


import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
       MODEL_FOR_QUESTION_ANSWERING_MAPPING,
       MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
       MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
       LayoutLMvaConfig,
       LayoutLMvaForQuestionAnswering,
       LayoutLMvaForSequenceClassification,
       LayoutLMvaForTokenClassification,
       LayoutLMvaModel,
   )
   from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
   from PIL import Image

   from transformers import LayoutLMvaImageProcessor







class lowerCAmelCase_						:

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=2		,				_UpperCAmelCase=3		,				_UpperCAmelCase=4		,				_UpperCAmelCase=2		,				_UpperCAmelCase=7		,				_UpperCAmelCase=True		,				_UpperCAmelCase=True		,				_UpperCAmelCase=True		,				_UpperCAmelCase=True		,				_UpperCAmelCase=99		,				_UpperCAmelCase=36		,				_UpperCAmelCase=3		,				_UpperCAmelCase=4		,				_UpperCAmelCase=37		,				_UpperCAmelCase="gelu"		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=5_12		,				_UpperCAmelCase=16		,				_UpperCAmelCase=2		,				_UpperCAmelCase=0.02		,				_UpperCAmelCase=6		,				_UpperCAmelCase=6		,				_UpperCAmelCase=3		,				_UpperCAmelCase=4		,				_UpperCAmelCase=None		,				_UpperCAmelCase=10_00		,				):
       snake_case_	=				parent
       snake_case_	=				batch_size
       snake_case_	=				num_channels
       snake_case_	=				image_size
       snake_case_	=				patch_size
       snake_case_	=				text_seq_length
       snake_case_	=				is_training
       snake_case_	=				use_input_mask
       snake_case_	=				use_token_type_ids
       snake_case_	=				use_labels
       snake_case_	=				vocab_size
       snake_case_	=				hidden_size
       snake_case_	=				num_hidden_layers
       snake_case_	=				num_attention_heads
       snake_case_	=				intermediate_size
       snake_case_	=				hidden_act
       snake_case_	=				hidden_dropout_prob
       snake_case_	=				attention_probs_dropout_prob
       snake_case_	=				max_position_embeddings
       snake_case_	=				type_vocab_size
       snake_case_	=				type_sequence_label_size
       snake_case_	=				initializer_range
       snake_case_	=				coordinate_size
       snake_case_	=				shape_size
       snake_case_	=				num_labels
       snake_case_	=				num_choices
       snake_case_	=				scope
       snake_case_	=				range_bbox

       # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
       snake_case_	=				text_seq_length
       snake_case_	=				(image_size // patch_size) ** 2 + 1
       snake_case_	=				self.text_seq_length + self.image_seq_length



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				ids_tensor([self.batch_size, self.text_seq_length]		,				self.vocab_size )

       snake_case_	=				ids_tensor([self.batch_size, self.text_seq_length, 4]		,				self.range_bbox )
       # Ensure that bbox is legal
       for i in range(bbox.shape[0] ):
             for j in range(bbox.shape[1] ):
                   if bbox[i, j, 3] < bbox[i, j, 1]:
                         snake_case_	=				bbox[i, j, 3]
                         snake_case_	=				bbox[i, j, 1]
                         snake_case_	=				t
                   if bbox[i, j, 2] < bbox[i, j, 0]:
                         snake_case_	=				bbox[i, j, 2]
                         snake_case_	=				bbox[i, j, 0]
                         snake_case_	=				t

       snake_case_	=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

       snake_case_	=				None
       if self.use_input_mask:
             snake_case_	=				random_attention_mask([self.batch_size, self.text_seq_length] )

       snake_case_	=				None
       if self.use_token_type_ids:
             snake_case_	=				ids_tensor([self.batch_size, self.text_seq_length]		,				self.type_vocab_size )

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

       snake_case_	=				LayoutLMvaConfig(
           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		,				coordinate_size=self.coordinate_size		,				shape_size=self.shape_size		,				input_size=self.image_size		,				patch_size=self.patch_size		,				)

       return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				LayoutLMvaModel(config=_UpperCAmelCase )
       model.to(_UpperCAmelCase )
       model.eval()

       # text + image
       snake_case_	=				model(_UpperCAmelCase		,				pixel_values=_UpperCAmelCase )
       snake_case_	=				model(
           _UpperCAmelCase		,				bbox=_UpperCAmelCase		,				pixel_values=_UpperCAmelCase		,				attention_mask=_UpperCAmelCase		,				token_type_ids=_UpperCAmelCase )
       snake_case_	=				model(_UpperCAmelCase		,				bbox=_UpperCAmelCase		,				pixel_values=_UpperCAmelCase		,				token_type_ids=_UpperCAmelCase )
       snake_case_	=				model(_UpperCAmelCase		,				bbox=_UpperCAmelCase		,				pixel_values=_UpperCAmelCase )

       self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size) )

       # text only
       snake_case_	=				model(_UpperCAmelCase )
       self.parent.assertEqual(
           result.last_hidden_state.shape		,				(self.batch_size, self.text_seq_length, self.hidden_size) )

       # image only
       snake_case_	=				model(pixel_values=_UpperCAmelCase )
       self.parent.assertEqual(
           result.last_hidden_state.shape		,				(self.batch_size, self.image_seq_length, self.hidden_size) )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				self.num_labels
       snake_case_	=				LayoutLMvaForSequenceClassification(_UpperCAmelCase )
       model.to(_UpperCAmelCase )
       model.eval()
       snake_case_	=				model(
           _UpperCAmelCase		,				bbox=_UpperCAmelCase		,				pixel_values=_UpperCAmelCase		,				attention_mask=_UpperCAmelCase		,				token_type_ids=_UpperCAmelCase		,				labels=_UpperCAmelCase		,				)
       self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_labels) )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				self.num_labels
       snake_case_	=				LayoutLMvaForTokenClassification(config=_UpperCAmelCase )
       model.to(_UpperCAmelCase )
       model.eval()
       snake_case_	=				model(
           _UpperCAmelCase		,				bbox=_UpperCAmelCase		,				pixel_values=_UpperCAmelCase		,				attention_mask=_UpperCAmelCase		,				token_type_ids=_UpperCAmelCase		,				labels=_UpperCAmelCase		,				)
       self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.text_seq_length, self.num_labels) )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				LayoutLMvaForQuestionAnswering(config=_UpperCAmelCase )
       model.to(_UpperCAmelCase )
       model.eval()
       snake_case_	=				model(
           _UpperCAmelCase		,				bbox=_UpperCAmelCase		,				pixel_values=_UpperCAmelCase		,				attention_mask=_UpperCAmelCase		,				token_type_ids=_UpperCAmelCase		,				start_positions=_UpperCAmelCase		,				end_positions=_UpperCAmelCase		,				)
       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 ):
       snake_case_	=				self.prepare_config_and_inputs()
       (
           (
           snake_case_   
       )	,   (
           snake_case_   
       )	,   (
           snake_case_   
       )	,   (
           snake_case_   
       )	,   (
           snake_case_   
       )	,   (
           snake_case_   
       )	,   (
           snake_case_   
       )	,   (
           snake_case_   
       )	,      
       )	=				config_and_inputs
       snake_case_	=				{
           '''input_ids''': input_ids,
           '''bbox''': bbox,
           '''pixel_values''': pixel_values,
           '''token_type_ids''': token_type_ids,
           '''attention_mask''': input_mask,
       }
       return config, inputs_dict







@require_torch
class lowerCAmelCase_						(			lowerCamelCase__ ,						lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  False
 __snake_case												=  False
 __snake_case												=  False

 __snake_case												=  (
     (
         LayoutLMvaModel,
         LayoutLMvaForSequenceClassification,
         LayoutLMvaForTokenClassification,
         LayoutLMvaForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 __snake_case												=  (
     {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
     if is_torch_available()
     else {}
 )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       # `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
       # embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
       # the sequence dimension of the text embedding only.
       # (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
       return True



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				LayoutLMvaModelTester(self )
       snake_case_	=				ConfigTester(self		,				config_class=_UpperCAmelCase		,				hidden_size=37 )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase=False ):
       snake_case_	=				copy.deepcopy(_UpperCAmelCase )
       if model_class in get_values(_UpperCAmelCase ):
             snake_case_	=				{
                 k: v.unsqueeze(1 ).expand(-1		,				self.model_tester.num_choices		,				-1 ).contiguous()
                 if isinstance(_UpperCAmelCase		,				torch.Tensor ) and v.ndim > 1
                 else v
                 for k, v in inputs_dict.items()
             }
       if return_labels:
             if model_class in get_values(_UpperCAmelCase ):
                   snake_case_	=				torch.ones(self.model_tester.batch_size		,				dtype=torch.long		,				device=_UpperCAmelCase )
             elif model_class in get_values(_UpperCAmelCase ):
                   snake_case_	=				torch.zeros(
                       self.model_tester.batch_size		,				dtype=torch.long		,				device=_UpperCAmelCase )
                   snake_case_	=				torch.zeros(
                       self.model_tester.batch_size		,				dtype=torch.long		,				device=_UpperCAmelCase )
             elif model_class in [
                 *get_values(_UpperCAmelCase ),
             ]:
                   snake_case_	=				torch.zeros(
                       self.model_tester.batch_size		,				dtype=torch.long		,				device=_UpperCAmelCase )
             elif model_class in [
                 *get_values(_UpperCAmelCase ),
             ]:
                   snake_case_	=				torch.zeros(
                       (self.model_tester.batch_size, self.model_tester.text_seq_length)		,				dtype=torch.long		,				device=_UpperCAmelCase		,				)

       return inputs_dict



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



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
             snake_case_	=				type
             self.model_tester.create_and_check_model(*_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase )





 @slow
 def 						UpperCamelCase__					(     self ):
       for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             snake_case_	=				LayoutLMvaModel.from_pretrained(_UpperCAmelCase )
             self.assertIsNotNone(_UpperCAmelCase )






def 	__lowerCAmelCase   ()->  Dict:




      """simple docstring"""

      snake_case_	=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
      return image







@require_torch
class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 @cached_property
 def 						UpperCamelCase__					(     self ):
       return LayoutLMvaImageProcessor(apply_ocr=_UpperCAmelCase ) if is_vision_available() else None





 @slow
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base''' ).to(_UpperCAmelCase )

       snake_case_	=				self.default_image_processor
       snake_case_	=				prepare_img()
       snake_case_	=				image_processor(images=_UpperCAmelCase		,				return_tensors='''pt''' ).pixel_values.to(_UpperCAmelCase )

       snake_case_	=				torch.tensor([[1, 2]] )
       snake_case_	=				torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]] ).unsqueeze(0 )

       # forward pass
       snake_case_	=				model(
           input_ids=input_ids.to(_UpperCAmelCase )		,				bbox=bbox.to(_UpperCAmelCase )		,				pixel_values=pixel_values.to(_UpperCAmelCase )		,				)

       # verify the logits
       snake_case_	=				torch.Size((1, 1_99, 7_68) )
       self.assertEqual(outputs.last_hidden_state.shape		,				_UpperCAmelCase )

       snake_case_	=				torch.tensor(
           [[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]] ).to(_UpperCAmelCase )

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



import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class lowerCAmelCase_						(			lowerCamelCase__ ,						lowerCamelCase__ ,						lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  StableDiffusionInpaintPipeline
 __snake_case												=  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 __snake_case												=  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __snake_case												=  frozenset(
     []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 __snake_case												=  frozenset([]	)



 def 						UpperCamelCase__					(     self ):
       torch.manual_seed(0 )
       snake_case_	=				UNetaDConditionModel(
           block_out_channels=(32, 64)		,				layers_per_block=2		,				sample_size=32		,				in_channels=9		,				out_channels=4		,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,				up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,				cross_attention_dim=32		,				attention_head_dim=(2, 4)		,				use_linear_projection=_UpperCAmelCase		,				)
       snake_case_	=				PNDMScheduler(skip_prk_steps=_UpperCAmelCase )
       torch.manual_seed(0 )
       snake_case_	=				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		,				sample_size=1_28		,				)
       torch.manual_seed(0 )
       snake_case_	=				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=10_00		,				hidden_act='''gelu'''		,				projection_dim=5_12		,				)
       snake_case_	=				CLIPTextModel(_UpperCAmelCase )
       snake_case_	=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

       snake_case_	=				{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=0 ):
       # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
       snake_case_	=				floats_tensor((1, 3, 32, 32)		,				rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
       snake_case_	=				image.cpu().permute(0		,				2		,				3		,				1 )[0]
       snake_case_	=				Image.fromarray(np.uinta(_UpperCAmelCase ) ).convert('''RGB''' ).resize((64, 64) )
       snake_case_	=				Image.fromarray(np.uinta(image + 4 ) ).convert('''RGB''' ).resize((64, 64) )
       if str(_UpperCAmelCase ).startswith('''mps''' ):
             snake_case_	=				torch.manual_seed(_UpperCAmelCase )
       else:
             snake_case_	=				torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
       snake_case_	=				{
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': init_image,
           '''mask_image''': mask_image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 6.0,
           '''output_type''': '''numpy''',
       }
       return inputs



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       snake_case_	=				self.get_dummy_components()
       snake_case_	=				StableDiffusionInpaintPipeline(**_UpperCAmelCase )
       snake_case_	=				sd_pipe.to(_UpperCAmelCase )
       sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase )

       snake_case_	=				self.get_dummy_inputs(_UpperCAmelCase )
       snake_case_	=				sd_pipe(**_UpperCAmelCase ).images
       snake_case_	=				image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       snake_case_	=				np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476] )

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





 def 						UpperCamelCase__					(     self ):
       super().test_inference_batch_single_identical(expected_max_diff=3E-3 )







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

 '''simple docstring'''

 def 						UpperCamelCase__					(     self ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-inpaint/init_image.png''' )
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
       snake_case_	=				load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
           '''/yellow_cat_sitting_on_a_park_bench.npy''' )

       snake_case_	=				'''stabilityai/stable-diffusion-2-inpainting'''
       snake_case_	=				StableDiffusionInpaintPipeline.from_pretrained(_UpperCAmelCase		,				safety_checker=_UpperCAmelCase )
       pipe.to(_UpperCAmelCase )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase )
       pipe.enable_attention_slicing()

       snake_case_	=				'''Face of a yellow cat, high resolution, sitting on a park bench'''

       snake_case_	=				torch.manual_seed(0 )
       snake_case_	=				pipe(
           prompt=_UpperCAmelCase		,				image=_UpperCAmelCase		,				mask_image=_UpperCAmelCase		,				generator=_UpperCAmelCase		,				output_type='''np'''		,				)
       snake_case_	=				output.images[0]

       assert image.shape == (5_12, 5_12, 3)
       assert np.abs(expected_image - image ).max() < 9E-3



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-inpaint/init_image.png''' )
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
       snake_case_	=				load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
           '''/yellow_cat_sitting_on_a_park_bench_fp16.npy''' )

       snake_case_	=				'''stabilityai/stable-diffusion-2-inpainting'''
       snake_case_	=				StableDiffusionInpaintPipeline.from_pretrained(
           _UpperCAmelCase		,				torch_dtype=torch.floataa		,				safety_checker=_UpperCAmelCase		,				)
       pipe.to(_UpperCAmelCase )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase )
       pipe.enable_attention_slicing()

       snake_case_	=				'''Face of a yellow cat, high resolution, sitting on a park bench'''

       snake_case_	=				torch.manual_seed(0 )
       snake_case_	=				pipe(
           prompt=_UpperCAmelCase		,				image=_UpperCAmelCase		,				mask_image=_UpperCAmelCase		,				generator=_UpperCAmelCase		,				output_type='''np'''		,				)
       snake_case_	=				output.images[0]

       assert image.shape == (5_12, 5_12, 3)
       assert np.abs(expected_image - image ).max() < 5E-1





 def 						UpperCamelCase__					(     self ):
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-inpaint/init_image.png''' )
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )

       snake_case_	=				'''stabilityai/stable-diffusion-2-inpainting'''
       snake_case_	=				PNDMScheduler.from_pretrained(_UpperCAmelCase		,				subfolder='''scheduler''' )
       snake_case_	=				StableDiffusionInpaintPipeline.from_pretrained(
           _UpperCAmelCase		,				safety_checker=_UpperCAmelCase		,				scheduler=_UpperCAmelCase		,				torch_dtype=torch.floataa		,				)
       pipe.to(_UpperCAmelCase )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase )
       pipe.enable_attention_slicing(1 )
       pipe.enable_sequential_cpu_offload()

       snake_case_	=				'''Face of a yellow cat, high resolution, sitting on a park bench'''

       snake_case_	=				torch.manual_seed(0 )
       snake_case_	=				pipe(
           prompt=_UpperCAmelCase		,				image=_UpperCAmelCase		,				mask_image=_UpperCAmelCase		,				generator=_UpperCAmelCase		,				num_inference_steps=2		,				output_type='''np'''		,				)

       snake_case_	=				torch.cuda.max_memory_allocated()
       # make sure that less than 2.65 GB is allocated
       assert mem_bytes < 2.65 * 10**9