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 					UpperCAmelCase_				:




    def __init__(					self  :   List[Any]      ,      lowerCAmelCase  :   Union[str, Any]      ,      lowerCAmelCase  :   Tuple=13      ,      lowerCAmelCase  :   int=7      ,      lowerCAmelCase  :   Union[str, Any]=True      ,      lowerCAmelCase  :   Union[str, Any]=True      ,      lowerCAmelCase  :   Optional[int]=True      ,      lowerCAmelCase  :   Any=True      ,      lowerCAmelCase  :   int=99      ,      lowerCAmelCase  :   Dict=32      ,      lowerCAmelCase  :   List[str]=5      ,      lowerCAmelCase  :   Dict=4      ,      lowerCAmelCase  :   Any=37      ,      lowerCAmelCase  :   Any="gelu"      ,      lowerCAmelCase  :   int=0.1      ,      lowerCAmelCase  :   Union[str, Any]=0.1      ,      lowerCAmelCase  :   List[Any]=512      ,      lowerCAmelCase  :   Union[str, Any]=16      ,      lowerCAmelCase  :   str=2      ,      lowerCAmelCase  :   Dict=0.02      ,      lowerCAmelCase  :   Optional[int]=3      ,      lowerCAmelCase  :   Union[str, Any]=None      ,      lowerCAmelCase  :   int=20      ,      lowerCAmelCase  :   Any=30      ,      lowerCAmelCase  :   Tuple=2      ,      lowerCAmelCase  :   Dict=2      ,      lowerCAmelCase  :   int=10      ,      )	-> List[str]:
      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  :   List[Any]   )	-> List[Any]:
      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[str]   )	-> Union[str, 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  :   int      ,      lowerCAmelCase  :   Optional[int]      ,      lowerCAmelCase  :   Any      ,      lowerCAmelCase  :   str      ,      lowerCAmelCase  :   str      ,      lowerCAmelCase  :   Union[str, Any]      ,      lowerCAmelCase  :   str      ,      lowerCAmelCase  :   Union[str, Any]      ,      lowerCAmelCase  :   Any      ,      )	-> str:
      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  :   Any      ,      lowerCAmelCase  :   int      ,      lowerCAmelCase  :   Any      ,      lowerCAmelCase  :   Optional[Any]      ,      lowerCAmelCase  :   Dict      ,      lowerCAmelCase  :   Union[str, Any]      ,      lowerCAmelCase  :   List[str]      ,      lowerCAmelCase  :   Optional[int]      ,      lowerCAmelCase  :   Tuple      ,      )	-> Optional[int]:
      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  :   Optional[Any]      ,      lowerCAmelCase  :   Dict      ,      lowerCAmelCase  :   List[str]      ,      lowerCAmelCase  :   Union[str, Any]      ,      lowerCAmelCase  :   List[Any]      ,      lowerCAmelCase  :   Union[str, Any]      ,      lowerCAmelCase  :   Any      ,      lowerCAmelCase  :   Optional[Any]      ,      lowerCAmelCase  :   List[str]      ,      )	-> Any:
      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  :   Optional[int]      ,      lowerCAmelCase  :   Union[str, Any]      ,      lowerCAmelCase  :   Optional[int]      ,      lowerCAmelCase  :   Any      ,      lowerCAmelCase  :   str      ,      lowerCAmelCase  :   str      ,      lowerCAmelCase  :   Optional[int]      ,      lowerCAmelCase  :   Tuple      ,      lowerCAmelCase  :   Union[str, Any]      ,      )	-> str:
      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  :   List[Any]   )	-> List[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 					UpperCAmelCase_				(							_a     ,     _a     ,     unittest.TestCase    ):
    UpperCAmelCase_			:       List[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 						__lowerCamelCase (					self  :   Dict      ,      lowerCAmelCase  :   Optional[Any]      ,      lowerCAmelCase  :   int      ,      lowerCAmelCase  :   List[Any]      ,      lowerCAmelCase  :   str      ,      lowerCAmelCase  :   Any   )	-> str:
      # ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
      # (batch of pretokenized examples).
      return True




    def 						__lowerCamelCase (					self  :   str   )	-> Union[str, Any]:
      a__     		=     MarkupLMModelTester(self   )
      a__     		=     ConfigTester(self      ,      config_class=lowerCAmelCase      ,      hidden_size=37   )




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




    def 						__lowerCamelCase (					self  :   Optional[int]   )	-> str:
      a__     		=     self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCAmelCase   )




    def 						__lowerCamelCase (					self  :   List[str]   )	-> Tuple:
      a__     		=     self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase   )




    def 						__lowerCamelCase (					self  :   Dict   )	-> Optional[Any]:
      a__     		=     self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase   )




    def 						__lowerCamelCase (					self  :   Optional[Any]   )	-> int:
      a__     		=     self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase   )

def 				__lowercase	(						)				->List[Any]:

  '''simple docstring'''




  a__     		=     "\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 					UpperCAmelCase_				(							unittest.TestCase    ):




    @cached_property
    def 						__lowerCamelCase (					self  :   str   )	-> Optional[Any]:
      # TODO use from_pretrained here
      a__     		=     MarkupLMFeatureExtractor()
      a__     		=     MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base"   )

      return MarkupLMProcessor(lowerCAmelCase      ,      lowerCAmelCase   )




    @slow
    def 						__lowerCamelCase (					self  :   str   )	-> Optional[Any]:
      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, 768]   )
      self.assertEqual(outputs.last_hidden_state.shape      ,      lowerCAmelCase   )

      a__     		=     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(lowerCAmelCase   )

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


def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__					)				->Optional[int]:

  '''simple docstring'''




  sequence.append(lowerCamelCase__					)

  a__     		=     0
  while sequence[index] != target:
    index += 1

  sequence.pop()

  if index == len(lowerCamelCase__					):
    return None

  return index


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE   						=    input("""Enter numbers separated by comma:\n""").strip()
     _SCREAMING_SNAKE_CASE   						=    [int(item) for item in user_input.split(""",""")]

     _SCREAMING_SNAKE_CASE   						=    input("""Enter a single number to be found in the list:\n""")
     _SCREAMING_SNAKE_CASE   						=    int(target_input)
     _SCREAMING_SNAKE_CASE   						=    sentinel_linear_search(sequence, target)
     if result is not None:
          print(F"""{target} found at positions: {result}""")
     else:
          print("""Not found""")

