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 		a__						:

					def __init__(   self				:	Any,_A				:	str,_A				:	Dict=13,_A				:	Union[str, Any]=7,_A				:	Dict=True,_A				:	int=True,_A				:	Union[str, Any]=True,_A				:	str=True,_A				:	Tuple=99,_A				:	Optional[Any]=32,_A				:	Optional[int]=5,_A				:	List[str]=4,_A				:	str=37,_A				:	Dict="gelu",_A				:	Dict=0.1,_A				:	List[Any]=0.1,_A				:	Optional[int]=512,_A				:	List[Any]=16,_A				:	Optional[int]=2,_A				:	List[Any]=0.02,_A				:	List[Any]=3,_A				:	Optional[Any]=None,_A				:	int=20,_A				:	List[Any]=30,_A				:	Optional[Any]=2,_A				:	str=2,_A				:	int=10,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   parent
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   batch_size
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   seq_length
							SCREAMING_SNAKE_CASE_  :		int    				=   is_training
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   use_input_mask
							SCREAMING_SNAKE_CASE_  :		Dict    				=   use_token_type_ids
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   use_labels
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   vocab_size
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   hidden_size
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   num_hidden_layers
							SCREAMING_SNAKE_CASE_  :		Any    				=   num_attention_heads
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   intermediate_size
							SCREAMING_SNAKE_CASE_  :		Any    				=   hidden_act
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   hidden_dropout_prob
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   max_position_embeddings
							SCREAMING_SNAKE_CASE_  :		Any    				=   type_vocab_size
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   type_sequence_label_size
							SCREAMING_SNAKE_CASE_  :		str    				=   initializer_range
							SCREAMING_SNAKE_CASE_  :		str    				=   num_labels
							SCREAMING_SNAKE_CASE_  :		Dict    				=   scope
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   max_xpath_tag_unit_embeddings
							SCREAMING_SNAKE_CASE_  :		Any    				=   max_xpath_subs_unit_embeddings
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   tag_pad_id
							SCREAMING_SNAKE_CASE_  :		Any    				=   subs_pad_id
							SCREAMING_SNAKE_CASE_  :		str    				=   max_depth

					def     __UpperCamelCase       (   self				:	Optional[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   ids_tensor([self.batch_size, self.seq_length],self.vocab_size   )

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

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

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

							SCREAMING_SNAKE_CASE_  :		Tuple    				=   None
							if self.use_token_type_ids:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   ids_tensor([self.batch_size, self.seq_length],self.type_vocab_size   )

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

							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   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				:	Dict   ):







							"""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				:	Dict,_A				:	Tuple,_A				:	str,_A				:	Union[str, Any],_A				:	Union[str, Any],_A				:	Any,_A				:	Tuple,_A				:	List[str],_A				:	List[str],):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   MarkupLMModel(config=_A   )
							model.to(_A   )
							model.eval()
							print("Configs:",model.config.tag_pad_id,model.config.subs_pad_id   )
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   model(_A,attention_mask=_A,token_type_ids=_A   )
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   model(_A,token_type_ids=_A   )
							SCREAMING_SNAKE_CASE_  :		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     __UpperCamelCase       (   self				:	Tuple,_A				:	List[str],_A				:	int,_A				:	Dict,_A				:	Union[str, Any],_A				:	Any,_A				:	str,_A				:	Optional[Any],_A				:	List[Any],):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   self.num_labels
							SCREAMING_SNAKE_CASE_  :		Dict    				=   MarkupLMForSequenceClassification(_A   )
							model.to(_A   )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		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.num_labels)   )

					def     __UpperCamelCase       (   self				:	Optional[int],_A				:	Dict,_A				:	Optional[int],_A				:	int,_A				:	str,_A				:	List[str],_A				:	Union[str, Any],_A				:	Any,_A				:	Tuple,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple    				=   self.num_labels
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   MarkupLMForTokenClassification(config=_A   )
							model.to(_A   )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   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     __UpperCamelCase       (   self				:	Optional[int],_A				:	Dict,_A				:	int,_A				:	Optional[int],_A				:	Dict,_A				:	Tuple,_A				:	List[str],_A				:	int,_A				:	List[Any],):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   MarkupLMForQuestionAnswering(config=_A   )
							model.to(_A   )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   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     __UpperCamelCase       (   self				:	Optional[int]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   self.prepare_config_and_inputs()
							(
							    (
							    SCREAMING_SNAKE_CASE_
							)				,   (
							    SCREAMING_SNAKE_CASE_
							)				,   (
							    SCREAMING_SNAKE_CASE_
							)				,   (
							    SCREAMING_SNAKE_CASE_
							)				,   (
							    SCREAMING_SNAKE_CASE_
							)				,   (
							    SCREAMING_SNAKE_CASE_
							)				,   (
							    SCREAMING_SNAKE_CASE_
							)				,   (
							    SCREAMING_SNAKE_CASE_
							)				,   
							)  :		Dict    				=   config_and_inputs
							SCREAMING_SNAKE_CASE_  :		Dict    				=   {
							    "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 		a__						( A__   ,		A__   ,		unittest.TestCase ):
					A     	=							(
					    (
					        MarkupLMModel,
					        MarkupLMForSequenceClassification,
					        MarkupLMForTokenClassification,
					        MarkupLMForQuestionAnswering,
					    )
					    if is_torch_available()
					    else None
					)
					A     	=							(
					    {
					        'feature-extraction': MarkupLMModel,
					        'question-answering': MarkupLMForQuestionAnswering,
					        'text-classification': MarkupLMForSequenceClassification,
					        'token-classification': MarkupLMForTokenClassification,
					        'zero-shot': MarkupLMForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)

					def     __UpperCamelCase       (   self				:	int,_A				:	str,_A				:	List[str],_A				:	Dict,_A				:	Optional[int],_A				:	List[str]   ):







							"""simple docstring"""






							return True

					def     __UpperCamelCase       (   self				:	int   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   MarkupLMModelTester(self   )
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   ConfigTester(self,config_class=_A,hidden_size=37   )

					def     __UpperCamelCase       (   self				:	Dict   ):







							"""simple docstring"""






							self.config_tester.run_common_tests()

					def     __UpperCamelCase       (   self				:	List[str]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_A   )

					def     __UpperCamelCase       (   self				:	Dict   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str    				=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*_A   )

					def     __UpperCamelCase       (   self				:	str   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*_A   )






					def     __UpperCamelCase       (   self				:	Optional[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*_A   )







def 				_snake_case     (       ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   "\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 		a__						( unittest.TestCase ):

					@cached_property
					def     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   MarkupLMFeatureExtractor()
							SCREAMING_SNAKE_CASE_  :		Dict    				=   MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base"   )

							return MarkupLMProcessor(_A,_A   )






					@slow
					def     __UpperCamelCase       (   self				:	Union[str, Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   MarkupLMModel.from_pretrained("microsoft/markuplm-base"   ).to(_A   )

							SCREAMING_SNAKE_CASE_  :		Any    				=   self.default_processor

							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   processor(prepare_html_string(),return_tensors="pt"   )
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   inputs.to(_A   )

							# forward pass
							with torch.no_grad():
									SCREAMING_SNAKE_CASE_  :		Any    				=   model(**_A   )

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

							SCREAMING_SNAKE_CASE_  :		Any    				=   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   )   )

import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


__lowerCamelCase :     Tuple          =  logging.get_logger(__name__)






class 		a__						( A__ ):

					def __init__(   self				:	List[Any],*_A				:	Union[str, Any],**_A				:	List[Any]   ):







							"""simple docstring"""






							warnings.warn(
							    "The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
							    " use ViTImageProcessor instead.",_A,)
							super().__init__(*_A,**_A   )
