



'''simple docstring'''






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 					snake_case_	(   _lowerCAmelCase    :		Tuple    ) ->  Optional[Any]:
							UpperCAmelCase					:			int									=						[tensor.shape for tensor in tensor_list]
							return all(shape == shapes[0] for shape in shapes[1:]    )

class 							SCREAMING_SNAKE_CASE(				SCREAMING_SNAKE_CASE__				,						SCREAMING_SNAKE_CASE__				,						SCREAMING_SNAKE_CASE__				,						unittest.TestCase     ):







	"""simple docstring"""






	lowerCamelCase__     					=			StableDiffusionLatentUpscalePipeline
	lowerCamelCase__     					=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
	    """height""",
	    """width""",
	    """cross_attention_kwargs""",
	    """negative_prompt_embeds""",
	    """prompt_embeds""",
	}
	lowerCamelCase__     					=			PipelineTesterMixin.required_optional_params - {"""num_images_per_prompt"""}
	lowerCamelCase__     					=			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	lowerCamelCase__     					=			frozenset(
	    []     )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	lowerCamelCase__     					=			frozenset([]     )

	lowerCamelCase__     					=			True







	@property
	def 	A      (		self     :	Optional[Any]						)     ->						List[Any]:
								UpperCAmelCase					:			Union[str, Any]									=						1
								UpperCAmelCase					:			int									=						4
								UpperCAmelCase					:			Optional[Any]									=						(16, 16)

								UpperCAmelCase					:			List[str]									=						floats_tensor((batch_size, num_channels) + sizes						,     rng=random.Random(0						)						).to(__snake_case						)
								return image







	def 	A      (		self     :	Optional[int]						)     ->						List[Any]:
								torch.manual_seed(0						)
								UpperCAmelCase					:			Any									=						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''')						,     )
								UpperCAmelCase					:			List[str]									=						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						,     )
								UpperCAmelCase					:			Any									=						EulerDiscreteScheduler(prediction_type='''sample'''						)
								UpperCAmelCase					:			int									=						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						,     )
								UpperCAmelCase					:			int									=						CLIPTextModel(__snake_case						)
								UpperCAmelCase					:			Union[str, Any]									=						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

								UpperCAmelCase					:			List[str]									=						{
								    '''unet''': model.eval(),
								    '''vae''': vae.eval(),
								    '''scheduler''': scheduler,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								}

								return components







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



								if str(__snake_case						).startswith('''mps'''						):
															UpperCAmelCase					:			List[str]									=						torch.manual_seed(__snake_case						)
								else:
															UpperCAmelCase					:			List[str]									=						torch.Generator(device=__snake_case						).manual_seed(__snake_case						)
								UpperCAmelCase					:			int									=						{
								    '''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 	A      (		self     :	str						)     ->						Dict:
								UpperCAmelCase					:			Union[str, Any]									=						'''cpu'''

								UpperCAmelCase					:			List[Any]									=						self.get_dummy_components()
								UpperCAmelCase					:			Union[str, Any]									=						self.pipeline_class(**__snake_case						)
								pipe.to(__snake_case						)
								pipe.set_progress_bar_config(disable=__snake_case						)

								UpperCAmelCase					:			List[str]									=						self.get_dummy_inputs(__snake_case						)
								UpperCAmelCase					:			Optional[int]									=						pipe(**__snake_case						).images
								UpperCAmelCase					:			int									=						image[0, -3:, -3:, -1]

								self.assertEqual(image.shape						,     (1, 256, 256, 3)						)
								UpperCAmelCase					:			Any									=						np.array(
								    [0.47_22_24_12, 0.41_92_16_33, 0.44_71_74_34, 0.46_87_41_92, 0.42_58_82_58, 0.46_15_07_26, 0.4_67_75_34, 0.45_58_38_32, 0.48_57_90_55]						)
								UpperCAmelCase					:			Any									=						np.abs(image_slice.flatten() - expected_slice						).max()
								self.assertLessEqual(__snake_case						,     1E-3						)







	def 	A      (		self     :	Union[str, Any]						)     ->						List[str]:
								super().test_attention_slicing_forward_pass(expected_max_diff=7E-3						)







	def 	A      (		self     :	Optional[int]						)     ->						List[str]:
								super().test_cpu_offload_forward_pass(expected_max_diff=3E-3						)







	def 	A      (		self     :	List[Any]						)     ->						Tuple:
								super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3						)







	def 	A      (		self     :	Optional[Any]						)     ->						Any:
								super().test_inference_batch_single_identical(expected_max_diff=7E-3						)







	def 	A      (		self     :	List[Any]						)     ->						int:
								super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3						)







	def 	A      (		self     :	Tuple						)     ->						str:
								super().test_save_load_local(expected_max_difference=3E-3						)







	def 	A      (		self     :	List[str]						)     ->						Dict:
								super().test_save_load_optional_components(expected_max_difference=3E-3						)







	def 	A      (		self     :	Any						)     ->						int:
								UpperCAmelCase					:			Any									=						[
								    '''DDIMScheduler''',
								    '''DDPMScheduler''',
								    '''PNDMScheduler''',
								    '''HeunDiscreteScheduler''',
								    '''EulerAncestralDiscreteScheduler''',
								    '''KDPM2DiscreteScheduler''',
								    '''KDPM2AncestralDiscreteScheduler''',
								    '''DPMSolverSDEScheduler''',
								]
								UpperCAmelCase					:			List[Any]									=						self.get_dummy_components()
								UpperCAmelCase					:			Optional[int]									=						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						)
								UpperCAmelCase					:			Optional[int]									=						self.get_dummy_inputs(__snake_case						)
								UpperCAmelCase					:			Tuple									=						2

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

															UpperCAmelCase					:			Any									=						getattr(__snake_case						,     scheduler_enum.name						)
															UpperCAmelCase					:			Union[str, Any]									=						scheduler_cls.from_config(pipe.scheduler.config						)
															UpperCAmelCase					:			Dict									=						pipe(**__snake_case						)[0]
															outputs.append(__snake_case						)

								assert check_same_shape(__snake_case						)

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







	"""simple docstring"""






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







	def 	A      (		self     :	List[Any]						)     ->						Dict:
								UpperCAmelCase					:			Dict									=						torch.manual_seed(33						)

								UpperCAmelCase					:			List[str]									=						StableDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''						,     torch_dtype=torch.floataa						)
								pipe.to('''cuda'''						)

								UpperCAmelCase					:			List[str]									=						StableDiffusionLatentUpscalePipeline.from_pretrained(
								    '''stabilityai/sd-x2-latent-upscaler'''						,     torch_dtype=torch.floataa						)
								upscaler.to('''cuda'''						)

								UpperCAmelCase					:			str									=						'''a photo of an astronaut high resolution, unreal engine, ultra realistic'''

								UpperCAmelCase					:			List[Any]									=						pipe(__snake_case						,     generator=__snake_case						,     output_type='''latent'''						).images

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

								UpperCAmelCase					:			List[Any]									=						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 	A      (		self     :	Any						)     ->						List[str]:
								UpperCAmelCase					:			Any									=						torch.manual_seed(33						)

								UpperCAmelCase					:			Optional[int]									=						StableDiffusionLatentUpscalePipeline.from_pretrained(
								    '''stabilityai/sd-x2-latent-upscaler'''						,     torch_dtype=torch.floataa						)
								upscaler.to('''cuda'''						)

								UpperCAmelCase					:			Union[str, Any]									=						'''the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'''

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

								UpperCAmelCase					:			Union[str, Any]									=						upscaler(
								    prompt=__snake_case						,     image=__snake_case						,     num_inference_steps=20						,     guidance_scale=0						,     generator=__snake_case						,     output_type='''np'''						,     ).images[0]

								UpperCAmelCase					:			List[Any]									=						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











from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
				import accelerate

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Union[str, Any]:


			'''simple docstring'''







			if not is_accelerate_available():
						return method
			SCREAMING_SNAKE_CASE_								=						version.parse(accelerate.__version__ ).base_version
			if version.parse(SCREAMING_SNAKE_CASE ) < version.parse('0.17.0' ):
						return method

			def wrapper(self       ,    *SCREAMING_SNAKE_CASE       ,    **SCREAMING_SNAKE_CASE ):
						if hasattr(self       ,    '_hf_hook' ) and hasattr(self._hf_hook       ,    'pre_forward' ):
									self._hf_hook.pre_forward(self )
						return method(self       ,    *SCREAMING_SNAKE_CASE       ,    **SCREAMING_SNAKE_CASE )

			return wrapper
