

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase 	=   {
    '''configuration_led''': ['''LED_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LEDConfig'''],
    '''tokenization_led''': ['''LEDTokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase 	=   ['''LEDTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase 	=   [
           '''LED_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''LEDForConditionalGeneration''',
           '''LEDForQuestionAnswering''',
           '''LEDForSequenceClassification''',
           '''LEDModel''',
           '''LEDPreTrainedModel''',
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase 	=   ['''TFLEDForConditionalGeneration''', '''TFLEDModel''', '''TFLEDPreTrainedModel''']


if TYPE_CHECKING:
       from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
       from .tokenization_led import LEDTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_led_fast import LEDTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_led import (
                  LED_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LEDForConditionalGeneration,
                  LEDForQuestionAnswering,
                  LEDForSequenceClassification,
                  LEDModel,
                  LEDPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
       import sys

       lowerCamelCase 	=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



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 		__magic_name__     :

   '''simple docstring'''




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






     """simple docstring"""






     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 					_UpperCAmelCase					(							self			)				->       Optional[Any]:






     """simple docstring"""






     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 					_UpperCAmelCase					(							self			)				->       List[str]:






     """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 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_,       )				->       Tuple:






     """simple docstring"""






     a__    =MarkupLMModel(config=lowercase_			)
     model.to(lowercase_			)
     model.eval()
     print('''Configs:''',       model.config.tag_pad_id,       model.config.subs_pad_id			)
     a__    =model(lowercase_,       attention_mask=lowercase_,       token_type_ids=lowercase_			)
     a__    =model(lowercase_,       token_type_ids=lowercase_			)
     a__    =model(lowercase_			)
     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 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_,       )				->       Tuple:






     """simple docstring"""






     a__    =self.num_labels
     a__    =MarkupLMForSequenceClassification(lowercase_			)
     model.to(lowercase_			)
     model.eval()
     a__    =model(
         lowercase_,       xpath_tags_seq=lowercase_,       xpath_subs_seq=lowercase_,       attention_mask=lowercase_,       token_type_ids=lowercase_,       labels=lowercase_,       )
     self.parent.assertEqual(result.logits.shape,       (self.batch_size, self.num_labels)			)



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_,       )				->       Union[str, Any]:






     """simple docstring"""






     a__    =self.num_labels
     a__    =MarkupLMForTokenClassification(config=lowercase_			)
     model.to(lowercase_			)
     model.eval()
     a__    =model(
         lowercase_,       xpath_tags_seq=lowercase_,       xpath_subs_seq=lowercase_,       attention_mask=lowercase_,       token_type_ids=lowercase_,       labels=lowercase_,       )
     self.parent.assertEqual(result.logits.shape,       (self.batch_size, self.seq_length, self.num_labels)			)



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_,       )				->       Any:






     """simple docstring"""






     a__    =MarkupLMForQuestionAnswering(config=lowercase_			)
     model.to(lowercase_			)
     model.eval()
     a__    =model(
         lowercase_,       xpath_tags_seq=lowercase_,       xpath_subs_seq=lowercase_,       attention_mask=lowercase_,       token_type_ids=lowercase_,       start_positions=lowercase_,       end_positions=lowercase_,       )
     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 					_UpperCAmelCase					(							self			)				->       Union[str, Any]:






     """simple docstring"""






     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 		__magic_name__     (				lowerCamelCase__						,			lowerCamelCase__						,			unittest.TestCase				):

   '''simple docstring'''




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



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_			)				->       Union[str, Any]:






     """simple docstring"""






     return True



   def 					_UpperCAmelCase					(							self			)				->       List[str]:






     """simple docstring"""






     a__    =MarkupLMModelTester(self			)
     a__    =ConfigTester(self,       config_class=lowercase_,       hidden_size=37			)



   def 					_UpperCAmelCase					(							self			)				->       Union[str, Any]:






     """simple docstring"""






     self.config_tester.run_common_tests()



   def 					_UpperCAmelCase					(							self			)				->       List[str]:






     """simple docstring"""






     a__    =self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_			)



   def 					_UpperCAmelCase					(							self			)				->       Optional[int]:






     """simple docstring"""






     a__    =self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*lowercase_			)



   def 					_UpperCAmelCase					(							self			)				->       Tuple:






     """simple docstring"""






     a__    =self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*lowercase_			)



   def 					_UpperCAmelCase					(							self			)				->       int:






     """simple docstring"""






     a__    =self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*lowercase_			)





def      UpperCAmelCase__							(     ):





  '''simple docstring'''







  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 		__magic_name__     (				unittest.TestCase				):

   '''simple docstring'''




   @cached_property
   def 					_UpperCAmelCase					(							self			)				->       List[str]:






     """simple docstring"""






     a__    =MarkupLMFeatureExtractor()
     a__    =MarkupLMTokenizer.from_pretrained('''microsoft/markuplm-base'''			)

     return MarkupLMProcessor(lowercase_,       lowercase_			)



   @slow
   def 					_UpperCAmelCase					(							self			)				->       Optional[Any]:






     """simple docstring"""






     a__    =MarkupLMModel.from_pretrained('''microsoft/markuplm-base'''			).to(lowercase_			)

     a__    =self.default_processor

     a__    =processor(prepare_html_string(),       return_tensors='''pt'''			)
     a__    =inputs.to(lowercase_			)

     # forward pass
     with torch.no_grad():
       a__    =model(**lowercase_			)

     # verify the last hidden states
     a__    =torch.Size([1, 14, 768]			)
     self.assertEqual(outputs.last_hidden_state.shape,       lowercase_			)

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

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