




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 lowercase    (  SCREAMING_SNAKE_CASE )			->							Optional[Any]:


			'''simple docstring'''







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



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

	A    							= True





	@property
	def 						A_(							self      )  ->					List[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						1
				SCREAMING_SNAKE_CASE_								=						4
				SCREAMING_SNAKE_CASE_								=						(16, 16)

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





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




				"""simple docstring"""



				torch.manual_seed(0      )
				SCREAMING_SNAKE_CASE_								=						UNetaDConditionModel(
				    act_fn='gelu'      ,     attention_head_dim=8      ,     norm_num_groups=SCREAMING_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=SCREAMING_SNAKE_CASE      ,     only_cross_attention=SCREAMING_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')      ,     )
				SCREAMING_SNAKE_CASE_								=						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      ,     )
				SCREAMING_SNAKE_CASE_								=						EulerDiscreteScheduler(prediction_type='sample'      )
				SCREAMING_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=1000      ,     hidden_act='quick_gelu'      ,     projection_dim=512      ,     )
				SCREAMING_SNAKE_CASE_								=						CLIPTextModel(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

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

				return components





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE=0      )  ->					int:




				"""simple docstring"""



				if str(SCREAMING_SNAKE_CASE      ).startswith('mps'      ):
							SCREAMING_SNAKE_CASE_								=						torch.manual_seed(SCREAMING_SNAKE_CASE      )
				else:
							SCREAMING_SNAKE_CASE_								=						torch.Generator(device=SCREAMING_SNAKE_CASE      ).manual_seed(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						{
				    '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      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						'cpu'

				SCREAMING_SNAKE_CASE_								=						self.get_dummy_components()
				SCREAMING_SNAKE_CASE_								=						self.pipeline_class(**SCREAMING_SNAKE_CASE      )
				pipe.to(SCREAMING_SNAKE_CASE      )
				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

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

				self.assertEqual(image.shape      ,     (1, 256, 256, 3)      )
				SCREAMING_SNAKE_CASE_								=						np.array(
				    [0.4_7_2_2_2_4_1_2, 0.4_1_9_2_1_6_3_3, 0.4_4_7_1_7_4_3_4, 0.4_6_8_7_4_1_9_2, 0.4_2_5_8_8_2_5_8, 0.4_6_1_5_0_7_2_6, 0.4_6_7_7_5_3_4, 0.4_5_5_8_3_8_3_2, 0.4_8_5_7_9_0_5_5]      )
				SCREAMING_SNAKE_CASE_								=						np.abs(image_slice.flatten() - expected_slice      ).max()
				self.assertLessEqual(SCREAMING_SNAKE_CASE      ,     1e-3      )





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




				"""simple docstring"""



				super().test_attention_slicing_forward_pass(expected_max_diff=7e-3      )





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




				"""simple docstring"""



				super().test_cpu_offload_forward_pass(expected_max_diff=3e-3      )





	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3      )





	def 						A_(							self      )  ->					Any:




				"""simple docstring"""



				super().test_inference_batch_single_identical(expected_max_diff=7e-3      )





	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3      )





	def 						A_(							self      )  ->					str:




				"""simple docstring"""



				super().test_save_load_local(expected_max_difference=3e-3      )





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				super().test_save_load_optional_components(expected_max_difference=3e-3      )





	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[
				    'DDIMScheduler',
				    'DDPMScheduler',
				    'PNDMScheduler',
				    'HeunDiscreteScheduler',
				    'EulerAncestralDiscreteScheduler',
				    'KDPM2DiscreteScheduler',
				    'KDPM2AncestralDiscreteScheduler',
				    'DPMSolverSDEScheduler',
				]
				SCREAMING_SNAKE_CASE_								=						self.get_dummy_components()
				SCREAMING_SNAKE_CASE_								=						self.pipeline_class(**SCREAMING_SNAKE_CASE      )

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

				pipe.to(SCREAMING_SNAKE_CASE      )
				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						self.get_dummy_inputs(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						2

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

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

				assert check_same_shape(SCREAMING_SNAKE_CASE      )




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





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						torch.manual_seed(33      )

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

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

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

				SCREAMING_SNAKE_CASE_								=						pipe(SCREAMING_SNAKE_CASE      ,     generator=SCREAMING_SNAKE_CASE      ,     output_type='latent'      ).images

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

				SCREAMING_SNAKE_CASE_								=						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      )  ->					List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						torch.manual_seed(33      )

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

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

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

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

				SCREAMING_SNAKE_CASE_								=						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

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
				import jax



@require_flax
class       a_       (     SCREAMING_SNAKE_CASE__  ,    unittest.TestCase     ):
	A    							= FlaxAutoencoderKL





	@property
	def 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						4
				SCREAMING_SNAKE_CASE_								=						3
				SCREAMING_SNAKE_CASE_								=						(32, 32)

				SCREAMING_SNAKE_CASE_								=						jax.random.PRNGKey(0      )
				SCREAMING_SNAKE_CASE_								=						jax.random.uniform(SCREAMING_SNAKE_CASE      ,     ((batch_size, num_channels) + sizes)      )

				return {"sample": image, "prng_key": prng_key}





	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						{
				    'block_out_channels': [32, 64],
				    'in_channels': 3,
				    'out_channels': 3,
				    'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
				    'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
				    'latent_channels': 4,
				}
				SCREAMING_SNAKE_CASE_								=						self.dummy_input
				return init_dict, inputs_dict
