
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_       :



       """simple docstring"""






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






       def 	__lowerCAmelCase     (		self   )					->str:
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size   )

           SCREAMING_SNAKE_CASE				:				Any				      = ids_tensor(
               [self.batch_size, self.seq_length, self.max_depth]				,       self.max_xpath_tag_unit_embeddings   )

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

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

           SCREAMING_SNAKE_CASE				:				Optional[int]				      = None
           if self.use_token_type_ids:
               SCREAMING_SNAKE_CASE				:				Any				      = ids_tensor([self.batch_size, self.seq_length]				,       self.type_vocab_size   )

           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = None
           SCREAMING_SNAKE_CASE				:				str				      = None
           if self.use_labels:
               SCREAMING_SNAKE_CASE				:				Optional[Any]				      = ids_tensor([self.batch_size]				,       self.type_sequence_label_size   )
               SCREAMING_SNAKE_CASE				:				Dict				      = ids_tensor([self.batch_size, self.seq_length]				,       self.num_labels   )

           SCREAMING_SNAKE_CASE				:				int				      = 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   )					->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 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       )					->Dict:
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = MarkupLMModel(config=_lowerCamelCase   )
           model.to(_lowerCamelCase   )
           model.eval()
           print('''Configs:'''				,       model.config.tag_pad_id				,       model.config.subs_pad_id   )
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = model(_lowerCamelCase				,       attention_mask=_lowerCamelCase				,       token_type_ids=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Dict				      = model(_lowerCamelCase				,       token_type_ids=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				int				      = 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 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       )					->Any:
           SCREAMING_SNAKE_CASE				:				str				      = self.num_labels
           SCREAMING_SNAKE_CASE				:				List[Any]				      = MarkupLMForSequenceClassification(_lowerCamelCase   )
           model.to(_lowerCamelCase   )
           model.eval()
           SCREAMING_SNAKE_CASE				:				List[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 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       )					->Any:
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = self.num_labels
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = MarkupLMForTokenClassification(config=_lowerCamelCase   )
           model.to(_lowerCamelCase   )
           model.eval()
           SCREAMING_SNAKE_CASE				:				int				      = 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 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase				,       )					->Dict:
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = MarkupLMForQuestionAnswering(config=_lowerCamelCase   )
           model.to(_lowerCamelCase   )
           model.eval()
           SCREAMING_SNAKE_CASE				:				Tuple				      = 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 	__lowerCAmelCase     (		self   )					->int:
           SCREAMING_SNAKE_CASE				:				Any				      = 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
           )      ,							
           )				:				str				      = config_and_inputs
           SCREAMING_SNAKE_CASE				:				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     a_       (			a__				,    a__				,    unittest.TestCase							):



       """simple docstring"""
       __SCREAMING_SNAKE_CASE       :					Optional[int]						=  (
           (
               MarkupLMModel,
               MarkupLMForSequenceClassification,
               MarkupLMForTokenClassification,
               MarkupLMForQuestionAnswering,
           )
           if is_torch_available()
           else None
       )
       __SCREAMING_SNAKE_CASE       :					Optional[int]						=  (
           {
               'feature-extraction': MarkupLMModel,
               'question-answering': MarkupLMForQuestionAnswering,
               'text-classification': MarkupLMForSequenceClassification,
               'token-classification': MarkupLMForTokenClassification,
               'zero-shot': MarkupLMForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )






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






       def 	__lowerCAmelCase     (		self   )					->Optional[Any]:
           SCREAMING_SNAKE_CASE				:				List[Any]				      = MarkupLMModelTester(self   )
           SCREAMING_SNAKE_CASE				:				str				      = ConfigTester(self				,       config_class=_lowerCamelCase				,       hidden_size=37   )






       def 	__lowerCAmelCase     (		self   )					->int:
           self.config_tester.run_common_tests()






       def 	__lowerCAmelCase     (		self   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				Tuple				      = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->Optional[Any]:
           SCREAMING_SNAKE_CASE				:				List[Any]				      = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->Tuple:
           SCREAMING_SNAKE_CASE				:				Tuple				      = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase   )






       def 	__lowerCAmelCase     (		self   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				str				      = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase   )







def 				UpperCAmelCase_(    ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = '''
    <!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     a_       (			unittest.TestCase							):



       """simple docstring"""






       @cached_property
       def 	__lowerCAmelCase     (		self   )					->Any:
           # TODO use from_pretrained here
           SCREAMING_SNAKE_CASE				:				str				      = MarkupLMFeatureExtractor()
           SCREAMING_SNAKE_CASE				:				Dict				      = MarkupLMTokenizer.from_pretrained('''microsoft/markuplm-base'''   )

           return MarkupLMProcessor(_lowerCamelCase				,       _lowerCamelCase   )






       @slow
       def 	__lowerCAmelCase     (		self   )					->int:
           SCREAMING_SNAKE_CASE				:				Tuple				      = MarkupLMModel.from_pretrained('''microsoft/markuplm-base'''   ).to(_lowerCamelCase   )

           SCREAMING_SNAKE_CASE				:				Tuple				      = self.default_processor

           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = processor(prepare_html_string()				,       return_tensors='''pt'''   )
           SCREAMING_SNAKE_CASE				:				Any				      = inputs.to(_lowerCamelCase   )

           # forward pass
           with torch.no_grad():
               SCREAMING_SNAKE_CASE				:				Any				      = model(**_lowerCamelCase   )

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

           SCREAMING_SNAKE_CASE				:				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(_lowerCamelCase   )

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


import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






@skip_mps
class     a_       (			a__				,    a__				,    a__				,    unittest.TestCase							):



       """simple docstring"""
       __SCREAMING_SNAKE_CASE       :					List[Any]						=  StableDiffusionModelEditingPipeline
       __SCREAMING_SNAKE_CASE       :					Optional[int]						=  TEXT_TO_IMAGE_PARAMS
       __SCREAMING_SNAKE_CASE       :					int						=  TEXT_TO_IMAGE_BATCH_PARAMS
       __SCREAMING_SNAKE_CASE       :					Any						=  TEXT_TO_IMAGE_IMAGE_PARAMS
       __SCREAMING_SNAKE_CASE       :					List[Any]						=  TEXT_TO_IMAGE_IMAGE_PARAMS






       def 	__lowerCAmelCase     (		self   )					->int:
           torch.manual_seed(0   )
           SCREAMING_SNAKE_CASE				:				Dict				      = UNetaDConditionModel(
               block_out_channels=(32, 64)				,       layers_per_block=2				,       sample_size=32				,       in_channels=4				,       out_channels=4				,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,       cross_attention_dim=32				,       )
           SCREAMING_SNAKE_CASE				:				Dict				      = DDIMScheduler()
           torch.manual_seed(0   )
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = AutoencoderKL(
               block_out_channels=[32, 64]				,       in_channels=3				,       out_channels=3				,       down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,       up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,       latent_channels=4				,       )
           torch.manual_seed(0   )
           SCREAMING_SNAKE_CASE				:				List[str]				      = CLIPTextConfig(
               bos_token_id=0				,       eos_token_id=2				,       hidden_size=32				,       intermediate_size=37				,       layer_norm_eps=1e-05				,       num_attention_heads=4				,       num_hidden_layers=5				,       pad_token_id=1				,       vocab_size=1000				,       )
           SCREAMING_SNAKE_CASE				:				Dict				      = CLIPTextModel(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Tuple				      = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

           SCREAMING_SNAKE_CASE				:				List[Any]				      = {
               '''unet''': unet,
               '''scheduler''': scheduler,
               '''vae''': vae,
               '''text_encoder''': text_encoder,
               '''tokenizer''': tokenizer,
               '''safety_checker''': None,
               '''feature_extractor''': None,
           }
           return components






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase=0   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				Any				      = torch.manual_seed(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = {
               '''prompt''': '''A field of roses''',
               '''generator''': generator,
               # Setting height and width to None to prevent OOMs on CPU.
               '''height''': None,
               '''width''': None,
               '''num_inference_steps''': 2,
               '''guidance_scale''': 6.0,
               '''output_type''': '''numpy''',
           }
           return inputs






       def 	__lowerCAmelCase     (		self   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				Tuple				      = '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE				:				Any				      = self.get_dummy_components()
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = StableDiffusionModelEditingPipeline(**_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[Any]				      = sd_pipe.to(_lowerCamelCase   )
           sd_pipe.set_progress_bar_config(disable=_lowerCamelCase   )

           SCREAMING_SNAKE_CASE				:				Tuple				      = self.get_dummy_inputs(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Tuple				      = sd_pipe(**_lowerCamelCase   ).images
           SCREAMING_SNAKE_CASE				:				Any				      = image[0, -3:, -3:, -1]
           assert image.shape == (1, 64, 64, 3)

           SCREAMING_SNAKE_CASE				:				List[str]				      = np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9]   )

           assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2






       def 	__lowerCAmelCase     (		self   )					->List[Any]:
           SCREAMING_SNAKE_CASE				:				Dict				      = '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE				:				Tuple				      = self.get_dummy_components()
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = StableDiffusionModelEditingPipeline(**_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				str				      = sd_pipe.to(_lowerCamelCase   )
           sd_pipe.set_progress_bar_config(disable=_lowerCamelCase   )

           SCREAMING_SNAKE_CASE				:				Any				      = self.get_dummy_inputs(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[str]				      = '''french fries'''
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = sd_pipe(**_lowerCamelCase				,       negative_prompt=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Any				      = output.images
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = image[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3]   )

           assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2






       def 	__lowerCAmelCase     (		self   )					->Union[str, Any]:
           SCREAMING_SNAKE_CASE				:				List[str]				      = '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = self.get_dummy_components()
           SCREAMING_SNAKE_CASE				:				List[Any]				      = EulerAncestralDiscreteScheduler(
               beta_start=0.0_0_0_8_5				,       beta_end=0.0_1_2				,       beta_schedule='''scaled_linear'''   )
           SCREAMING_SNAKE_CASE				:				List[Any]				      = StableDiffusionModelEditingPipeline(**_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = sd_pipe.to(_lowerCamelCase   )
           sd_pipe.set_progress_bar_config(disable=_lowerCamelCase   )

           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = self.get_dummy_inputs(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = sd_pipe(**_lowerCamelCase   ).images
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = image[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           SCREAMING_SNAKE_CASE				:				str				      = np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7]   )

           assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2






       def 	__lowerCAmelCase     (		self   )					->Any:
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE				:				List[Any]				      = self.get_dummy_components()
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = PNDMScheduler()
           SCREAMING_SNAKE_CASE				:				Tuple				      = StableDiffusionModelEditingPipeline(**_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = sd_pipe.to(_lowerCamelCase   )
           sd_pipe.set_progress_bar_config(disable=_lowerCamelCase   )

           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = self.get_dummy_inputs(_lowerCamelCase   )
           # the pipeline does not expect pndm so test if it raises error.
           with self.assertRaises(_lowerCamelCase   ):
               SCREAMING_SNAKE_CASE				:				Dict				      = sd_pipe(**_lowerCamelCase   ).images






       def 	__lowerCAmelCase     (		self   )					->Dict:
           super().test_inference_batch_single_identical(expected_max_diff=5e-3   )






       def 	__lowerCAmelCase     (		self   )					->List[Any]:
           super().test_attention_slicing_forward_pass(expected_max_diff=5e-3   )






@slow
@require_torch_gpu
class     a_       (			unittest.TestCase							):



       """simple docstring"""






       def 	__lowerCAmelCase     (		self   )					->Dict:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase=0   )					->Dict:
           SCREAMING_SNAKE_CASE				:				str				      = torch.manual_seed(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[Any]				      = {
               '''prompt''': '''A field of roses''',
               '''generator''': generator,
               '''num_inference_steps''': 3,
               '''guidance_scale''': 7.5,
               '''output_type''': '''numpy''',
           }
           return inputs






       def 	__lowerCAmelCase     (		self   )					->List[str]:
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = '''CompVis/stable-diffusion-v1-4'''
           SCREAMING_SNAKE_CASE				:				List[str]				      = StableDiffusionModelEditingPipeline.from_pretrained(_lowerCamelCase				,       safety_checker=_lowerCamelCase   )
           pipe.to(_lowerCamelCase   )
           pipe.set_progress_bar_config(disable=_lowerCamelCase   )
           pipe.enable_attention_slicing()

           SCREAMING_SNAKE_CASE				:				Any				      = self.get_inputs()
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = pipe(**_lowerCamelCase   ).images
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 512, 512, 3)

           SCREAMING_SNAKE_CASE				:				str				      = np.array(
               [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8]   )

           assert np.abs(expected_slice - image_slice   ).max() < 1e-2

           # make sure image changes after editing
           pipe.edit_model('''A pack of roses'''				,       '''A pack of blue roses'''   )

           SCREAMING_SNAKE_CASE				:				Any				      = pipe(**_lowerCamelCase   ).images
           SCREAMING_SNAKE_CASE				:				List[str]				      = image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 512, 512, 3)

           assert np.abs(expected_slice - image_slice   ).max() > 1e-1






       def 	__lowerCAmelCase     (		self   )					->str:
           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()
           torch.cuda.reset_peak_memory_stats()

           SCREAMING_SNAKE_CASE				:				Optional[int]				      = '''CompVis/stable-diffusion-v1-4'''
           SCREAMING_SNAKE_CASE				:				Any				      = DDIMScheduler.from_pretrained(_lowerCamelCase				,       subfolder='''scheduler'''   )
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = StableDiffusionModelEditingPipeline.from_pretrained(
               _lowerCamelCase				,       scheduler=_lowerCamelCase				,       safety_checker=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Dict				      = pipe.to(_lowerCamelCase   )
           pipe.set_progress_bar_config(disable=_lowerCamelCase   )
           pipe.enable_attention_slicing(1   )
           pipe.enable_sequential_cpu_offload()

           SCREAMING_SNAKE_CASE				:				List[Any]				      = self.get_inputs()
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = pipe(**_lowerCamelCase   )

           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = torch.cuda.max_memory_allocated()
           # make sure that less than 4.4 GB is allocated
           assert mem_bytes < 4.4 * 10**9
