is_training
     a   :	List[Any]						=    use_input_mask
     a   :	Tuple						=    use_token_type_ids
     a   :	Union[str, Any]						=    use_labels
     a   :	Union[str, Any]						=    vocab_size
     a   :	int						=    hidden_size
     a   :	Optional[Any]						=    num_hidden_layers
     a   :	List[Any]						=    num_attention_heads
     a   :	int						=    intermediate_size
     a   :	Union[str, Any]						=    hidden_act
     a   :	Union[str, Any]						=    hidden_dropout_prob
     a   :	Optional[int]						=    attention_probs_dropout_prob
     a   :	Union[str, Any]						=    max_position_embeddings
     a   :	int						=    type_vocab_size
     a   :	Tuple						=    type_sequence_label_size
     a   :	Tuple						=    initializer_range
     a   :	Optional[Any]						=    num_labels
     a   :	Union[str, Any]						=    scope
     a   :	int						=    max_xpath_tag_unit_embeddings
     a   :	Optional[int]						=    max_xpath_subs_unit_embeddings
     a   :	Optional[Any]						=    tag_pad_id
     a   :	str						=    subs_pad_id
     a   :	List[str]						=    max_depth
  def 							lowercase_     (						self						)				->					Optional[int]:
     a   :	List[Any]						=    ids_tensor([self.batch_size, self.seq_length]  ,					self.vocab_size						)

     a   :	List[str]						=    ids_tensor(
         [self.batch_size, self.seq_length, self.max_depth]  ,					self.max_xpath_tag_unit_embeddings						)

     a   :	Any						=    ids_tensor(
         [self.batch_size, self.seq_length, self.max_depth]  ,					self.max_xpath_subs_unit_embeddings						)

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

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

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

     a   :	Optional[int]						=    self.get_config()

     return (
         config,
         input_ids,
         xpath_tags_seq,
         xpath_subs_seq,
         token_type_ids,
         input_mask,
         sequence_labels,
         token_labels,
     )
  def 							lowercase_     (						self						)				->					Optional[int]:
     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 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					)				->					List[str]:
     a   :	Optional[int]						=    MarkupLMModel(config=snake_case_						)
     model.to(snake_case_						)
     model.eval()
     print('Configs:'  ,					model.config.tag_pad_id  ,					model.config.subs_pad_id						)
     a   :	List[Any]						=    model(snake_case_  ,					attention_mask=snake_case_  ,					token_type_ids=snake_case_						)
     a   :	List[Any]						=    model(snake_case_  ,					token_type_ids=snake_case_						)
     a   :	Optional[int]						=    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 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					)				->					Any:
     a   :	str						=    self.num_labels
     a   :	int						=    MarkupLMForSequenceClassification(snake_case_						)
     model.to(snake_case_						)
     model.eval()
     a   :	int						=    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 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					)				->					Optional[Any]:
     a   :	int						=    self.num_labels
     a   :	List[Any]						=    MarkupLMForTokenClassification(config=snake_case_						)
     model.to(snake_case_						)
     model.eval()
     a   :	List[str]						=    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 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					)				->					Union[str, Any]:
     a   :	Any						=    MarkupLMForQuestionAnswering(config=snake_case_						)
     model.to(snake_case_						)
     model.eval()
     a   :	str						=    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 							lowercase_     (						self						)				->					List[str]:
     a   :	Optional[Any]						=    self.prepare_config_and_inputs()
     (
         a       
     )   :	Tuple						=    config_and_inputs
     a   :	Optional[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_       (     _snake_case      ,  _snake_case      ,  unittest.TestCase   ):





  """simple docstring"""

  lowercase		:	Union[str, Any]   =						(
      (
          MarkupLMModel,
          MarkupLMForSequenceClassification,
          MarkupLMForTokenClassification,
          MarkupLMForQuestionAnswering,
      )
      if is_torch_available()
      else None
  )
  lowercase		:	Optional[Any]   =						(
      {
          """feature-extraction""": MarkupLMModel,
          """question-answering""": MarkupLMForQuestionAnswering,
          """text-classification""": MarkupLMForSequenceClassification,
          """token-classification""": MarkupLMForTokenClassification,
          """zero-shot""": MarkupLMForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Dict:
     # ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
     # (batch of pretokenized examples).
     return True
  def 							lowercase_     (						self						)				->					List[str]:
     a   :	str						=    MarkupLMModelTester(self						)
     a   :	Optional[int]						=    ConfigTester(self  ,					config_class=snake_case_  ,					hidden_size=37						)
  def 							lowercase_     (						self						)				->					Tuple:
     self.config_tester.run_common_tests()
  def 							lowercase_     (						self						)				->					Dict:
     a   :	int						=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*snake_case_						)
  def 							lowercase_     (						self						)				->					Union[str, Any]:
     a   :	int						=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*snake_case_						)
  def 							lowercase_     (						self						)				->					Tuple:
     a   :	Optional[Any]						=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*snake_case_						)






  def 							lowercase_     (						self						)				->					Optional[int]:
     a   :	Any						=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*snake_case_						)




def        A_					(					)     ->     Any:
   a   :	int						=    "\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 							lowercase_     (						self						)				->					Optional[int]:
     # TODO use from_pretrained here
     a   :	List[Any]						=    MarkupLMFeatureExtractor()
     a   :	Optional[int]						=    MarkupLMTokenizer.from_pretrained('microsoft/markuplm-base'						)

     return MarkupLMProcessor(snake_case_  ,					snake_case_						)






  @slow
  def 							lowercase_     (						self						)				->					Optional[int]:
     a   :	List[Any]						=    MarkupLMModel.from_pretrained('microsoft/markuplm-base'						).to(snake_case_						)

     a   :	List[str]						=    self.default_processor

     a   :	List[str]						=    processor(prepare_html_string()  ,					return_tensors='pt'						)
     a   :	Tuple						=    inputs.to(snake_case_						)

     # forward pass
     with torch.no_grad():
        a   :	Optional[Any]						=    model(**snake_case_						)

     # verify the last hidden states
     a   :	Tuple						=    torch.Size([1, 14, 7_68]						)
     self.assertEqual(outputs.last_hidden_state.shape  ,					snake_case_						)

     a   :	Dict						=    torch.tensor(
         [[0.0675, -0.0052, 0.5001], [-0.2281, 0.0802, 0.2192], [-0.0583, -0.3311, 0.1185]]						).to(snake_case_						)

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







"""simple docstring"""
from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
_a   :      List[str]       =				{
    """1""": ["""2""", """3""", """4""", """5"""],
    """2""": ["""1""", """3""", """4""", """5"""],
    """3""": ["""1""", """2""", """4""", """5""", """10"""],
    """4""": ["""1""", """2""", """3""", """5""", """6"""],
    """5""": ["""1""", """2""", """3""", """4""", """7"""],
    """6""": ["""7""", """8""", """9""", """10""", """4"""],
    """7""": ["""6""", """8""", """9""", """10""", """5"""],
    """8""": ["""6""", """7""", """9""", """10"""],
    """9""": ["""6""", """7""", """8""", """10"""],
    """10""": ["""6""", """7""", """8""", """9""", """3"""],
}
def 					a__ ( a :					dict[str, list[str]]		):

  """simple docstring"""







  _snake_case  :    List[Any]            = {node: {node} for node in graph}

  _snake_case  :    str            = {node: graph[node][:] for node in graph}

  while len(a		) > 2:
    # Choose a random edge.
    _snake_case  :    str            = random.choice(list(graph_copy.keys()		)		)
    _snake_case  :    Union[str, Any]            = random.choice(graph_copy[u]		)

    # Contract edge (u, v) to new node uv
    _snake_case  :    Union[str, Any]            = u + v
    _snake_case  :    Union[str, Any]            = list(set(graph_copy[u] + graph_copy[v]		)		)
    uv_neighbors.remove(a		)
    uv_neighbors.remove(a		)
    _snake_case  :    Union[str, Any]            = uv_neighbors
    for neighbor in uv_neighbors:
      graph_copy[neighbor].append(a		)

    _snake_case  :    Dict            = set(contracted_nodes[u].union(contracted_nodes[v]		)		)

    # Remove nodes u and v.
    del graph_copy[u]
    del graph_copy[v]
    for neighbor in uv_neighbors:
      if u in graph_copy[neighbor]:
        graph_copy[neighbor].remove(a		)
      if v in graph_copy[neighbor]:
        graph_copy[neighbor].remove(a		)

    # Find cutset.
  _snake_case  :    List[Any]            = [contracted_nodes[node] for node in graph_copy]
  return {
      (node, neighbor)
      for node in groups[0]
      for neighbor in graph[node]
      if neighbor in groups[1]
  }


if __name__ == "__main__":
    print(partition_graph(TEST_GRAPH))

