





"""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      lowercase__ (					lowerCAmelCase__   :			List[Any]			)    ->			Optional[Any]:



			'''simple docstring'''
			a__  :	Dict		 =			[tensor.shape for tensor in tensor_list]
			return all(shape == shapes[0] for shape in shapes[1:]			)



class 					__UpperCAmelCase							(						_UpperCamelCase						,  _UpperCamelCase						,  _UpperCamelCase						,  unittest.TestCase  ):
				__lowerCamelCase				:	Union[str, Any]				     =				StableDiffusionLatentUpscalePipeline
				__lowerCamelCase				:	List[Any]				     =				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
				    'height',
				    'width',
				    'cross_attention_kwargs',
				    'negative_prompt_embeds',
				    'prompt_embeds',
				}
				__lowerCamelCase				:	List[Any]				     =				PipelineTesterMixin.required_optional_params - {'num_images_per_prompt'}
				__lowerCamelCase				:	List[Any]				     =				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				__lowerCamelCase				:	Union[str, Any]				     =				frozenset(
				    []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				__lowerCamelCase				:	List[Any]				     =				frozenset([]  )

				__lowerCamelCase				:	Dict				     =				True
				@property
				def       UpperCAmelCase      (		self    :			List[Any]    )	->	int:



							'''simple docstring'''



							a__  :	Optional[int]		 =			1
							a__  :	Any		 =			4
							a__  :	int		 =			(16, 16)

							a__  :	List[Any]		 =			floats_tensor((batch_size, num_channels) + sizes	, rng=random.Random(0    )    ).to(a_    )
							return image
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	List[str]:



							'''simple docstring'''



							torch.manual_seed(0    )
							a__  :	int		 =			UNetaDConditionModel(
							    act_fn="gelu"	, attention_head_dim=8	, norm_num_groups=a_	, block_out_channels=[32, 32, 64, 64]	, time_cond_proj_dim=1_60	, 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=a_	, only_cross_attention=a_	, 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__  :	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	, )
							a__  :	Optional[int]		 =			EulerDiscreteScheduler(prediction_type="sample"    )
							a__  :	List[str]		 =			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=10_00	, hidden_act="quick_gelu"	, projection_dim=5_12	, )
							a__  :	Optional[int]		 =			CLIPTextModel(a_    )
							a__  :	List[str]		 =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )

							a__  :	int		 =			{
							    "unet": model.eval(),
							    "vae": vae.eval(),
							    "scheduler": scheduler,
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							}

							return components
				def       UpperCAmelCase      (		self    :			List[str]	, a_    :			int	, a_    :			List[str]=0    )	->	int:



							'''simple docstring'''



							if str(a_    ).startswith("mps"    ):
										a__  :	Optional[int]		 =			torch.manual_seed(a_    )
							else:
										a__  :	Optional[int]		 =			torch.Generator(device=a_    ).manual_seed(a_    )
							a__  :	Optional[Any]		 =			{
							    "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       UpperCAmelCase      (		self    :			List[str]    )	->	Dict:



							'''simple docstring'''



							a__  :	Optional[int]		 =			"cpu"

							a__  :	Any		 =			self.get_dummy_components()
							a__  :	Tuple		 =			self.pipeline_class(**a_    )
							pipe.to(a_    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	List[Any]		 =			self.get_dummy_inputs(a_    )
							a__  :	List[str]		 =			pipe(**a_    ).images
							a__  :	int		 =			image[0, -3:, -3:, -1]

							self.assertEqual(image.shape	, (1, 2_56, 2_56, 3)    )
							a__  :	int		 =			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__  :	int		 =			np.abs(image_slice.flatten() - expected_slice    ).max()
							self.assertLessEqual(a_	, 1E-3    )
				def       UpperCAmelCase      (		self    :			int    )	->	Union[str, Any]:



							'''simple docstring'''



							super().test_attention_slicing_forward_pass(expected_max_diff=7E-3    )
				def       UpperCAmelCase      (		self    :			List[Any]    )	->	Tuple:



							'''simple docstring'''



							super().test_cpu_offload_forward_pass(expected_max_diff=3E-3    )
				def       UpperCAmelCase      (		self    :			List[str]    )	->	Any:



							'''simple docstring'''



							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3    )
				def       UpperCAmelCase      (		self    :			Dict    )	->	Optional[int]:



							'''simple docstring'''



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



							'''simple docstring'''



							super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3    )
				def       UpperCAmelCase      (		self    :			Tuple    )	->	Optional[Any]:



							'''simple docstring'''



							super().test_save_load_local(expected_max_difference=3E-3    )
				def       UpperCAmelCase      (		self    :			Any    )	->	Dict:



							'''simple docstring'''



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






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



							'''simple docstring'''



							a__  :	Union[str, Any]		 =			[
							    "DDIMScheduler",
							    "DDPMScheduler",
							    "PNDMScheduler",
							    "HeunDiscreteScheduler",
							    "EulerAncestralDiscreteScheduler",
							    "KDPM2DiscreteScheduler",
							    "KDPM2AncestralDiscreteScheduler",
							    "DPMSolverSDEScheduler",
							]
							a__  :	Optional[int]		 =			self.get_dummy_components()
							a__  :	int		 =			self.pipeline_class(**a_    )

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

							pipe.to(a_    )
							pipe.set_progress_bar_config(disable=a_    )
							a__  :	Dict		 =			self.get_dummy_inputs(a_    )
							a__  :	int		 =			2

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

										a__  :	Optional[int]		 =			getattr(a_	, scheduler_enum.name    )
										a__  :	Union[str, Any]		 =			scheduler_cls.from_config(pipe.scheduler.config    )
										a__  :	Tuple		 =			pipe(**a_    )[0]
										outputs.append(a_    )

							assert check_same_shape(a_    )



@require_torch_gpu
@slow
class 					__UpperCAmelCase							(						unittest.TestCase  ):
				def       UpperCAmelCase      (		self    :			List[str]    )	->	Tuple:



							'''simple docstring'''



							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()
				def       UpperCAmelCase      (		self    :			Tuple    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	str		 =			torch.manual_seed(33    )

							a__  :	Union[str, Any]		 =			StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"	, torch_dtype=torch.floataa    )
							pipe.to("cuda"    )

							a__  :	List[Any]		 =			StableDiffusionLatentUpscalePipeline.from_pretrained(
							    "stabilityai/sd-x2-latent-upscaler"	, torch_dtype=torch.floataa    )
							upscaler.to("cuda"    )

							a__  :	Dict		 =			"a photo of an astronaut high resolution, unreal engine, ultra realistic"

							a__  :	Optional[Any]		 =			pipe(a_	, generator=a_	, output_type="latent"    ).images

							a__  :	Tuple		 =			upscaler(
							    prompt=a_	, image=a_	, num_inference_steps=20	, guidance_scale=0	, generator=a_	, output_type="np"	, ).images[0]

							a__  :	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       UpperCAmelCase      (		self    :			Tuple    )	->	List[str]:



							'''simple docstring'''



							a__  :	List[Any]		 =			torch.manual_seed(33    )

							a__  :	Optional[Any]		 =			StableDiffusionLatentUpscalePipeline.from_pretrained(
							    "stabilityai/sd-x2-latent-upscaler"	, torch_dtype=torch.floataa    )
							upscaler.to("cuda"    )

							a__  :	Any		 =			"the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"

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

							a__  :	int		 =			upscaler(
							    prompt=a_	, image=a_	, num_inference_steps=20	, guidance_scale=0	, generator=a_	, output_type="np"	, ).images[0]

							a__  :	Union[str, 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







'''simple docstring'''
from __future__ import annotations




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[int]							     =  2
   lowercase_						:     Tuple							     =  []
   while i * i <= n:
      if n % i:
         i += 1
      else:
         n //= i
         factors.append(_UpperCamelCase				)
   if n > 1:
      factors.append(_UpperCamelCase				)
   return factors


if __name__ == "__main__":
 import doctest

 doctest.testmod()




