

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     lowerCamelCase_			:




	def __init__(    self	:    List[Any]				,			__A	:    List[Any]				,			__A	:    List[Any]=13				,			__A	:    Dict=7				,			__A	:    Tuple=True				,			__A	:    Optional[Any]=True				,			__A	:    List[Any]=True				,			__A	:    str=True				,			__A	:    int=99				,			__A	:    Any=32				,			__A	:    str=5				,			__A	:    List[str]=4				,			__A	:    Optional[Any]=37				,			__A	:    str="gelu"				,			__A	:    Optional[int]=0.1				,			__A	:    Any=0.1				,			__A	:    Union[str, Any]=512				,			__A	:    Optional[int]=16				,			__A	:    List[Any]=2				,			__A	:    Optional[Any]=0.0_2				,			__A	:    Optional[Any]=3				,			__A	:    Tuple=None				,			__A	:    Optional[Any]=20				,			__A	:    str=30				,			__A	:    Optional[int]=2				,			__A	:    Dict=2				,			__A	:    str=10				,			):
		__A   : Dict								= parent
		__A   : Dict								= batch_size
		__A   : List[str]								= seq_length
		__A   : List[str]								= is_training
		__A   : Tuple								= use_input_mask
		__A   : str								= use_token_type_ids
		__A   : str								= use_labels
		__A   : List[Any]								= vocab_size
		__A   : int								= hidden_size
		__A   : Optional[Any]								= num_hidden_layers
		__A   : int								= num_attention_heads
		__A   : Any								= intermediate_size
		__A   : Optional[Any]								= hidden_act
		__A   : int								= hidden_dropout_prob
		__A   : int								= attention_probs_dropout_prob
		__A   : Optional[Any]								= max_position_embeddings
		__A   : Any								= type_vocab_size
		__A   : List[Any]								= type_sequence_label_size
		__A   : int								= initializer_range
		__A   : Tuple								= num_labels
		__A   : List[str]								= scope
		__A   : List[Any]								= max_xpath_tag_unit_embeddings
		__A   : Union[str, Any]								= max_xpath_subs_unit_embeddings
		__A   : Dict								= tag_pad_id
		__A   : int								= subs_pad_id
		__A   : Optional[int]								= max_depth




	def 					lowerCAmelCase_					(    self	:    Union[str, Any]       ):
		__A   : Any								= ids_tensor([self.batch_size, self.seq_length]				,			self.vocab_size       )

		__A   : Union[str, Any]								= ids_tensor(
		    [self.batch_size, self.seq_length, self.max_depth]				,			self.max_xpath_tag_unit_embeddings       )

		__A   : Tuple								= ids_tensor(
		    [self.batch_size, self.seq_length, self.max_depth]				,			self.max_xpath_subs_unit_embeddings       )

		__A   : List[Any]								= None
		if self.use_input_mask:
			__A   : str								= ids_tensor([self.batch_size, self.seq_length]				,			vocab_size=2       )

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

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

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




	def 					lowerCAmelCase_					(    self	:    Tuple				,			__A	:    str				,			__A	:    Tuple				,			__A	:    List[Any]				,			__A	:    Tuple				,			__A	:    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       ):
		__A   : List[Any]								= MarkupLMModelTester(self       )
		__A   : Any								= ConfigTester(self				,			config_class=__A				,			hidden_size=37       )




	def 					lowerCAmelCase_					(    self	:    Tuple       ):
		self.config_tester.run_common_tests()




	def 					lowerCAmelCase_					(    self	:    List[str]       ):
		__A   : str								= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*__A       )




	def 					lowerCAmelCase_					(    self	:    str       ):
		__A   : Tuple								= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_sequence_classification(*__A       )




	def 					lowerCAmelCase_					(    self	:    Tuple       ):
		__A   : Tuple								= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_token_classification(*__A       )




	def 					lowerCAmelCase_					(    self	:    Any       ):
		__A   : Optional[Any]								= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_question_answering(*__A       )






def        __SCREAMING_SNAKE_CASE    (      )				->							int:
	__A   : Tuple								= """
    <!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     lowerCamelCase_			(  unittest.TestCase  ):




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

		return MarkupLMProcessor(__A				,			__A       )




	@slow
	def 					lowerCAmelCase_					(    self	:    List[str]       ):
		__A   : Dict								= MarkupLMModel.from_pretrained("""microsoft/markuplm-base"""       ).to(__A       )

		__A   : Any								= self.default_processor

		__A   : Optional[Any]								= processor(prepare_html_string()				,			return_tensors="""pt"""       )
		__A   : List[Any]								= inputs.to(__A       )

		# forward pass
		with torch.no_grad():
			__A   : Optional[int]								= model(**__A       )

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

		__A   : List[Any]								= torch.tensor(
		    [[0.0_6_7_5, -0.0_0_5_2, 0.5_0_0_1], [-0.2_2_8_1, 0.0_8_0_2, 0.2_1_9_2], [-0.0_5_8_3, -0.3_3_1_1, 0.1_1_8_5]]       ).to(__A       )

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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_       :				Optional[Any]		   =      {
    '''configuration_prophetnet''': ['''PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ProphetNetConfig'''],
    '''tokenization_prophetnet''': ['''ProphetNetTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_       :				List[str]		   =      [
			    '''PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''ProphetNetDecoder''',
			    '''ProphetNetEncoder''',
			    '''ProphetNetForCausalLM''',
			    '''ProphetNetForConditionalGeneration''',
			    '''ProphetNetModel''',
			    '''ProphetNetPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
			from .tokenization_prophetnet import ProphetNetTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_prophetnet import (
						    PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ProphetNetDecoder,
						    ProphetNetEncoder,
						    ProphetNetForCausalLM,
						    ProphetNetForConditionalGeneration,
						    ProphetNetModel,
						    ProphetNetPreTrainedModel,
						)

else:
			import sys

			UpperCAmelCase_       :				Any		   =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





