"""simple docstring"""


class 					a					:



  def __init__(		self   :    int						,  lowerCamelCase_   :    Any     )      -> str:
      __a			      =						data
      __a			      =						None
      __a			      =						None



  def __str__(		self   :    Optional[Any]     )      -> Optional[int]:
      return F"""{self.data}"""






class 					a					:



  def __init__(		self   :    Tuple     )      -> List[str]:
      __a			      =						None
      __a			      =						None



  def __iter__(		self   :    Optional[Any]     )      -> Optional[Any]:
      __a			      =						self.head
      while node:
          yield node.data
          __a			      =						node.next



  def __str__(		self   :    str     )      -> Optional[Any]:
      return "->".join([str(lowerCamelCase_     ) for item in self]     )



  def __len__(		self   :    Optional[int]     )      -> List[Any]:
      return sum(1 for _ in self     )



  def        lowerCAmelCase_				(		self   :    str						,  lowerCamelCase_   :    List[Any]     )      -> int:
      self.insert_at_nth(0						,  lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    List[Any]						,  lowerCamelCase_   :    int     )      -> str:
      self.insert_at_nth(len(self     )						,  lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Union[str, Any]						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    Tuple     )      -> Optional[Any]:
      __a			      =						len(self     )

      if not 0 <= index <= length:
          raise IndexError("""list index out of range"""     )
      __a			      =						Node(lowerCamelCase_     )
      if self.head is None:
          __a			      =						__a			      =						new_node
      elif index == 0:
          __a			      =						new_node
          __a			      =						self.head
          __a			      =						new_node
      elif index == length:
          __a			      =						new_node
          __a			      =						self.tail
          __a			      =						new_node
      else:
          __a			      =						self.head
          for _ in range(0						,  lowerCamelCase_     ):
              __a			      =						temp.next
          __a			      =						new_node
          __a			      =						temp.previous
          __a			      =						temp
          __a			      =						new_node



  def        lowerCAmelCase_				(		self   :    Optional[int]     )      -> Tuple:
      return self.delete_at_nth(0     )



  def        lowerCAmelCase_				(		self   :    Dict     )      -> int:
      return self.delete_at_nth(len(self     ) - 1     )



  def        lowerCAmelCase_				(		self   :    Optional[Any]						,  lowerCamelCase_   :    int     )      -> str:
      __a			      =						len(self     )

      if not 0 <= index <= length - 1:
          raise IndexError("""list index out of range"""     )
      __a			      =						self.head  # default first node
      if length == 1:
          __a			      =						__a			      =						None
      elif index == 0:
          __a			      =						self.head.next
          __a			      =						None
      elif index == length - 1:
          __a			      =						self.tail
          __a			      =						self.tail.previous
          __a			      =						None
      else:
          __a			      =						self.head
          for _ in range(0						,  lowerCamelCase_     ):
              __a			      =						temp.next
          __a			      =						temp
          __a			      =						temp.previous
          __a			      =						temp.next
      return delete_node.data



  def        lowerCAmelCase_				(		self   :    Any						,  lowerCamelCase_   :    List[Any]     )      -> str:
      __a			      =						self.head

      while current.data != data:  # Find the position to delete
          if current.next:
              __a			      =						current.next
          else:  # We have reached the end an no value matches
              raise ValueError("""No data matching given value"""     )

      if current == self.head:
          self.delete_head()

      elif current == self.tail:
          self.delete_tail()

      else:  # Before: 1 <--> 2(current) <--> 3
          __a			      =						current.next  # 1 --> 3
          __a			      =						current.previous  # 1 <--> 3
      return data



  def        lowerCAmelCase_				(		self   :    List[str]     )      -> Dict:
      return len(self     ) == 0

def 						UpperCamelCase				(	):
    __a			      =						DoublyLinkedList()
    assert linked_list.is_empty() is True
    assert str(_lowerCAmelCase						) == ""

    try:
        linked_list.delete_head()
        raise AssertionError  # This should not happen.
    except IndexError:
        assert True  # This should happen.

    try:
        linked_list.delete_tail()
        raise AssertionError  # This should not happen.
    except IndexError:
        assert True  # This should happen.

    for i in range(10						):
        assert len(_lowerCAmelCase						) == i
        linked_list.insert_at_nth(_lowerCAmelCase				, i + 1						)
    assert str(_lowerCAmelCase						) == "->".join(str(_lowerCAmelCase						) for i in range(1				, 11						)						)

    linked_list.insert_at_head(0						)
    linked_list.insert_at_tail(11						)
    assert str(_lowerCAmelCase						) == "->".join(str(_lowerCAmelCase						) for i in range(0				, 12						)						)

    assert linked_list.delete_head() == 0
    assert linked_list.delete_at_nth(9						) == 10
    assert linked_list.delete_tail() == 11
    assert len(_lowerCAmelCase						) == 9
    assert str(_lowerCAmelCase						) == "->".join(str(_lowerCAmelCase						) for i in range(1				, 10						)						)


if __name__ == "__main__":
 from doctest import testmod

 testmod()

"""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					:



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



  def        lowerCAmelCase_				(		self   :    Dict     )      -> Dict:
      __a			      =						ids_tensor([self.batch_size, self.seq_length]						,  self.vocab_size     )

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

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

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

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

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

      __a			      =						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   :    List[Any]     )      -> Optional[Any]:
      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   :    Tuple						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    List[str]						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    Any						,  lowerCamelCase_   :    Optional[Any]						,  lowerCamelCase_   :    Any						,  lowerCamelCase_   :    List[Any]						,  )      -> int:
      __a			      =						MarkupLMModel(config=lowerCamelCase_     )
      model.to(lowerCamelCase_     )
      model.eval()
      print("""Configs:"""						,  model.config.tag_pad_id						,  model.config.subs_pad_id     )
      __a			      =						model(lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  token_type_ids=lowerCamelCase_     )
      __a			      =						model(lowerCamelCase_						,  token_type_ids=lowerCamelCase_     )
      __a			      =						model(lowerCamelCase_     )
      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   :    Optional[int]						,  lowerCamelCase_   :    List[Any]						,  lowerCamelCase_   :    Optional[int]						,  lowerCamelCase_   :    str						,  lowerCamelCase_   :    Optional[int]						,  lowerCamelCase_   :    List[Any]						,  lowerCamelCase_   :    Tuple						,  lowerCamelCase_   :    List[str]						,  lowerCamelCase_   :    int						,  )      -> Optional[Any]:
      __a			      =						self.num_labels
      __a			      =						MarkupLMForSequenceClassification(lowerCamelCase_     )
      model.to(lowerCamelCase_     )
      model.eval()
      __a			      =						model(
          lowerCamelCase_						,  xpath_tags_seq=lowerCamelCase_						,  xpath_subs_seq=lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  token_type_ids=lowerCamelCase_						,  labels=lowerCamelCase_						,  )
      self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.num_labels)     )



  def        lowerCAmelCase_				(		self   :    int						,  lowerCamelCase_   :    Any						,  lowerCamelCase_   :    Any						,  lowerCamelCase_   :    List[Any]						,  lowerCamelCase_   :    Dict						,  lowerCamelCase_   :    Union[str, Any]						,  lowerCamelCase_   :    Union[str, Any]						,  lowerCamelCase_   :    str						,  lowerCamelCase_   :    Union[str, Any]						,  )      -> int:
      __a			      =						self.num_labels
      __a			      =						MarkupLMForTokenClassification(config=lowerCamelCase_     )
      model.to(lowerCamelCase_     )
      model.eval()
      __a			      =						model(
          lowerCamelCase_						,  xpath_tags_seq=lowerCamelCase_						,  xpath_subs_seq=lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  token_type_ids=lowerCamelCase_						,  labels=lowerCamelCase_						,  )
      self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.seq_length, self.num_labels)     )



  def        lowerCAmelCase_				(		self   :    List[Any]						,  lowerCamelCase_   :    Tuple						,  lowerCamelCase_   :    Dict						,  lowerCamelCase_   :    Optional[int]						,  lowerCamelCase_   :    Optional[Any]						,  lowerCamelCase_   :    Optional[Any]						,  lowerCamelCase_   :    Union[str, Any]						,  lowerCamelCase_   :    Dict						,  lowerCamelCase_   :    List[Any]						,  )      -> Tuple:
      __a			      =						MarkupLMForQuestionAnswering(config=lowerCamelCase_     )
      model.to(lowerCamelCase_     )
      model.eval()
      __a			      =						model(
          lowerCamelCase_						,  xpath_tags_seq=lowerCamelCase_						,  xpath_subs_seq=lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  token_type_ids=lowerCamelCase_						,  start_positions=lowerCamelCase_						,  end_positions=lowerCamelCase_						,  )
      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   :    str     )      -> str:
      __a			      =						self.prepare_config_and_inputs()
      (
          (
          __a
      )      ,  (
          __a
      )      ,  (
          __a
      )      ,  (
          __a
      )      ,  (
          __a
      )      ,  (
          __a
      )      ,  (
          __a
      )      ,  (
          __a
      )      ,  
      )			      =						config_and_inputs
      __a			      =						{
          """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					(				A_   ,		A_   ,		unittest.TestCase							):
  A_ :				int 			=							(
      (
          MarkupLMModel,
          MarkupLMForSequenceClassification,
          MarkupLMForTokenClassification,
          MarkupLMForQuestionAnswering,
      )
      if is_torch_available()
      else None
  )
  A_ :				Any 			=							(
      {
          '''feature-extraction''': MarkupLMModel,
          '''question-answering''': MarkupLMForQuestionAnswering,
          '''text-classification''': MarkupLMForSequenceClassification,
          '''token-classification''': MarkupLMForTokenClassification,
          '''zero-shot''': MarkupLMForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )



  def        lowerCAmelCase_				(		self   :    Union[str, Any]						,  lowerCamelCase_   :    Optional[Any]						,  lowerCamelCase_   :    Union[str, Any]						,  lowerCamelCase_   :    Optional[int]						,  lowerCamelCase_   :    List[str]						,  lowerCamelCase_   :    Union[str, Any]     )      -> List[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   :    Tuple     )      -> str:
      __a			      =						MarkupLMModelTester(self     )
      __a			      =						ConfigTester(self						,  config_class=lowerCamelCase_						,  hidden_size=37     )



  def        lowerCAmelCase_				(		self   :    Union[str, Any]     )      -> List[str]:
      self.config_tester.run_common_tests()



  def        lowerCAmelCase_				(		self   :    str     )      -> Dict:
      __a			      =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Dict     )      -> Optional[int]:
      __a			      =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Tuple     )      -> Tuple:
      __a			      =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Union[str, Any]     )      -> int:
      __a			      =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_     )

def 						UpperCamelCase				(	):
    __a			      =						"""
    <!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        lowerCAmelCase_				(		self   :    int     )      -> Any:
      # TODO use from_pretrained here
      __a			      =						MarkupLMFeatureExtractor()
      __a			      =						MarkupLMTokenizer.from_pretrained("""microsoft/markuplm-base"""     )

      return MarkupLMProcessor(lowerCamelCase_						,  lowerCamelCase_     )



  @slow
  def        lowerCAmelCase_				(		self   :    Optional[int]     )      -> Dict:
      __a			      =						MarkupLMModel.from_pretrained("""microsoft/markuplm-base"""     ).to(lowerCamelCase_     )

      __a			      =						self.default_processor

      __a			      =						processor(prepare_html_string()						,  return_tensors="""pt"""     )
      __a			      =						inputs.to(lowerCamelCase_     )

      # forward pass
      with torch.no_grad():
          __a			      =						model(**lowerCamelCase_     )

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

      __a			      =						torch.tensor(
          [[0.06_75, -0.00_52, 0.50_01], [-0.22_81, 0.08_02, 0.21_92], [-0.05_83, -0.33_11, 0.11_85]]     ).to(lowerCamelCase_     )

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