






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        _SCREAMING_SNAKE_CASE			:





	def __init__(						self							:   List[Any]					,    __lowerCamelCase							:   List[str]					,    __lowerCamelCase							:   Union[str, Any]=13					,    __lowerCamelCase							:   List[str]=7					,    __lowerCamelCase							:   Optional[int]=True					,    __lowerCamelCase							:   List[str]=True					,    __lowerCamelCase							:   Dict=True					,    __lowerCamelCase							:   Dict=True					,    __lowerCamelCase							:   Dict=99					,    __lowerCamelCase							:   Union[str, Any]=32					,    __lowerCamelCase							:   Any=5					,    __lowerCamelCase							:   Union[str, Any]=4					,    __lowerCamelCase							:   Optional[Any]=37					,    __lowerCamelCase							:   int="gelu"					,    __lowerCamelCase							:   Optional[Any]=0.1					,    __lowerCamelCase							:   List[Any]=0.1					,    __lowerCamelCase							:   List[str]=512					,    __lowerCamelCase							:   Dict=16					,    __lowerCamelCase							:   int=2					,    __lowerCamelCase							:   Union[str, Any]=0.02					,    __lowerCamelCase							:   Optional[int]=3					,    __lowerCamelCase							:   List[str]=None					,    __lowerCamelCase							:   Any=20					,    __lowerCamelCase							:   Dict=30					,    __lowerCamelCase							:   Optional[int]=2					,    __lowerCamelCase							:   Optional[Any]=2					,    __lowerCamelCase							:   int=10					,    ):
		UpperCamelCase	:Optional[Any]		=      parent
		UpperCamelCase	:Optional[int]		=      batch_size
		UpperCamelCase	:Any		=      seq_length
		UpperCamelCase	:Optional[int]		=      is_training
		UpperCamelCase	:Union[str, Any]		=      use_input_mask
		UpperCamelCase	:str		=      use_token_type_ids
		UpperCamelCase	:int		=      use_labels
		UpperCamelCase	:Optional[Any]		=      vocab_size
		UpperCamelCase	:int		=      hidden_size
		UpperCamelCase	:Tuple		=      num_hidden_layers
		UpperCamelCase	:Dict		=      num_attention_heads
		UpperCamelCase	:Union[str, Any]		=      intermediate_size
		UpperCamelCase	:List[Any]		=      hidden_act
		UpperCamelCase	:int		=      hidden_dropout_prob
		UpperCamelCase	:Dict		=      attention_probs_dropout_prob
		UpperCamelCase	:List[Any]		=      max_position_embeddings
		UpperCamelCase	:str		=      type_vocab_size
		UpperCamelCase	:Tuple		=      type_sequence_label_size
		UpperCamelCase	:Any		=      initializer_range
		UpperCamelCase	:Optional[int]		=      num_labels
		UpperCamelCase	:Tuple		=      scope
		UpperCamelCase	:Tuple		=      max_xpath_tag_unit_embeddings
		UpperCamelCase	:Optional[Any]		=      max_xpath_subs_unit_embeddings
		UpperCamelCase	:List[str]		=      tag_pad_id
		UpperCamelCase	:Tuple		=      subs_pad_id
		UpperCamelCase	:List[Any]		=      max_depth





	def 		_A					(						self							:   Union[str, Any]      ):
		UpperCamelCase	:Tuple		=      ids_tensor([self.batch_size, self.seq_length]					,    self.vocab_size      )

		UpperCamelCase	:Optional[int]		=      ids_tensor(
		    [self.batch_size, self.seq_length, self.max_depth]					,    self.max_xpath_tag_unit_embeddings      )

		UpperCamelCase	:str		=      ids_tensor(
		    [self.batch_size, self.seq_length, self.max_depth]					,    self.max_xpath_subs_unit_embeddings      )

		UpperCamelCase	:Optional[Any]		=      None
		if self.use_input_mask:
			UpperCamelCase	:int		=      ids_tensor([self.batch_size, self.seq_length]					,    vocab_size=2      )

		UpperCamelCase	:str		=      None
		if self.use_token_type_ids:
			UpperCamelCase	:List[Any]		=      ids_tensor([self.batch_size, self.seq_length]					,    self.type_vocab_size      )

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

		UpperCamelCase	:Any		=      self.get_config()

		return (
		    config,
		    input_ids,
		    xpath_tags_seq,
		    xpath_subs_seq,
		    token_type_ids,
		    input_mask,
		    sequence_labels,
		    token_labels,
		)





	def 		_A					(						self							:   List[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 		_A					(						self							:   Optional[int]					,    __lowerCamelCase							:   Union[str, Any]					,    __lowerCamelCase							:   Union[str, Any]					,    __lowerCamelCase							:   Union[str, Any]					,    __lowerCamelCase							:   int					,    __lowerCamelCase							:   Optional[int]					,    __lowerCamelCase							:   Tuple					,    __lowerCamelCase							:   Any					,    __lowerCamelCase							:   Tuple					,    ):
		UpperCamelCase	:str		=      MarkupLMModel(config=__lowercase      )
		model.to(__lowercase      )
		model.eval()
		print("""Configs:"""					,    model.config.tag_pad_id					,    model.config.subs_pad_id      )
		UpperCamelCase	:Union[str, Any]		=      model(__lowercase					,    attention_mask=__lowercase					,    token_type_ids=__lowercase      )
		UpperCamelCase	:List[Any]		=      model(__lowercase					,    token_type_ids=__lowercase      )
		UpperCamelCase	:List[str]		=      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 		_A					(						self							:   List[Any]					,    __lowerCamelCase							:   Any					,    __lowerCamelCase							:   Union[str, Any]					,    __lowerCamelCase							:   Optional[Any]					,    __lowerCamelCase							:   Tuple					,    __lowerCamelCase							:   Optional[Any]					,    __lowerCamelCase							:   List[Any]					,    __lowerCamelCase							:   Any					,    __lowerCamelCase							:   Any					,    ):
		UpperCamelCase	:Any		=      self.num_labels
		UpperCamelCase	:List[Any]		=      MarkupLMForSequenceClassification(__lowercase      )
		model.to(__lowercase      )
		model.eval()
		UpperCamelCase	:Optional[Any]		=      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 		_A					(						self							:   Tuple					,    __lowerCamelCase							:   List[str]					,    __lowerCamelCase							:   Union[str, Any]					,    __lowerCamelCase							:   str					,    __lowerCamelCase							:   Tuple					,    __lowerCamelCase							:   List[Any]					,    __lowerCamelCase							:   int					,    __lowerCamelCase							:   Union[str, Any]					,    __lowerCamelCase							:   Union[str, Any]					,    ):
		UpperCamelCase	:List[Any]		=      self.num_labels
		UpperCamelCase	:int		=      MarkupLMForTokenClassification(config=__lowercase      )
		model.to(__lowercase      )
		model.eval()
		UpperCamelCase	:Optional[Any]		=      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 		_A					(						self							:   Optional[Any]					,    __lowerCamelCase							:   str					,    __lowerCamelCase							:   List[Any]					,    __lowerCamelCase							:   str					,    __lowerCamelCase							:   Any					,    __lowerCamelCase							:   List[Any]					,    __lowerCamelCase							:   int					,    __lowerCamelCase							:   Dict					,    __lowerCamelCase							:   Optional[int]					,    ):
		UpperCamelCase	:Dict		=      MarkupLMForQuestionAnswering(config=__lowercase      )
		model.to(__lowercase      )
		model.eval()
		UpperCamelCase	:Union[str, Any]		=      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 		_A					(						self							:   Optional[Any]      ):
		UpperCamelCase	:Dict		=      self.prepare_config_and_inputs()
		(
		    (
		    UpperCamelCase	
		)	, (
		    UpperCamelCase	
		)	, (
		    UpperCamelCase	
		)	, (
		    UpperCamelCase	
		)	, (
		    UpperCamelCase	
		)	, (
		    UpperCamelCase	
		)	, (
		    UpperCamelCase	
		)	, (
		    UpperCamelCase	
		)	, 	
		)	:Dict		=      config_and_inputs
		UpperCamelCase	:List[str]		=      {
		    """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        _SCREAMING_SNAKE_CASE			(     __UpperCamelCase						,  __UpperCamelCase						,  unittest.TestCase ):
	snake_case__				:	Any	      =    (
	    (
	        MarkupLMModel,
	        MarkupLMForSequenceClassification,
	        MarkupLMForTokenClassification,
	        MarkupLMForQuestionAnswering,
	    )
	    if is_torch_available()
	    else None
	)
	snake_case__				:	Optional[Any]	      =    (
	    {
	        'feature-extraction': MarkupLMModel,
	        'question-answering': MarkupLMForQuestionAnswering,
	        'text-classification': MarkupLMForSequenceClassification,
	        'token-classification': MarkupLMForTokenClassification,
	        'zero-shot': MarkupLMForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)





	def 		_A					(						self							:   int					,    __lowerCamelCase							:   Any					,    __lowerCamelCase							:   Dict					,    __lowerCamelCase							:   Tuple					,    __lowerCamelCase							:   Optional[int]					,    __lowerCamelCase							:   Tuple      ):
		# ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
		# (batch of pretokenized examples).
		return True





	def 		_A					(						self							:   Dict      ):
		UpperCamelCase	:Union[str, Any]		=      MarkupLMModelTester(self      )
		UpperCamelCase	:Optional[int]		=      ConfigTester(self					,    config_class=__lowercase					,    hidden_size=37      )





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





	def 		_A					(						self							:   Tuple      ):
		UpperCamelCase	:Optional[Any]		=      self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*__lowercase      )





	def 		_A					(						self							:   Dict      ):
		UpperCamelCase	:Union[str, Any]		=      self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_sequence_classification(*__lowercase      )





	def 		_A					(						self							:   Dict      ):
		UpperCamelCase	:str		=      self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_token_classification(*__lowercase      )





	def 		_A					(						self							:   Dict      ):
		UpperCamelCase	:Tuple		=      self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_question_answering(*__lowercase      )

def   SCREAMING_SNAKE_CASE_				(				)						->     List[Any]:






	"""simple docstring"""



	UpperCamelCase	:Dict		=      """
    <!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        _SCREAMING_SNAKE_CASE			(     unittest.TestCase ):





	@cached_property
	def 		_A					(						self							:   List[str]      ):
		# TODO use from_pretrained here
		UpperCamelCase	:Dict		=      MarkupLMFeatureExtractor()
		UpperCamelCase	:Any		=      MarkupLMTokenizer.from_pretrained("""microsoft/markuplm-base"""      )

		return MarkupLMProcessor(__lowercase					,    __lowercase      )





	@slow
	def 		_A					(						self							:   Union[str, Any]      ):
		UpperCamelCase	:List[Any]		=      MarkupLMModel.from_pretrained("""microsoft/markuplm-base"""      ).to(__lowercase      )

		UpperCamelCase	:Union[str, Any]		=      self.default_processor

		UpperCamelCase	:Tuple		=      processor(prepare_html_string()					,    return_tensors="""pt"""      )
		UpperCamelCase	:List[str]		=      inputs.to(__lowercase      )

		# forward pass
		with torch.no_grad():
			UpperCamelCase	:Any		=      model(**__lowercase      )

		# verify the last hidden states
		UpperCamelCase	:Optional[Any]		=      torch.Size([1, 14, 768]      )
		self.assertEqual(outputs.last_hidden_state.shape					,    __lowercase      )

		UpperCamelCase	:Dict		=      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      )      )








"""simple docstring"""



from math import exp




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->float:
 return 8 * x - 2 * exp(-x			)




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->float:
 UpperCAmelCase__ 						=				lower_bound
 UpperCAmelCase__ 						=				upper_bound
 for _ in range(0       ,	_SCREAMING_SNAKE_CASE			):
  UpperCAmelCase__						,	UpperCAmelCase__ 						=				xa, xa - (f(_SCREAMING_SNAKE_CASE			) * (xa - xa)) / (f(_SCREAMING_SNAKE_CASE			) - f(_SCREAMING_SNAKE_CASE			))
 return xa


if __name__ == "__main__":
 print(F'''Example: {secant_method(1, 3, 2)}''')



