

"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class 			__UpperCamelCase (   _A     ,		_A     ,		_A     ,		unittest.TestCase						):
		SCREAMING_SNAKE_CASE								=    StableDiffusionImageVariationPipeline
		SCREAMING_SNAKE_CASE								=    IMAGE_VARIATION_PARAMS
		SCREAMING_SNAKE_CASE								=    IMAGE_VARIATION_BATCH_PARAMS
		SCREAMING_SNAKE_CASE								=    frozenset([]						)
		# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
		SCREAMING_SNAKE_CASE								=    frozenset([]						)


		def 					SCREAMING_SNAKE_CASE__   (self :		Any):
							torch.manual_seed(0)
							A			=					UNetaDConditionModel(
							    block_out_channels=(3_2, 6_4)     ,     layers_per_block=2     ,     sample_size=3_2     ,     in_channels=4     ,     out_channels=4     ,     down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")     ,     up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")     ,     cross_attention_dim=3_2     ,     )
							A			=					PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE)
							torch.manual_seed(0)
							A			=					AutoencoderKL(
							    block_out_channels=[3_2, 6_4]     ,     in_channels=3     ,     out_channels=3     ,     down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]     ,     up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]     ,     latent_channels=4     ,     )
							torch.manual_seed(0)
							A			=					CLIPVisionConfig(
							    hidden_size=3_2     ,     projection_dim=3_2     ,     intermediate_size=3_7     ,     layer_norm_eps=1E-05     ,     num_attention_heads=4     ,     num_hidden_layers=5     ,     image_size=3_2     ,     patch_size=4     ,     )
							A			=					CLIPVisionModelWithProjection(__SCREAMING_SNAKE_CASE)
							A			=					CLIPImageProcessor(crop_size=3_2     ,     size=3_2)

							A			=					{
							    "unet": unet,
							    "scheduler": scheduler,
							    "vae": vae,
							    "image_encoder": image_encoder,
							    "feature_extractor": feature_extractor,
							    "safety_checker": None,
							}
							return components


		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]     ,     __SCREAMING_SNAKE_CASE :		Tuple     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]=0):
							A			=					floats_tensor((1, 3, 3_2, 3_2)     ,     rng=random.Random(__SCREAMING_SNAKE_CASE))
							A			=					image.cpu().permute(0     ,     2     ,     3     ,     1)[0]
							A			=					Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE)).convert("RGB").resize((3_2, 3_2))
							if str(__SCREAMING_SNAKE_CASE).startswith("mps"):
												A			=					torch.manual_seed(__SCREAMING_SNAKE_CASE)
							else:
												A			=					torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(__SCREAMING_SNAKE_CASE)
							A			=					{
							    "image": image,
							    "generator": generator,
							    "num_inference_steps": 2,
							    "guidance_scale": 6.0,
							    "output_type": "numpy",
							}
							return inputs


		def 					SCREAMING_SNAKE_CASE__   (self :		List[Any]):
							A			=					"cpu"  # ensure determinism for the device-dependent torch.Generator
							A			=					self.get_dummy_components()
							A			=					StableDiffusionImageVariationPipeline(**__SCREAMING_SNAKE_CASE)
							A			=					sd_pipe.to(__SCREAMING_SNAKE_CASE)
							sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

							A			=					self.get_dummy_inputs(__SCREAMING_SNAKE_CASE)
							A			=					sd_pipe(**__SCREAMING_SNAKE_CASE).images
							A			=					image[0, -3:, -3:, -1]

							assert image.shape == (1, 6_4, 6_4, 3)
							A			=					np.array([0.5_2_3_9, 0.5_7_2_3, 0.4_7_9_6, 0.5_0_4_9, 0.5_5_5_0, 0.4_6_8_5, 0.5_3_2_9, 0.4_8_9_1, 0.4_9_2_1])

							assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3


		def 					SCREAMING_SNAKE_CASE__   (self :		Tuple):
							A			=					"cpu"  # ensure determinism for the device-dependent torch.Generator
							A			=					self.get_dummy_components()
							A			=					StableDiffusionImageVariationPipeline(**__SCREAMING_SNAKE_CASE)
							A			=					sd_pipe.to(__SCREAMING_SNAKE_CASE)
							sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

							A			=					self.get_dummy_inputs(__SCREAMING_SNAKE_CASE)
							A			=					2 * [inputs["image"]]
							A			=					sd_pipe(**__SCREAMING_SNAKE_CASE)

							A			=					output.images

							A			=					image[-1, -3:, -3:, -1]

							assert image.shape == (2, 6_4, 6_4, 3)
							A			=					np.array([0.6_8_9_2, 0.5_6_3_7, 0.5_8_3_6, 0.5_7_7_1, 0.6_2_5_4, 0.6_4_0_9, 0.5_5_8_0, 0.5_5_6_9, 0.5_2_8_9])

							assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3


		def 					SCREAMING_SNAKE_CASE__   (self :		Any):
							super().test_inference_batch_single_identical(expected_max_diff=3E-3)





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


		def 					SCREAMING_SNAKE_CASE__   (self :		Dict):
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()


		def 					SCREAMING_SNAKE_CASE__   (self :		Dict     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]     ,     __SCREAMING_SNAKE_CASE :		int="cpu"     ,     __SCREAMING_SNAKE_CASE :		List[Any]=torch.floataa     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=0):
							A			=					torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(__SCREAMING_SNAKE_CASE)
							A			=					load_image(
							    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
							    "/stable_diffusion_imgvar/input_image_vermeer.png")
							A			=					np.random.RandomState(__SCREAMING_SNAKE_CASE).standard_normal((1, 4, 6_4, 6_4))
							A			=					torch.from_numpy(__SCREAMING_SNAKE_CASE).to(device=__SCREAMING_SNAKE_CASE     ,     dtype=__SCREAMING_SNAKE_CASE)
							A			=					{
							    "image": init_image,
							    "latents": latents,
							    "generator": generator,
							    "num_inference_steps": 3,
							    "guidance_scale": 7.5,
							    "output_type": "numpy",
							}
							return inputs


		def 					SCREAMING_SNAKE_CASE__   (self :		Dict):
							A			=					StableDiffusionImageVariationPipeline.from_pretrained(
							    "lambdalabs/sd-image-variations-diffusers"     ,     safety_checker=__SCREAMING_SNAKE_CASE)
							A			=					sd_pipe.to(__SCREAMING_SNAKE_CASE)
							sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

							A			=					self.get_inputs(__SCREAMING_SNAKE_CASE)
							A			=					sd_pipe(**__SCREAMING_SNAKE_CASE).images
							A			=					image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							A			=					np.array([0.8_4_4_9_1, 0.9_0_7_8_9, 0.7_5_7_0_8, 0.7_8_7_3_4, 0.8_3_4_8_5, 0.7_0_0_9_9, 0.6_6_9_3_8, 0.6_8_7_2_7, 0.6_1_3_7_9])
							assert np.abs(image_slice - expected_slice).max() < 6E-3


		def 					SCREAMING_SNAKE_CASE__   (self :		int):
							A			=					0

							def callback_fn(__SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		torch.FloatTensor) -> None:
												A			=					True
												nonlocal number_of_steps
												number_of_steps += 1
												if step == 1:
																	A			=					latents.detach().cpu().numpy()
																	assert latents.shape == (1, 4, 6_4, 6_4)
																	A			=					latents[0, -3:, -3:, -1]
																	A			=					np.array(
																	    [-0.1_6_2_1, 0.2_8_3_7, -0.7_9_7_9, -0.1_2_2_1, -1.3_0_5_7, 0.7_6_8_1, -2.1_1_9_1, 0.0_4_6_4, 1.6_3_0_9])

																	assert np.abs(latents_slice.flatten() - expected_slice).max() < 5E-2
												elif step == 2:
																	A			=					latents.detach().cpu().numpy()
																	assert latents.shape == (1, 4, 6_4, 6_4)
																	A			=					latents[0, -3:, -3:, -1]
																	A			=					np.array([0.6_2_9_9, 1.7_5_0_0, 1.1_9_9_2, -2.1_5_8_2, -1.8_9_9_4, 0.7_3_3_4, -0.7_0_9_0, 1.0_1_3_7, 1.5_2_7_3])

																	assert np.abs(latents_slice.flatten() - expected_slice).max() < 5E-2

							A			=					False

							A			=					StableDiffusionImageVariationPipeline.from_pretrained(
							    "fusing/sd-image-variations-diffusers"     ,     safety_checker=__SCREAMING_SNAKE_CASE     ,     torch_dtype=torch.floataa     ,     )
							pipe.to(__SCREAMING_SNAKE_CASE)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)
							pipe.enable_attention_slicing()

							A			=					self.get_inputs(__SCREAMING_SNAKE_CASE     ,     dtype=torch.floataa)
							pipe(**__SCREAMING_SNAKE_CASE     ,     callback=__SCREAMING_SNAKE_CASE     ,     callback_steps=1)
							assert callback_fn.has_been_called
							assert number_of_steps == inputs["num_inference_steps"]


		def 					SCREAMING_SNAKE_CASE__   (self :		List[str]):
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							A			=					"fusing/sd-image-variations-diffusers"
							A			=					StableDiffusionImageVariationPipeline.from_pretrained(
							    __SCREAMING_SNAKE_CASE     ,     safety_checker=__SCREAMING_SNAKE_CASE     ,     torch_dtype=torch.floataa)
							A			=					pipe.to(__SCREAMING_SNAKE_CASE)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)
							pipe.enable_attention_slicing(1)
							pipe.enable_sequential_cpu_offload()

							A			=					self.get_inputs(__SCREAMING_SNAKE_CASE     ,     dtype=torch.floataa)
							A			=					pipe(**__SCREAMING_SNAKE_CASE)

							A			=					torch.cuda.max_memory_allocated()
							# make sure that less than 2.6 GB is allocated
							assert mem_bytes < 2.6 * 1_0**9





@nightly
@require_torch_gpu
class 			__UpperCamelCase (   unittest.TestCase						):


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[int]):
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[Any]     ,     __SCREAMING_SNAKE_CASE :		Tuple     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]="cpu"     ,     __SCREAMING_SNAKE_CASE :		int=torch.floataa     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]=0):
							A			=					torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(__SCREAMING_SNAKE_CASE)
							A			=					load_image(
							    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
							    "/stable_diffusion_imgvar/input_image_vermeer.png")
							A			=					np.random.RandomState(__SCREAMING_SNAKE_CASE).standard_normal((1, 4, 6_4, 6_4))
							A			=					torch.from_numpy(__SCREAMING_SNAKE_CASE).to(device=__SCREAMING_SNAKE_CASE     ,     dtype=__SCREAMING_SNAKE_CASE)
							A			=					{
							    "image": init_image,
							    "latents": latents,
							    "generator": generator,
							    "num_inference_steps": 5_0,
							    "guidance_scale": 7.5,
							    "output_type": "numpy",
							}
							return inputs


		def 					SCREAMING_SNAKE_CASE__   (self :		List[Any]):
							A			=					StableDiffusionImageVariationPipeline.from_pretrained("fusing/sd-image-variations-diffusers")
							sd_pipe.to(__SCREAMING_SNAKE_CASE)
							sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

							A			=					self.get_inputs(__SCREAMING_SNAKE_CASE)
							A			=					sd_pipe(**__SCREAMING_SNAKE_CASE).images[0]

							A			=					load_numpy(
							    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
							    "/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy")
							A			=					np.abs(expected_image - image).max()
							assert max_diff < 1E-3


		def 					SCREAMING_SNAKE_CASE__   (self :		Dict):
							A			=					StableDiffusionImageVariationPipeline.from_pretrained("fusing/sd-image-variations-diffusers")
							A			=					DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)
							sd_pipe.to(__SCREAMING_SNAKE_CASE)
							sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

							A			=					self.get_inputs(__SCREAMING_SNAKE_CASE)
							A			=					2_5
							A			=					sd_pipe(**__SCREAMING_SNAKE_CASE).images[0]

							A			=					load_numpy(
							    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
							    "/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy")
							A			=					np.abs(expected_image - image).max()
							assert max_diff < 1E-3













from dataclasses import dataclass
from typing import Callable, Optional

import datasets




@dataclass
class 							a_ (						datasets.BuilderConfig		):


							'''simple docstring'''






							__a:	Optional[Callable]     =    None
							__a:	Optional[dict]     =    None
							__a:	Optional[datasets.Features]     =    None

							def      _lowercase       (		self   )					->					Optional[int]:
														'''simple docstring'''

														assert self.generator is not None, "generator must be specified"

														if self.gen_kwargs is None:
																					lowerCAmelCase_										=     {}




class 							a_ (						datasets.GeneratorBasedBuilder		):


							'''simple docstring'''






							__a:	Optional[Any]     =    GeneratorConfig

							def      _lowercase       (		self   )					->					Union[str, Any]:
														'''simple docstring'''

														return datasets.DatasetInfo(features=self.config.features   )

							def      _lowercase       (		self			,			lowercase_   )					->					Dict:
														'''simple docstring'''

														return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,			gen_kwargs=self.config.gen_kwargs   )]






							def      _lowercase       (		self			,			**lowercase_   )					->					Union[str, Any]:
														'''simple docstring'''

														for idx, ex in enumerate(self.config.generator(**lowercase_   )   ):
																					yield idx, ex



