
'''simple docstring'''







import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


lowercase					:							Optional[int]   =      logging.get_logger(__name__)

enable_full_determinism()


class 		__UpperCAmelCase   (      _lowerCamelCase		,     _lowerCamelCase		,     unittest.TestCase		):
							__lowercase   	= UNetaDModel
							__lowercase   	= """sample"""


							@property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	4
								_snake_case     		=	3
								_snake_case     		=	(32, 32)

								_snake_case     		=	floats_tensor((batch_size, num_channels) + sizes						).to(lowerCAmelCase_						)
								_snake_case     		=	torch.tensor([10]						).to(lowerCAmelCase_						)

								return {"sample": noise, "timestep": time_step}


							@property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return (3, 32, 32)


							@property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return (3, 32, 32)




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	{
								    'block_out_channels': (32, 64),
								    'down_block_types': ('DownBlock2D', 'AttnDownBlock2D'),
								    'up_block_types': ('AttnUpBlock2D', 'UpBlock2D'),
								    'attention_head_dim': 3,
								    'out_channels': 3,
								    'in_channels': 3,
								    'layers_per_block': 2,
								    'sample_size': 32,
								}
								_snake_case     		=	self.dummy_input
								return init_dict, inputs_dict


class 		__UpperCAmelCase   (      _lowerCamelCase		,     _lowerCamelCase		,     unittest.TestCase		):
							__lowercase   	= UNetaDModel
							__lowercase   	= """sample"""


							@property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	4
								_snake_case     		=	4
								_snake_case     		=	(32, 32)

								_snake_case     		=	floats_tensor((batch_size, num_channels) + sizes						).to(lowerCAmelCase_						)
								_snake_case     		=	torch.tensor([10]						).to(lowerCAmelCase_						)

								return {"sample": noise, "timestep": time_step}


							@property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return (4, 32, 32)


							@property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return (4, 32, 32)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	{
								    'sample_size': 32,
								    'in_channels': 4,
								    'out_channels': 4,
								    'layers_per_block': 2,
								    'block_out_channels': (32, 64),
								    'attention_head_dim': 32,
								    'down_block_types': ('DownBlock2D', 'DownBlock2D'),
								    'up_block_types': ('UpBlock2D', 'UpBlock2D'),
								}
								_snake_case     		=	self.dummy_input
								return init_dict, inputs_dict


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case		,				_snake_case     		=	UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'					,     output_loading_info=lowerCAmelCase_						)

								self.assertIsNotNone(lowerCAmelCase_						)
								self.assertEqual(len(loading_info['missing_keys']						)					,     0						)

								model.to(lowerCAmelCase_						)
								_snake_case     		=	model(**self.dummy_input						).sample

								assert image is not None, "Make sure output is not None"


							@unittest.skipIf(torch_device != 'cuda'					,     'This test is supposed to run on GPU'						)
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case		,				_snake_case     		=	UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'					,     output_loading_info=lowerCAmelCase_						)
								model.to(lowerCAmelCase_						)
								_snake_case     		=	model(**self.dummy_input						).sample

								assert image is not None, "Make sure output is not None"


							@unittest.skipIf(torch_device != 'cuda'					,     'This test is supposed to run on GPU'						)
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case		,				_snake_case     		=	UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'					,     output_loading_info=lowerCAmelCase_						)
								model_accelerate.to(lowerCAmelCase_						)
								model_accelerate.eval()

								_snake_case     		=	torch.randn(
								    1					,     model_accelerate.config.in_channels					,     model_accelerate.config.sample_size					,     model_accelerate.config.sample_size					,     generator=torch.manual_seed(0						)					,     )
								_snake_case     		=	noise.to(lowerCAmelCase_						)
								_snake_case     		=	torch.tensor([10] * noise.shape[0]						).to(lowerCAmelCase_						)

								_snake_case     		=	model_accelerate(lowerCAmelCase_					,     lowerCAmelCase_						)['sample']

								# two models don't need to stay in the device at the same time
								del model_accelerate
								torch.cuda.empty_cache()
								gc.collect()

								_snake_case		,				_snake_case     		=	UNetaDModel.from_pretrained(
								    'fusing/unet-ldm-dummy-update'					,     output_loading_info=lowerCAmelCase_					,     low_cpu_mem_usage=lowerCAmelCase_						)
								model_normal_load.to(lowerCAmelCase_						)
								model_normal_load.eval()
								_snake_case     		=	model_normal_load(lowerCAmelCase_					,     lowerCAmelCase_						)['sample']

								assert torch_all_close(lowerCAmelCase_					,     lowerCAmelCase_					,     rtol=1E-3						)




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'						)
								model.eval()
								model.to(lowerCAmelCase_						)

								_snake_case     		=	torch.randn(
								    1					,     model.config.in_channels					,     model.config.sample_size					,     model.config.sample_size					,     generator=torch.manual_seed(0						)					,     )
								_snake_case     		=	noise.to(lowerCAmelCase_						)
								_snake_case     		=	torch.tensor([10] * noise.shape[0]						).to(lowerCAmelCase_						)

								with torch.no_grad():
									_snake_case     		=	model(lowerCAmelCase_					,     lowerCAmelCase_						).sample

								_snake_case     		=	output[0, -1, -3:, -3:].flatten().cpu()
								# fmt: off
								_snake_case     		=	torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800]						)
								# fmt: on

								self.assertTrue(torch_all_close(lowerCAmelCase_					,     lowerCAmelCase_					,     rtol=1E-3						)						)


class 		__UpperCAmelCase   (      _lowerCamelCase		,     _lowerCamelCase		,     unittest.TestCase		):
							__lowercase   	= UNetaDModel
							__lowercase   	= """sample"""


							@property
							def 				lowerCamelCase       ( self					,     lowerCAmelCase_=(32, 32)						):





								"""simple docstring"""






								_snake_case     		=	4
								_snake_case     		=	3

								_snake_case     		=	floats_tensor((batch_size, num_channels) + sizes						).to(lowerCAmelCase_						)
								_snake_case     		=	torch.tensor(batch_size * [10]						).to(dtype=torch.intaa					,     device=lowerCAmelCase_						)

								return {"sample": noise, "timestep": time_step}


							@property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return (3, 32, 32)


							@property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return (3, 32, 32)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	{
								    'block_out_channels': [32, 64, 64, 64],
								    'in_channels': 3,
								    'layers_per_block': 1,
								    'out_channels': 3,
								    'time_embedding_type': 'fourier',
								    'norm_eps': 1E-6,
								    'mid_block_scale_factor': math.sqrt(2.0						),
								    'norm_num_groups': None,
								    'down_block_types': [
								        'SkipDownBlock2D',
								        'AttnSkipDownBlock2D',
								        'SkipDownBlock2D',
								        'SkipDownBlock2D',
								    ],
								    'up_block_types': [
								        'SkipUpBlock2D',
								        'SkipUpBlock2D',
								        'AttnSkipUpBlock2D',
								        'SkipUpBlock2D',
								    ],
								}
								_snake_case     		=	self.dummy_input
								return init_dict, inputs_dict


							@slow
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case		,				_snake_case     		=	UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'					,     output_loading_info=lowerCAmelCase_						)
								self.assertIsNotNone(lowerCAmelCase_						)
								self.assertEqual(len(loading_info['missing_keys']						)					,     0						)

								model.to(lowerCAmelCase_						)
								_snake_case     		=	self.dummy_input
								_snake_case     		=	floats_tensor((4, 3) + (2_56, 2_56)						).to(lowerCAmelCase_						)
								_snake_case     		=	noise
								_snake_case     		=	model(**lowerCAmelCase_						)

								assert image is not None, "Make sure output is not None"


							@slow
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'						)
								model.to(lowerCAmelCase_						)

								_snake_case     		=	4
								_snake_case     		=	3
								_snake_case     		=	(2_56, 2_56)

								_snake_case     		=	torch.ones((batch_size, num_channels) + sizes						).to(lowerCAmelCase_						)
								_snake_case     		=	torch.tensor(batch_size * [1E-4]						).to(lowerCAmelCase_						)

								with torch.no_grad():
									_snake_case     		=	model(lowerCAmelCase_					,     lowerCAmelCase_						).sample

								_snake_case     		=	output[0, -3:, -3:, -1].flatten().cpu()
								# fmt: off
								_snake_case     		=	torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -10980.7129, -20028.8535, 8148.2822, 2342.2905, 567.7608]						)
								# fmt: on

								self.assertTrue(torch_all_close(lowerCAmelCase_					,     lowerCAmelCase_					,     rtol=1E-2						)						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update'						)
								model.to(lowerCAmelCase_						)

								_snake_case     		=	4
								_snake_case     		=	3
								_snake_case     		=	(32, 32)

								_snake_case     		=	torch.ones((batch_size, num_channels) + sizes						).to(lowerCAmelCase_						)
								_snake_case     		=	torch.tensor(batch_size * [1E-4]						).to(lowerCAmelCase_						)

								with torch.no_grad():
									_snake_case     		=	model(lowerCAmelCase_					,     lowerCAmelCase_						).sample

								_snake_case     		=	output[0, -3:, -3:, -1].flatten().cpu()
								# fmt: off
								_snake_case     		=	torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256]						)
								# fmt: on

								self.assertTrue(torch_all_close(lowerCAmelCase_					,     lowerCAmelCase_					,     rtol=1E-2						)						)




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								pass



'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 GLPNImageProcessor


class 		__UpperCAmelCase   (      unittest.TestCase		):


							def __init__( self					,     lowerCAmelCase_					,     lowerCAmelCase_=7					,     lowerCAmelCase_=3					,     lowerCAmelCase_=18					,     lowerCAmelCase_=30					,     lowerCAmelCase_=4_00					,     lowerCAmelCase_=True					,     lowerCAmelCase_=32					,     lowerCAmelCase_=True					,     ):





								"""simple docstring"""






								_snake_case     		=	parent
								_snake_case     		=	batch_size
								_snake_case     		=	num_channels
								_snake_case     		=	image_size
								_snake_case     		=	min_resolution
								_snake_case     		=	max_resolution
								_snake_case     		=	do_resize
								_snake_case     		=	size_divisor
								_snake_case     		=	do_rescale




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return {
								    "do_resize": self.do_resize,
								    "size_divisor": self.size_divisor,
								    "do_rescale": self.do_rescale,
								}


@require_torch
@require_vision
class 		__UpperCAmelCase   (      _lowerCamelCase		,     unittest.TestCase		):
							__lowercase   	= GLPNImageProcessor if is_vision_available() else None


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	GLPNImageProcessingTester(self						)


							@property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return self.image_processor_tester.prepare_image_processor_dict()


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.image_processing_class(**self.image_processor_dict						)
								self.assertTrue(hasattr(lowerCAmelCase_					,     'do_resize'						)						)
								self.assertTrue(hasattr(lowerCAmelCase_					,     'size_divisor'						)						)
								self.assertTrue(hasattr(lowerCAmelCase_					,     'resample'						)						)
								self.assertTrue(hasattr(lowerCAmelCase_					,     'do_rescale'						)						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								pass


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_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=lowerCAmelCase_						)
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_					,     Image.Image						)

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_snake_case     		=	image_processing(image_inputs[0]					,     return_tensors='pt'						).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_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=lowerCAmelCase_					,     numpify=lowerCAmelCase_						)
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_					,     np.ndarray						)

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_snake_case     		=	image_processing(image_inputs[0]					,     return_tensors='pt'						).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_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=lowerCAmelCase_					,     torchify=lowerCAmelCase_						)
								for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_					,     torch.Tensor						)

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_snake_case     		=	image_processing(image_inputs[0]					,     return_tensors='pt'						).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)

