





'''simple docstring'''




import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import DetaImageProcessor






class 				__snake_case     (					unittest.TestCase							):





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_=7      ,       UpperCamelCase_=3      ,       UpperCamelCase_=30      ,       UpperCamelCase_=400      ,       UpperCamelCase_=True      ,       UpperCamelCase_=None      ,       UpperCamelCase_=True      ,       UpperCamelCase_=[0.5, 0.5, 0.5]      ,       UpperCamelCase_=[0.5, 0.5, 0.5]      ,       UpperCamelCase_=True      ,       UpperCamelCase_=1 / 255      ,       UpperCamelCase_=True      ,       )      ->		Optional[Any]:
		# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
		snake_case__	       =   size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
		snake_case__	       =   parent
		snake_case__	       =   batch_size
		snake_case__	       =   num_channels
		snake_case__	       =   min_resolution
		snake_case__	       =   max_resolution
		snake_case__	       =   do_resize
		snake_case__	       =   size
		snake_case__	       =   do_normalize
		snake_case__	       =   image_mean
		snake_case__	       =   image_std
		snake_case__	       =   do_rescale
		snake_case__	       =   rescale_factor
		snake_case__	       =   do_pad





	def   _snake_case   (		self		)      ->		Optional[Any]:
		return {
		    "do_resize": self.do_resize,
		    "size": self.size,
		    "do_normalize": self.do_normalize,
		    "image_mean": self.image_mean,
		    "image_std": self.image_std,
		    "do_rescale": self.do_rescale,
		    "rescale_factor": self.rescale_factor,
		    "do_pad": self.do_pad,
		}





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_=False		)      ->		List[str]:
		if not batched:
			snake_case__	       =   image_inputs[0]
			if isinstance(UpperCamelCase_      ,       Image.Image		):
				snake_case__       ,  snake_case__	       =   image.size
			else:
				snake_case__       ,  snake_case__	       =   image.shape[1], image.shape[2]
			if w < h:
				snake_case__	       =   int(self.size['shortest_edge'] * h / w		)
				snake_case__	       =   self.size['shortest_edge']
			elif w > h:
				snake_case__	       =   self.size['shortest_edge']
				snake_case__	       =   int(self.size['shortest_edge'] * w / h		)
			else:
				snake_case__	       =   self.size['shortest_edge']
				snake_case__	       =   self.size['shortest_edge']

		else:
			snake_case__	       =   []
			for image in image_inputs:
				snake_case__       ,  snake_case__	       =   self.get_expected_values([image]		)
				expected_values.append((expected_height, expected_width)		)
			snake_case__	       =   max(UpperCamelCase_      ,       key=lambda UpperCamelCase_		: item[0]		)[0]
			snake_case__	       =   max(UpperCamelCase_      ,       key=lambda UpperCamelCase_		: item[1]		)[1]

		return expected_height, expected_width






@require_torch
@require_vision
class 				__snake_case     (					__magic_name__       ,       unittest.TestCase							):
	__lowerCAmelCase       						=  DetaImageProcessor if is_vision_available() else None





	def   _snake_case   (		self		)      ->		Tuple:
		snake_case__	       =   DetaImageProcessingTester(self		)





	@property
	def   _snake_case   (		self		)      ->		Any:
		return self.image_processor_tester.prepare_image_processor_dict()





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		snake_case__	       =   self.image_processing_class(**self.image_processor_dict		)
		self.assertTrue(hasattr(UpperCamelCase_      ,       'image_mean'		)		)
		self.assertTrue(hasattr(UpperCamelCase_      ,       'image_std'		)		)
		self.assertTrue(hasattr(UpperCamelCase_      ,       'do_normalize'		)		)
		self.assertTrue(hasattr(UpperCamelCase_      ,       'do_resize'		)		)
		self.assertTrue(hasattr(UpperCamelCase_      ,       'do_rescale'		)		)
		self.assertTrue(hasattr(UpperCamelCase_      ,       'do_pad'		)		)
		self.assertTrue(hasattr(UpperCamelCase_      ,       'size'		)		)





	def   _snake_case   (		self		)      ->		Optional[int]:
		snake_case__	       =   self.image_processing_class.from_dict(self.image_processor_dict		)
		self.assertEqual(image_processor.size      ,       {'shortest_edge': 18, 'longest_edge': 1333}		)
		self.assertEqual(image_processor.do_pad      ,       UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		List[Any]:
		pass





	def   _snake_case   (		self		)      ->		List[Any]:
		# Initialize image_processing
		snake_case__	       =   self.image_processing_class(**self.image_processor_dict		)
		# create random PIL images
		snake_case__	       =   prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=UpperCamelCase_		)
		for image in image_inputs:
			self.assertIsInstance(UpperCamelCase_      ,       Image.Image		)

		# Test not batched input
		snake_case__	       =   image_processing(image_inputs[0]      ,       return_tensors='pt'		).pixel_values

		snake_case__       ,  snake_case__	       =   self.image_processor_tester.get_expected_values(UpperCamelCase_		)

		self.assertEqual(
		    encoded_images.shape      ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,       )

		# Test batched
		snake_case__       ,  snake_case__	       =   self.image_processor_tester.get_expected_values(UpperCamelCase_      ,       batched=UpperCamelCase_		)

		snake_case__	       =   image_processing(UpperCamelCase_      ,       return_tensors='pt'		).pixel_values
		self.assertEqual(
		    encoded_images.shape      ,       (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        expected_height,
		        expected_width,
		    )      ,       )





	def   _snake_case   (		self		)      ->		List[Any]:
		# Initialize image_processing
		snake_case__	       =   self.image_processing_class(**self.image_processor_dict		)
		# create random numpy tensors
		snake_case__	       =   prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=UpperCamelCase_      ,       numpify=UpperCamelCase_		)
		for image in image_inputs:
			self.assertIsInstance(UpperCamelCase_      ,       np.ndarray		)

		# Test not batched input
		snake_case__	       =   image_processing(image_inputs[0]      ,       return_tensors='pt'		).pixel_values

		snake_case__       ,  snake_case__	       =   self.image_processor_tester.get_expected_values(UpperCamelCase_		)

		self.assertEqual(
		    encoded_images.shape      ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,       )

		# Test batched
		snake_case__	       =   image_processing(UpperCamelCase_      ,       return_tensors='pt'		).pixel_values

		snake_case__       ,  snake_case__	       =   self.image_processor_tester.get_expected_values(UpperCamelCase_      ,       batched=UpperCamelCase_		)

		self.assertEqual(
		    encoded_images.shape      ,       (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        expected_height,
		        expected_width,
		    )      ,       )





	def   _snake_case   (		self		)      ->		List[str]:
		# Initialize image_processing
		snake_case__	       =   self.image_processing_class(**self.image_processor_dict		)
		# create random PyTorch tensors
		snake_case__	       =   prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=UpperCamelCase_      ,       torchify=UpperCamelCase_		)
		for image in image_inputs:
			self.assertIsInstance(UpperCamelCase_      ,       torch.Tensor		)

		# Test not batched input
		snake_case__	       =   image_processing(image_inputs[0]      ,       return_tensors='pt'		).pixel_values

		snake_case__       ,  snake_case__	       =   self.image_processor_tester.get_expected_values(UpperCamelCase_		)

		self.assertEqual(
		    encoded_images.shape      ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,       )

		# Test batched
		snake_case__	       =   image_processing(UpperCamelCase_      ,       return_tensors='pt'		).pixel_values

		snake_case__       ,  snake_case__	       =   self.image_processor_tester.get_expected_values(UpperCamelCase_      ,       batched=UpperCamelCase_		)

		self.assertEqual(
		    encoded_images.shape      ,       (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        expected_height,
		        expected_width,
		    )      ,       )





	@slow
	def   _snake_case   (		self		)      ->		List[Any]:
		# prepare image and target
		snake_case__	       =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
		with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'      ,       'r'		) as f:
			snake_case__	       =   json.loads(f.read()		)

		snake_case__	       =   {'image_id': 3_9769, 'annotations': target}

		# encode them
		snake_case__	       =   DetaImageProcessor()
		snake_case__	       =   image_processing(images=UpperCamelCase_      ,       annotations=UpperCamelCase_      ,       return_tensors='pt'		)

		# verify pixel values
		snake_case__	       =   torch.Size([1, 3, 800, 1066]		)
		self.assertEqual(encoding['pixel_values'].shape      ,       UpperCamelCase_		)

		snake_case__	       =   torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]		)
		self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,       UpperCamelCase_      ,       atol=1E-4		)		)

		# verify area
		snake_case__	       =   torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]		)
		self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,       UpperCamelCase_		)		)
		# verify boxes
		snake_case__	       =   torch.Size([6, 4]		)
		self.assertEqual(encoding['labels'][0]['boxes'].shape      ,       UpperCamelCase_		)
		snake_case__	       =   torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]		)
		self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,       UpperCamelCase_      ,       atol=1E-3		)		)
		# verify image_id
		snake_case__	       =   torch.tensor([3_9769]		)
		self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,       UpperCamelCase_		)		)
		# verify is_crowd
		snake_case__	       =   torch.tensor([0, 0, 0, 0, 0, 0]		)
		self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,       UpperCamelCase_		)		)
		# verify class_labels
		snake_case__	       =   torch.tensor([75, 75, 63, 65, 17, 17]		)
		self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,       UpperCamelCase_		)		)
		# verify orig_size
		snake_case__	       =   torch.tensor([480, 640]		)
		self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,       UpperCamelCase_		)		)
		# verify size
		snake_case__	       =   torch.tensor([800, 1066]		)
		self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,       UpperCamelCase_		)		)





	@slow
	def   _snake_case   (		self		)      ->		Optional[Any]:
		# prepare image, target and masks_path
		snake_case__	       =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
		with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'      ,       'r'		) as f:
			snake_case__	       =   json.loads(f.read()		)

		snake_case__	       =   {'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

		snake_case__	       =   pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'		)

		# encode them
		snake_case__	       =   DetaImageProcessor(format='coco_panoptic'		)
		snake_case__	       =   image_processing(images=UpperCamelCase_      ,       annotations=UpperCamelCase_      ,       masks_path=UpperCamelCase_      ,       return_tensors='pt'		)

		# verify pixel values
		snake_case__	       =   torch.Size([1, 3, 800, 1066]		)
		self.assertEqual(encoding['pixel_values'].shape      ,       UpperCamelCase_		)

		snake_case__	       =   torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]		)
		self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,       UpperCamelCase_      ,       atol=1E-4		)		)

		# verify area
		snake_case__	       =   torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]		)
		self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,       UpperCamelCase_		)		)
		# verify boxes
		snake_case__	       =   torch.Size([6, 4]		)
		self.assertEqual(encoding['labels'][0]['boxes'].shape      ,       UpperCamelCase_		)
		snake_case__	       =   torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]		)
		self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,       UpperCamelCase_      ,       atol=1E-3		)		)
		# verify image_id
		snake_case__	       =   torch.tensor([3_9769]		)
		self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,       UpperCamelCase_		)		)
		# verify is_crowd
		snake_case__	       =   torch.tensor([0, 0, 0, 0, 0, 0]		)
		self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,       UpperCamelCase_		)		)
		# verify class_labels
		snake_case__	       =   torch.tensor([17, 17, 63, 75, 75, 93]		)
		self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,       UpperCamelCase_		)		)
		# verify masks
		snake_case__	       =   82_2873
		self.assertEqual(encoding['labels'][0]['masks'].sum().item()      ,       UpperCamelCase_		)
		# verify orig_size
		snake_case__	       =   torch.tensor([480, 640]		)
		self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,       UpperCamelCase_		)		)
		# verify size
		snake_case__	       =   torch.tensor([800, 1066]		)
		self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,       UpperCamelCase_		)		)










'''simple docstring'''




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, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






class 				__snake_case     (					unittest.TestCase							):





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





	@property
	def   _snake_case   (		self		)      ->		str:
		snake_case__	       =   1
		snake_case__	       =   3
		snake_case__	       =   (32, 32)

		snake_case__	       =   floats_tensor((batch_size, num_channels) + sizes      ,       rng=random.Random(0		)		).to(UpperCamelCase_		)
		return image





	@property
	def   _snake_case   (		self		)      ->		Union[str, Any]:
		torch.manual_seed(0		)
		snake_case__	       =   UNetaDConditionModel(
		    block_out_channels=(32, 32, 64)      ,       layers_per_block=2      ,       sample_size=32      ,       in_channels=7      ,       out_channels=4      ,       down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D')      ,       up_block_types=('CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'UpBlock2D')      ,       cross_attention_dim=32      ,       attention_head_dim=8      ,       use_linear_projection=UpperCamelCase_      ,       only_cross_attention=(True, True, False)      ,       num_class_embeds=100      ,       )
		return model





	@property
	def   _snake_case   (		self		)      ->		Optional[Any]:
		torch.manual_seed(0		)
		snake_case__	       =   AutoencoderKL(
		    block_out_channels=[32, 32, 64]      ,       in_channels=3      ,       out_channels=3      ,       down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D']      ,       up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']      ,       latent_channels=4      ,       )
		return model





	@property
	def   _snake_case   (		self		)      ->		List[str]:
		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-0_5      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=1000      ,       hidden_act='gelu'      ,       projection_dim=512      ,       )
		return CLIPTextModel(UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		snake_case__	       =   'cpu'  # ensure determinism for the device-dependent torch.Generator
		snake_case__	       =   self.dummy_cond_unet_upscale
		snake_case__	       =   DDPMScheduler()
		snake_case__	       =   DDIMScheduler(prediction_type='v_prediction'		)
		snake_case__	       =   self.dummy_vae
		snake_case__	       =   self.dummy_text_encoder
		snake_case__	       =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

		snake_case__	       =   self.dummy_image.cpu().permute(0      ,       2      ,       3      ,       1		)[0]
		snake_case__	       =   Image.fromarray(np.uinta(UpperCamelCase_		)		).convert('RGB'		).resize((64, 64)		)

		# make sure here that pndm scheduler skips prk
		snake_case__	       =   StableDiffusionUpscalePipeline(
		    unet=UpperCamelCase_      ,       low_res_scheduler=UpperCamelCase_      ,       scheduler=UpperCamelCase_      ,       vae=UpperCamelCase_      ,       text_encoder=UpperCamelCase_      ,       tokenizer=UpperCamelCase_      ,       max_noise_level=350      ,       )
		snake_case__	       =   sd_pipe.to(UpperCamelCase_		)
		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   'A painting of a squirrel eating a burger'
		snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
		snake_case__	       =   sd_pipe(
		    [prompt]      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       guidance_scale=6.0      ,       noise_level=20      ,       num_inference_steps=2      ,       output_type='np'      ,       )

		snake_case__	       =   output.images

		snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
		snake_case__	       =   sd_pipe(
		    [prompt]      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       guidance_scale=6.0      ,       noise_level=20      ,       num_inference_steps=2      ,       output_type='np'      ,       return_dict=UpperCamelCase_      ,       )[0]

		snake_case__	       =   image[0, -3:, -3:, -1]
		snake_case__	       =   image_from_tuple[0, -3:, -3:, -1]

		snake_case__	       =   low_res_image.size[0] * 4
		assert image.shape == (1, expected_height_width, expected_height_width, 3)
		snake_case__	       =   np.array([0.3_1_1_3, 0.3_9_1_0, 0.4_2_7_2, 0.4_8_5_9, 0.5_0_6_1, 0.4_6_5_2, 0.5_3_6_2, 0.5_7_1_5, 0.5_6_6_1]		)

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





	def   _snake_case   (		self		)      ->		List[str]:
		snake_case__	       =   'cpu'  # ensure determinism for the device-dependent torch.Generator
		snake_case__	       =   self.dummy_cond_unet_upscale
		snake_case__	       =   DDPMScheduler()
		snake_case__	       =   DDIMScheduler(prediction_type='v_prediction'		)
		snake_case__	       =   self.dummy_vae
		snake_case__	       =   self.dummy_text_encoder
		snake_case__	       =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

		snake_case__	       =   self.dummy_image.cpu().permute(0      ,       2      ,       3      ,       1		)[0]
		snake_case__	       =   Image.fromarray(np.uinta(UpperCamelCase_		)		).convert('RGB'		).resize((64, 64)		)

		# make sure here that pndm scheduler skips prk
		snake_case__	       =   StableDiffusionUpscalePipeline(
		    unet=UpperCamelCase_      ,       low_res_scheduler=UpperCamelCase_      ,       scheduler=UpperCamelCase_      ,       vae=UpperCamelCase_      ,       text_encoder=UpperCamelCase_      ,       tokenizer=UpperCamelCase_      ,       max_noise_level=350      ,       )
		snake_case__	       =   sd_pipe.to(UpperCamelCase_		)
		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   'A painting of a squirrel eating a burger'
		snake_case__	       =   sd_pipe(
		    2 * [prompt]      ,       image=2 * [low_res_image]      ,       guidance_scale=6.0      ,       noise_level=20      ,       num_inference_steps=2      ,       output_type='np'      ,       )
		snake_case__	       =   output.images
		assert image.shape[0] == 2

		snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
		snake_case__	       =   sd_pipe(
		    [prompt]      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       num_images_per_prompt=2      ,       guidance_scale=6.0      ,       noise_level=20      ,       num_inference_steps=2      ,       output_type='np'      ,       )
		snake_case__	       =   output.images
		assert image.shape[0] == 2





	@unittest.skipIf(torch_device != 'cuda'      ,       'This test requires a GPU'		)
	def   _snake_case   (		self		)      ->		str:
		snake_case__	       =   self.dummy_cond_unet_upscale
		snake_case__	       =   DDPMScheduler()
		snake_case__	       =   DDIMScheduler(prediction_type='v_prediction'		)
		snake_case__	       =   self.dummy_vae
		snake_case__	       =   self.dummy_text_encoder
		snake_case__	       =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

		snake_case__	       =   self.dummy_image.cpu().permute(0      ,       2      ,       3      ,       1		)[0]
		snake_case__	       =   Image.fromarray(np.uinta(UpperCamelCase_		)		).convert('RGB'		).resize((64, 64)		)

		# put models in fp16, except vae as it overflows in fp16
		snake_case__	       =   unet.half()
		snake_case__	       =   text_encoder.half()

		# make sure here that pndm scheduler skips prk
		snake_case__	       =   StableDiffusionUpscalePipeline(
		    unet=UpperCamelCase_      ,       low_res_scheduler=UpperCamelCase_      ,       scheduler=UpperCamelCase_      ,       vae=UpperCamelCase_      ,       text_encoder=UpperCamelCase_      ,       tokenizer=UpperCamelCase_      ,       max_noise_level=350      ,       )
		snake_case__	       =   sd_pipe.to(UpperCamelCase_		)
		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   'A painting of a squirrel eating a burger'
		snake_case__	       =   torch.manual_seed(0		)
		snake_case__	       =   sd_pipe(
		    [prompt]      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       num_inference_steps=2      ,       output_type='np'      ,       ).images

		snake_case__	       =   low_res_image.size[0] * 4
		assert image.shape == (1, expected_height_width, expected_height_width, 3)






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





	def   _snake_case   (		self		)      ->		List[str]:
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()





	def   _snake_case   (		self		)      ->		Optional[int]:
		snake_case__	       =   load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/sd2-upscale/low_res_cat.png'		)
		snake_case__	       =   load_numpy(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
		    '/upsampled_cat.npy'		)

		snake_case__	       =   'stabilityai/stable-diffusion-x4-upscaler'
		snake_case__	       =   StableDiffusionUpscalePipeline.from_pretrained(UpperCamelCase_		)
		pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)
		pipe.enable_attention_slicing()

		snake_case__	       =   'a cat sitting on a park bench'

		snake_case__	       =   torch.manual_seed(0		)
		snake_case__	       =   pipe(
		    prompt=UpperCamelCase_      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       output_type='np'      ,       )
		snake_case__	       =   output.images[0]

		assert image.shape == (512, 512, 3)
		assert np.abs(expected_image - image		).max() < 1E-3





	def   _snake_case   (		self		)      ->		List[Any]:
		snake_case__	       =   load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/sd2-upscale/low_res_cat.png'		)
		snake_case__	       =   load_numpy(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
		    '/upsampled_cat_fp16.npy'		)

		snake_case__	       =   'stabilityai/stable-diffusion-x4-upscaler'
		snake_case__	       =   StableDiffusionUpscalePipeline.from_pretrained(
		    UpperCamelCase_      ,       torch_dtype=torch.floataa      ,       )
		pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)
		pipe.enable_attention_slicing()

		snake_case__	       =   'a cat sitting on a park bench'

		snake_case__	       =   torch.manual_seed(0		)
		snake_case__	       =   pipe(
		    prompt=UpperCamelCase_      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       output_type='np'      ,       )
		snake_case__	       =   output.images[0]

		assert image.shape == (512, 512, 3)
		assert np.abs(expected_image - image		).max() < 5E-1





	def   _snake_case   (		self		)      ->		int:
		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-upscale/low_res_cat.png'		)

		snake_case__	       =   'stabilityai/stable-diffusion-x4-upscaler'
		snake_case__	       =   StableDiffusionUpscalePipeline.from_pretrained(
		    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__	       =   'a cat sitting on a park bench'

		snake_case__	       =   torch.manual_seed(0		)
		snake_case__	       =   pipe(
		    prompt=UpperCamelCase_      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       num_inference_steps=5      ,       output_type='np'      ,       )

		snake_case__	       =   torch.cuda.max_memory_allocated()
		# make sure that less than 2.9 GB is allocated
		assert mem_bytes < 2.9 * 10**9



