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        __UpperCAmelCase						:

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

				def 		_a     (				self				):
										lowerCamelCase__ =ids_tensor([self.batch_size, self.seq_length]     ,			self.vocab_size				)

										lowerCamelCase__ =ids_tensor(
										    [self.batch_size, self.seq_length, self.max_depth]     ,			self.max_xpath_tag_unit_embeddings				)

										lowerCamelCase__ =ids_tensor(
										    [self.batch_size, self.seq_length, self.max_depth]     ,			self.max_xpath_subs_unit_embeddings				)

										lowerCamelCase__ =None
										if self.use_input_mask:
																lowerCamelCase__ =ids_tensor([self.batch_size, self.seq_length]     ,			vocab_size=2				)

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

										lowerCamelCase__ =None
										lowerCamelCase__ =None
										if self.use_labels:
																lowerCamelCase__ =ids_tensor([self.batch_size]     ,			self.type_sequence_label_size				)
																lowerCamelCase__ =ids_tensor([self.batch_size, self.seq_length]     ,			self.num_labels				)

										lowerCamelCase__ =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				):
										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     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			):
										lowerCamelCase__ =MarkupLMModel(config=_A				)
										model.to(_A				)
										model.eval()
										print("Configs:"     ,			model.config.tag_pad_id     ,			model.config.subs_pad_id				)
										lowerCamelCase__ =model(_A     ,			attention_mask=_A     ,			token_type_ids=_A				)
										lowerCamelCase__ =model(_A     ,			token_type_ids=_A				)
										lowerCamelCase__ =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 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			):
										lowerCamelCase__ =self.num_labels
										lowerCamelCase__ =MarkupLMForSequenceClassification(_A				)
										model.to(_A				)
										model.eval()
										lowerCamelCase__ =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 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			):
										lowerCamelCase__ =self.num_labels
										lowerCamelCase__ =MarkupLMForTokenClassification(config=_A				)
										model.to(_A				)
										model.eval()
										lowerCamelCase__ =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 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			):
										lowerCamelCase__ =MarkupLMForQuestionAnswering(config=_A				)
										model.to(_A				)
										model.eval()
										lowerCamelCase__ =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 		_a     (				self				):
										lowerCamelCase__ =self.prepare_config_and_inputs()
										(
										    lowerCamelCase__   
										) =config_and_inputs
										lowerCamelCase__ ={
										    "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        __UpperCAmelCase						(     A__				,				A__				,				unittest.TestCase     ):
				A__				:       Tuple  							=	(
				    (
				        MarkupLMModel,
				        MarkupLMForSequenceClassification,
				        MarkupLMForTokenClassification,
				        MarkupLMForQuestionAnswering,
				    )
				    if is_torch_available()
				    else None
				)
				A__				:       Dict  							=	(
				    {
				        '''feature-extraction''': MarkupLMModel,
				        '''question-answering''': MarkupLMForQuestionAnswering,
				        '''text-classification''': MarkupLMForSequenceClassification,
				        '''token-classification''': MarkupLMForTokenClassification,
				        '''zero-shot''': MarkupLMForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase				):
										return True

				def 		_a     (				self				):
										lowerCamelCase__ =MarkupLMModelTester(self				)
										lowerCamelCase__ =ConfigTester(self     ,			config_class=_A     ,			hidden_size=37				)

				def 		_a     (				self				):
										self.config_tester.run_common_tests()

				def 		_a     (				self				):
										lowerCamelCase__ =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_A				)

				def 		_a     (				self				):
										lowerCamelCase__ =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*_A				)

				def 		_a     (				self				):
										lowerCamelCase__ =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*_A				)






				def 		_a     (				self				):
										lowerCamelCase__ =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*_A				)





def       lowerCamelCase_			( )							-> Any:




						'''simple docstring'''



						lowerCamelCase__ ="\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        __UpperCAmelCase						(     unittest.TestCase     ):

				@cached_property
				def 		_a     (				self				):
										lowerCamelCase__ =MarkupLMFeatureExtractor()
										lowerCamelCase__ =MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base"				)

										return MarkupLMProcessor(_A     ,			_A				)






				@slow
				def 		_a     (				self				):
										lowerCamelCase__ =MarkupLMModel.from_pretrained("microsoft/markuplm-base"				).to(_A				)

										lowerCamelCase__ =self.default_processor

										lowerCamelCase__ =processor(prepare_html_string()     ,			return_tensors="pt"				)
										lowerCamelCase__ =inputs.to(_A				)

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

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

										lowerCamelCase__ =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				)				)



"""simple docstring"""


import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class        __UpperCAmelCase						(     __lowerCAmelCase				,				__lowerCAmelCase				,				unittest.TestCase     ):
				A__				:       Any  							=	IFSuperResolutionPipeline
				A__				:       Tuple  							=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
				A__				:       Optional[int]  							=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				A__				:       List[Any]  							=	PipelineTesterMixin.required_optional_params - {'''latents'''}

				def 		_a     (				self				):
										return self._get_superresolution_dummy_components()

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase=0				):
										if str(_lowerCamelCase				).startswith("mps"				):
																lowerCamelCase__ =torch.manual_seed(_lowerCamelCase				)
										else:
																lowerCamelCase__ =torch.Generator(device=_lowerCamelCase				).manual_seed(_lowerCamelCase				)

										lowerCamelCase__ =floats_tensor((1, 3, 32, 32)     ,			rng=random.Random(_lowerCamelCase				)				).to(_lowerCamelCase				)

										lowerCamelCase__ ={
										    "prompt": "A painting of a squirrel eating a burger",
										    "image": image,
										    "generator": generator,
										    "num_inference_steps": 2,
										    "output_type": "numpy",
										}

										return inputs

				@unittest.skipIf(
				    torch_device != "cuda" or not is_xformers_available()     ,			reason="XFormers attention is only available with CUDA and `xformers` installed"     ,			)
				def 		_a     (				self				):
										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)

				def 		_a     (				self				):
										self._test_save_load_optional_components()

				@unittest.skipIf(torch_device != "cuda"     ,			reason="float16 requires CUDA"				)
				def 		_a     (				self				):
										# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
										super().test_save_load_floataa(expected_max_diff=1E-1				)

				def 		_a     (				self				):
										self._test_attention_slicing_forward_pass(expected_max_diff=1E-2				)

				def 		_a     (				self				):
										self._test_save_load_local()






				def 		_a     (				self				):
										self._test_inference_batch_single_identical(
										    expected_max_diff=1E-2     ,			)


