
"""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 			_lowercase  :




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




	def      _UpperCamelCase       (					self   )						->				Optional[Any]:
			lowerCamelCase	:     str            =       ids_tensor([self.batch_size, self.seq_length]						,					self.vocab_size   )

			lowerCamelCase	:     str            =       ids_tensor(
			    [self.batch_size, self.seq_length, self.max_depth]						,					self.max_xpath_tag_unit_embeddings   )

			lowerCamelCase	:     Optional[int]            =       ids_tensor(
			    [self.batch_size, self.seq_length, self.max_depth]						,					self.max_xpath_subs_unit_embeddings   )

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

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

			lowerCamelCase	:     Optional[int]            =       None
			lowerCamelCase	:     Optional[Any]            =       None
			if self.use_labels:
					lowerCamelCase	:     Optional[int]            =       ids_tensor([self.batch_size]						,					self.type_sequence_label_size   )
					lowerCamelCase	:     Optional[int]            =       ids_tensor([self.batch_size, self.seq_length]						,					self.num_labels   )

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




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )						->				Optional[Any]:
			# ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
			# (batch of pretokenized examples).
			return True




	def      _UpperCamelCase       (					self   )						->				Any:
			lowerCamelCase	:     Optional[Any]            =       MarkupLMModelTester(self   )
			lowerCamelCase	:     str            =       ConfigTester(self						,					config_class=__lowerCamelCase						,					hidden_size=37   )




	def      _UpperCamelCase       (					self   )						->				Dict:
			self.config_tester.run_common_tests()




	def      _UpperCamelCase       (					self   )						->				List[Any]:
			lowerCamelCase	:     Optional[Any]            =       self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*__lowerCamelCase   )




	def      _UpperCamelCase       (					self   )						->				str:
			lowerCamelCase	:     Optional[int]            =       self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase   )




	def      _UpperCamelCase       (					self   )						->				List[Any]:
			lowerCamelCase	:     str            =       self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase   )




	def      _UpperCamelCase       (					self   )						->				Optional[Any]:
			lowerCamelCase	:     Any            =       self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase   )


def 						UpperCAmelCase   (						):





		'''simple docstring'''


		lowerCamelCase	:     str            =       '\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 			_lowercase  (							unittest.TestCase						):




	@cached_property
	def      _UpperCamelCase       (					self   )						->				Union[str, Any]:
			# TODO use from_pretrained here
			lowerCamelCase	:     Dict            =       MarkupLMFeatureExtractor()
			lowerCamelCase	:     Any            =       MarkupLMTokenizer.from_pretrained('microsoft/markuplm-base'   )

			return MarkupLMProcessor(__lowerCamelCase						,					__lowerCamelCase   )




	@slow
	def      _UpperCamelCase       (					self   )						->				List[Any]:
			lowerCamelCase	:     str            =       MarkupLMModel.from_pretrained('microsoft/markuplm-base'   ).to(__lowerCamelCase   )

			lowerCamelCase	:     str            =       self.default_processor

			lowerCamelCase	:     List[Any]            =       processor(prepare_html_string()						,					return_tensors='pt'   )
			lowerCamelCase	:     Tuple            =       inputs.to(__lowerCamelCase   )

			# forward pass
			with torch.no_grad():
					lowerCamelCase	:     Any            =       model(**__lowerCamelCase   )

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

			lowerCamelCase	:     Optional[int]            =       torch.tensor(
			    [[0.0675, -0.0052, 0.5001], [-0.2281, 0.0802, 0.2192], [-0.0583, -0.3311, 0.1185]]   ).to(__lowerCamelCase   )

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














"""simple docstring"""

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
			import torch

			from transformers import CamembertModel

@require_torch
@require_sentencepiece
@require_tokenizers
class     A_				(   unittest.TestCase	):







				@slow
				def      _snake_case       (				self				:    Tuple						)					->       Dict:
										__magic_name__      =  CamembertModel.from_pretrained("camembert-base"						)
										model.to(__lowerCamelCase						)

										__magic_name__      =  torch.tensor(
										    [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]    ,     device=__lowerCamelCase    ,     dtype=torch.long    ,     )  # J'aime le camembert !
										with torch.no_grad():
																__magic_name__      =  model(__lowerCamelCase						)["last_hidden_state"]
										__magic_name__      =  torch.Size((1, 1_0, 7_6_8)						)
										self.assertEqual(output.shape    ,     __lowerCamelCase						)
										# compare the actual values for a slice.
										__magic_name__      =  torch.tensor(
										    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]    ,     device=__lowerCamelCase    ,     dtype=torch.float    ,     )
										# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
										# camembert.eval()
										# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

										self.assertTrue(torch.allclose(output[:, :3, :3]    ,     __lowerCamelCase    ,     atol=1e-4						)						)



