

import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
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

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






def        UpperCamelCase_(							_snake_case   :  str	):




			"""simple docstring"""






			__a        =[tensor.shape for tensor in tensor_list]
			return all(shape == shapes[0] for shape in shapes[1:]	)






class 	__magic_name__    (			snake_case__  ,    snake_case__  ,    snake_case__  ,    unittest.TestCase      ):
			SCREAMING_SNAKE_CASE							   =						StableDiffusionLatentUpscalePipeline
			SCREAMING_SNAKE_CASE							   =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
			    'height',
			    'width',
			    'cross_attention_kwargs',
			    'negative_prompt_embeds',
			    'prompt_embeds',
			}
			SCREAMING_SNAKE_CASE							   =						PipelineTesterMixin.required_optional_params - {'num_images_per_prompt'}
			SCREAMING_SNAKE_CASE							   =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			SCREAMING_SNAKE_CASE							   =						frozenset(
			    []      )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			SCREAMING_SNAKE_CASE							   =						frozenset([]      )

			SCREAMING_SNAKE_CASE							   =						True





			@property
			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						__a        =1
						__a        =4
						__a        =(16, 16)

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





			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						torch.manual_seed(0					)
						__a        =UNetaDConditionModel(
						    act_fn='gelu'					,  attention_head_dim=8					,  norm_num_groups=__snake_case					,  block_out_channels=[32, 32, 64, 64]					,  time_cond_proj_dim=160					,  conv_in_kernel=1					,  conv_out_kernel=1					,  cross_attention_dim=32					,  down_block_types=(
						        'KDownBlock2D',
						        'KCrossAttnDownBlock2D',
						        'KCrossAttnDownBlock2D',
						        'KCrossAttnDownBlock2D',
						    )					,  in_channels=8					,  mid_block_type=__snake_case					,  only_cross_attention=__snake_case					,  out_channels=5					,  resnet_time_scale_shift='scale_shift'					,  time_embedding_type='fourier'					,  timestep_post_act='gelu'					,  up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')					,  )
						__a        =AutoencoderKL(
						    block_out_channels=[32, 32, 64, 64]					,  in_channels=3					,  out_channels=3					,  down_block_types=[
						        'DownEncoderBlock2D',
						        'DownEncoderBlock2D',
						        'DownEncoderBlock2D',
						        'DownEncoderBlock2D',
						    ]					,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']					,  latent_channels=4					,  )
						__a        =EulerDiscreteScheduler(prediction_type='sample'					)
						__a        =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					,  hidden_act='quick_gelu'					,  projection_dim=512					,  )
						__a        =CLIPTextModel(__snake_case					)
						__a        =CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

						__a        ={
						    'unet': model.eval(),
						    'vae': vae.eval(),
						    'scheduler': scheduler,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						}

						return components





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case=0					)   -> Union[str, Any]:




						'''simple docstring'''







						if str(__snake_case					).startswith('mps'					):
									__a        =torch.manual_seed(__snake_case					)
						else:
									__a        =torch.Generator(device=__snake_case					).manual_seed(__snake_case					)
						__a        ={
						    'prompt': 'A painting of a squirrel eating a burger',
						    'image': self.dummy_image.cpu(),
						    'generator': generator,
						    'num_inference_steps': 2,
						    'output_type': 'numpy',
						}
						return inputs





			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						__a        ='cpu'

						__a        =self.get_dummy_components()
						__a        =self.pipeline_class(**__snake_case					)
						pipe.to(__snake_case					)
						pipe.set_progress_bar_config(disable=__snake_case					)

						__a        =self.get_dummy_inputs(__snake_case					)
						__a        =pipe(**__snake_case					).images
						__a        =image[0, -3:, -3:, -1]

						self.assertEqual(image.shape					,  (1, 256, 256, 3)					)
						__a        =np.array(
						    [0.4722_2412, 0.4192_1633, 0.4471_7434, 0.4687_4192, 0.4258_8258, 0.4615_0726, 0.467_7534, 0.4558_3832, 0.4857_9055]					)
						__a        =np.abs(image_slice.flatten() - expected_slice					).max()
						self.assertLessEqual(__snake_case					,  1e-3					)





			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						super().test_attention_slicing_forward_pass(expected_max_diff=7e-3					)





			def 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						super().test_cpu_offload_forward_pass(expected_max_diff=3e-3					)





			def 		__magic_name__    (		self					)   -> Union[str, Any]:




						'''simple docstring'''
						super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3					)





			def 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						super().test_inference_batch_single_identical(expected_max_diff=7e-3					)





			def 		__magic_name__    (		self					)   -> Union[str, Any]:




						'''simple docstring'''
						super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3					)





			def 		__magic_name__    (		self					)   -> int:




						'''simple docstring'''
						super().test_save_load_local(expected_max_difference=3e-3					)





			def 		__magic_name__    (		self					)   -> Union[str, Any]:




						'''simple docstring'''
						super().test_save_load_optional_components(expected_max_difference=3e-3					)





			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						__a        =[
						    'DDIMScheduler',
						    'DDPMScheduler',
						    'PNDMScheduler',
						    'HeunDiscreteScheduler',
						    'EulerAncestralDiscreteScheduler',
						    'KDPM2DiscreteScheduler',
						    'KDPM2AncestralDiscreteScheduler',
						    'DPMSolverSDEScheduler',
						]
						__a        =self.get_dummy_components()
						__a        =self.pipeline_class(**__snake_case					)

						# make sure that PNDM does not need warm-up
						pipe.scheduler.register_to_config(skip_prk_steps=__snake_case					)

						pipe.to(__snake_case					)
						pipe.set_progress_bar_config(disable=__snake_case					)
						__a        =self.get_dummy_inputs(__snake_case					)
						__a        =2

						__a        =[]
						for scheduler_enum in KarrasDiffusionSchedulers:
									if scheduler_enum.name in skip_schedulers:
												# no sigma schedulers are not supported
												# no schedulers
												continue

									__a        =getattr(__snake_case					,  scheduler_enum.name					)
									__a        =scheduler_cls.from_config(pipe.scheduler.config					)
									__a        =pipe(**__snake_case					)[0]
									outputs.append(__snake_case					)

						assert check_same_shape(__snake_case					)






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





			def 		__magic_name__    (		self					)   -> List[str]:




						'''simple docstring'''
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()





			def 		__magic_name__    (		self					)   -> int:




						'''simple docstring'''
						__a        =torch.manual_seed(33					)

						__a        =StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'					,  torch_dtype=torch.floataa					)
						pipe.to('cuda'					)

						__a        =StableDiffusionLatentUpscalePipeline.from_pretrained(
						    'stabilityai/sd-x2-latent-upscaler'					,  torch_dtype=torch.floataa					)
						upscaler.to('cuda'					)

						__a        ='a photo of an astronaut high resolution, unreal engine, ultra realistic'

						__a        =pipe(__snake_case					,  generator=__snake_case					,  output_type='latent'					).images

						__a        =upscaler(
						    prompt=__snake_case					,  image=__snake_case					,  num_inference_steps=20					,  guidance_scale=0					,  generator=__snake_case					,  output_type='np'					,  ).images[0]

						__a        =load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'					)
						assert np.abs((expected_image - image).mean()					) < 5e-2





			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						__a        =torch.manual_seed(33					)

						__a        =StableDiffusionLatentUpscalePipeline.from_pretrained(
						    'stabilityai/sd-x2-latent-upscaler'					,  torch_dtype=torch.floataa					)
						upscaler.to('cuda'					)

						__a        ='the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'

						__a        =load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'					)

						__a        =upscaler(
						    prompt=__snake_case					,  image=__snake_case					,  num_inference_steps=20					,  guidance_scale=0					,  generator=__snake_case					,  output_type='np'					,  ).images[0]

						__a        =load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'					)
						assert np.abs((expected_image - image).max()					) < 5e-2














import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory


def __A    (      )->  Tuple:





						"""simple docstring"""



						raise RuntimeError('CUDA out of memory.'	)





class __lowerCamelCase (      nn.Module):





	"""simple docstring"""






	def __init__(							self    ):
							"""simple docstring"""







							super().__init__()
							_UpperCAmelCase									=			nn.Linear(3       ,    4    )
							_UpperCAmelCase									=			nn.BatchNormad(4    )
							_UpperCAmelCase									=			nn.Linear(4       ,    5    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							return self.lineara(self.batchnorm(self.lineara(UpperCAmelCase    )    )    )





class __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			[]

							@find_executable_batch_size(starting_batch_size=128    )
							def mock_training_loop_function(UpperCAmelCase    ):
													nonlocal batch_sizes
													batch_sizes.append(UpperCAmelCase    )
													if batch_size != 8:
																			raise_fake_out_of_memory()

							mock_training_loop_function()
							self.assertListEqual(UpperCAmelCase       ,    [128, 64, 32, 16, 8]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			[]

							@find_executable_batch_size(starting_batch_size=128    )
							def mock_training_loop_function(UpperCAmelCase       ,    UpperCAmelCase    ):
													nonlocal batch_sizes
													batch_sizes.append(UpperCAmelCase    )
													if batch_size != 8:
																			raise_fake_out_of_memory()
													return batch_size, arga

							_UpperCAmelCase					, _UpperCAmelCase									=			mock_training_loop_function('hello'    )
							self.assertListEqual(UpperCAmelCase       ,    [128, 64, 32, 16, 8]    )
							self.assertListEqual([bs, arga]       ,    [8, 'hello']    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=0    )
							def mock_training_loop_function(UpperCAmelCase    ):
													pass

							with self.assertRaises(UpperCAmelCase    ) as cm:
													mock_training_loop_function()
													self.assertIn('No executable batch size found, reached zero.'       ,    cm.exception.args[0]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=16    )
							def mock_training_loop_function(UpperCAmelCase    ):
													if batch_size > 0:
																			raise_fake_out_of_memory()
													pass

							with self.assertRaises(UpperCAmelCase    ) as cm:
													mock_training_loop_function()
													self.assertIn('No executable batch size found, reached zero.'       ,    cm.exception.args[0]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=128    )
							def mock_training_loop_function(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
													if batch_size != 8:
																			raise raise_fake_out_of_memory()

							with self.assertRaises(UpperCAmelCase    ) as cm:
													mock_training_loop_function(128       ,    'hello'       ,    'world'    )
													self.assertIn('Batch size was passed into `f`'       ,    cm.exception.args[0]    )
													self.assertIn('`f(arg1=\'hello\', arg2=\'world\')'       ,    cm.exception.args[0]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=16    )
							def mock_training_loop_function(UpperCAmelCase    ):
													raise ValueError('Oops, we had an error!'    )

							with self.assertRaises(UpperCAmelCase    ) as cm:
													mock_training_loop_function()
													self.assertIn('Oops, we had an error!'       ,    cm.exception.args[0]    )

	@require_cuda
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			torch.cuda.memory_allocated()
							_UpperCAmelCase									=			ModelForTest()
							model.cuda()
							self.assertGreater(torch.cuda.memory_allocated()       ,    UpperCAmelCase    )
							_UpperCAmelCase									=			release_memory(UpperCAmelCase    )
							self.assertEqual(torch.cuda.memory_allocated()       ,    UpperCAmelCase    )
