





'''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 					snake_case					:




 """simple docstring"""



 def __init__(	self	:							int							,     __A	:							Dict							,     __A	:							Tuple=1_3							,     __A	:							Optional[int]=7							,     __A	:							Optional[int]=True							,     __A	:							List[Any]=True							,     __A	:							Dict=True							,     __A	:							Optional[int]=True							,     __A	:							Optional[int]=9_9							,     __A	:							str=3_2							,     __A	:							Tuple=5							,     __A	:							Dict=4							,     __A	:							Any=3_7							,     __A	:							List[Any]="gelu"							,     __A	:							Tuple=0.1							,     __A	:							Dict=0.1							,     __A	:							int=5_1_2							,     __A	:							List[str]=1_6							,     __A	:							Dict=2							,     __A	:							Tuple=0.02							,     __A	:							List[str]=3							,     __A	:							int=None							,     __A	:							Tuple=2_0							,     __A	:							List[str]=3_0							,     __A	:							Tuple=2							,     __A	:							Optional[int]=2							,     __A	:							Any=1_0							,     ):
        __UpperCamelCase	=  parent
        __UpperCamelCase	=  batch_size
        __UpperCamelCase	=  seq_length
        __UpperCamelCase	=  is_training
        __UpperCamelCase	=  use_input_mask
        __UpperCamelCase	=  use_token_type_ids
        __UpperCamelCase	=  use_labels
        __UpperCamelCase	=  vocab_size
        __UpperCamelCase	=  hidden_size
        __UpperCamelCase	=  num_hidden_layers
        __UpperCamelCase	=  num_attention_heads
        __UpperCamelCase	=  intermediate_size
        __UpperCamelCase	=  hidden_act
        __UpperCamelCase	=  hidden_dropout_prob
        __UpperCamelCase	=  attention_probs_dropout_prob
        __UpperCamelCase	=  max_position_embeddings
        __UpperCamelCase	=  type_vocab_size
        __UpperCamelCase	=  type_sequence_label_size
        __UpperCamelCase	=  initializer_range
        __UpperCamelCase	=  num_labels
        __UpperCamelCase	=  scope
        __UpperCamelCase	=  max_xpath_tag_unit_embeddings
        __UpperCamelCase	=  max_xpath_subs_unit_embeddings
        __UpperCamelCase	=  tag_pad_id
        __UpperCamelCase	=  subs_pad_id
        __UpperCamelCase	=  max_depth






 def 	_lowerCamelCase (	self	:							List[Any]     ):
        __UpperCamelCase	=  ids_tensor([self.batch_size, self.seq_length]							,     self.vocab_size     )

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

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

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

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

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

        __UpperCamelCase	=  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	:							Tuple     ):
        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	:							Union[str, Any]							,     __A	:							Any							,     __A	:							Dict							,     __A	:							Union[str, Any]							,     __A	:							Any							,     __A	:							Dict							,     __A	:							Optional[Any]							,     __A	:							Any							,     __A	:							Tuple							,     ):
        __UpperCamelCase	=  MarkupLMModel(config=__A     )
        model.to(__A     )
        model.eval()
        print('Configs:'							,     model.config.tag_pad_id							,     model.config.subs_pad_id     )
        __UpperCamelCase	=  model(__A							,     attention_mask=__A							,     token_type_ids=__A     )
        __UpperCamelCase	=  model(__A							,     token_type_ids=__A     )
        __UpperCamelCase	=  model(__A     )
        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							,     __A	:							Tuple							,     __A	:							Tuple							,     __A	:							Optional[int]							,     __A	:							Any							,     __A	:							Union[str, Any]							,     __A	:							Any							,     __A	:							int							,     __A	:							List[Any]							,     ):
        __UpperCamelCase	=  self.num_labels
        __UpperCamelCase	=  MarkupLMForSequenceClassification(__A     )
        model.to(__A     )
        model.eval()
        __UpperCamelCase	=  model(
            __A							,     xpath_tags_seq=__A							,     xpath_subs_seq=__A							,     attention_mask=__A							,     token_type_ids=__A							,     labels=__A							,     )
        self.parent.assertEqual(result.logits.shape							,     (self.batch_size, self.num_labels)     )






 def 	_lowerCamelCase (	self	:							Optional[Any]							,     __A	:							Optional[int]							,     __A	:							Dict							,     __A	:							List[Any]							,     __A	:							Optional[int]							,     __A	:							List[str]							,     __A	:							Any							,     __A	:							Optional[Any]							,     __A	:							Optional[int]							,     ):
        __UpperCamelCase	=  self.num_labels
        __UpperCamelCase	=  MarkupLMForTokenClassification(config=__A     )
        model.to(__A     )
        model.eval()
        __UpperCamelCase	=  model(
            __A							,     xpath_tags_seq=__A							,     xpath_subs_seq=__A							,     attention_mask=__A							,     token_type_ids=__A							,     labels=__A							,     )
        self.parent.assertEqual(result.logits.shape							,     (self.batch_size, self.seq_length, self.num_labels)     )






 def 	_lowerCamelCase (	self	:							List[Any]							,     __A	:							List[str]							,     __A	:							int							,     __A	:							List[str]							,     __A	:							Any							,     __A	:							Optional[int]							,     __A	:							Any							,     __A	:							Union[str, Any]							,     __A	:							Optional[Any]							,     ):
        __UpperCamelCase	=  MarkupLMForQuestionAnswering(config=__A     )
        model.to(__A     )
        model.eval()
        __UpperCamelCase	=  model(
            __A							,     xpath_tags_seq=__A							,     xpath_subs_seq=__A							,     attention_mask=__A							,     token_type_ids=__A							,     start_positions=__A							,     end_positions=__A							,     )
        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[str]     ):
        __UpperCamelCase	=  self.prepare_config_and_inputs()
        (
            (
            __UpperCamelCase
        )	,						(
            __UpperCamelCase
        )	,						(
            __UpperCamelCase
        )	,						(
            __UpperCamelCase
        )	,						(
            __UpperCamelCase
        )	,						(
            __UpperCamelCase
        )	,						(
            __UpperCamelCase
        )	,						(
            __UpperCamelCase
        )	,						
        )	=  config_and_inputs
        __UpperCamelCase	=  {
            '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 					snake_case					(   __lowerCamelCase      ,	__lowerCamelCase      ,	unittest.TestCase	):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : List[str]      		=(
     (
         MarkupLMModel,
         MarkupLMForSequenceClassification,
         MarkupLMForTokenClassification,
         MarkupLMForQuestionAnswering,
     )
     if is_torch_available()
     else None
 )
 SCREAMING_SNAKE_CASE_     : Optional[int]      		=(
     {
         "feature-extraction": MarkupLMModel,
         "question-answering": MarkupLMForQuestionAnswering,
         "text-classification": MarkupLMForSequenceClassification,
         "token-classification": MarkupLMForTokenClassification,
         "zero-shot": MarkupLMForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )






 def 	_lowerCamelCase (	self	:							Optional[Any]							,     __A	:							List[str]							,     __A	:							List[str]							,     __A	:							Optional[Any]							,     __A	:							Optional[Any]							,     __A	:							Optional[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	:							int     ):
        __UpperCamelCase	=  MarkupLMModelTester(self     )
        __UpperCamelCase	=  ConfigTester(self							,     config_class=__A							,     hidden_size=3_7     )






 def 	_lowerCamelCase (	self	:							Union[str, Any]     ):
        self.config_tester.run_common_tests()






 def 	_lowerCamelCase (	self	:							Optional[int]     ):
        __UpperCamelCase	=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__A     )






 def 	_lowerCamelCase (	self	:							str     ):
        __UpperCamelCase	=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*__A     )






 def 	_lowerCamelCase (	self	:							Optional[int]     ):
        __UpperCamelCase	=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__A     )






 def 	_lowerCamelCase (	self	:							List[Any]     ):
        __UpperCamelCase	=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*__A     )






def     lowercase__       (       )			->				Optional[Any]:



       """simple docstring"""






       __UpperCamelCase	=  '\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 					snake_case					(   unittest.TestCase	):




 """simple docstring"""



 @cached_property
 def 	_lowerCamelCase (	self	:							Any     ):
        # TODO use from_pretrained here
        __UpperCamelCase	=  MarkupLMFeatureExtractor()
        __UpperCamelCase	=  MarkupLMTokenizer.from_pretrained('microsoft/markuplm-base'     )

        return MarkupLMProcessor(__A							,     __A     )






 @slow
 def 	_lowerCamelCase (	self	:							Any     ):
        __UpperCamelCase	=  MarkupLMModel.from_pretrained('microsoft/markuplm-base'     ).to(__A     )

        __UpperCamelCase	=  self.default_processor

        __UpperCamelCase	=  processor(prepare_html_string()							,     return_tensors='pt'     )
        __UpperCamelCase	=  inputs.to(__A     )

        # forward pass
        with torch.no_grad():
               __UpperCamelCase	=  model(**__A     )

        # verify the last hidden states
        __UpperCamelCase	=  torch.Size([1, 1_4, 7_6_8]     )
        self.assertEqual(outputs.last_hidden_state.shape							,     __A     )

        __UpperCamelCase	=  torch.tensor(
            [[0.0675, -0.0052, 0.5001], [-0.2281, 0.0802, 0.2192], [-0.0583, -0.3311, 0.1185]]     ).to(__A     )

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







'''simple docstring'''
def     lowercase__       (       __lowercase						:  list    ,  __lowercase						:  int    ,  __lowercase						:  int    ,  __lowercase						:  int					)			->				int:



       """simple docstring"""






       __UpperCamelCase	=  int((lower_bound + upper_bound) // 2					)
       if array[r] == value:
              return r
       if lower_bound >= upper_bound:
              return -1
       if array[r] < value:
              return binary_search(__lowercase    ,  r + 1    ,  __lowercase    ,  __lowercase					)
       else:
              return binary_search(__lowercase    ,  __lowercase    ,  r - 1    ,  __lowercase					)
def     lowercase__       (       __lowercase						:  int    ,  __lowercase						:  list					)			->				list:



       """simple docstring"""






       __UpperCamelCase	=  0
       if matrix[index][0] == value:
              return [index, 0]
       while index < len(__lowercase					) and matrix[index][0] < value:
              __UpperCamelCase	=  binary_search(matrix[index]    ,  0    ,  len(matrix[index]					) - 1    ,  __lowercase					)
              if r != -1:
                     return [index, r]
              index += 1
       return [-1, -1]


if __name__ == "__main__":
    import doctest

    doctest.testmod()
