


import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




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


	def  A(    self):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()


	def  A(    self):
							__UpperCAmelCase			:							List[str]	    =	StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''')
							__UpperCAmelCase			:							List[Any]	    =	sd_pipe.to(lowercase__)
							sd_pipe.set_progress_bar_config(disable=lowercase__)

							sd_pipe.set_scheduler('''sample_euler''')

							__UpperCAmelCase			:							Dict	    =	'''A painting of a squirrel eating a burger'''
							__UpperCAmelCase			:							List[str]	    =	torch.manual_seed(0)
							__UpperCAmelCase			:							Dict	    =	sd_pipe([prompt]				,     generator=lowercase__				,     guidance_scale=9.0				,     num_inference_steps=2_0				,     output_type='''np''')

							__UpperCAmelCase			:							Optional[Any]	    =	output.images

							__UpperCAmelCase			:							List[Any]	    =	image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							__UpperCAmelCase			:							str	    =	np.array([0.0_4_4_7, 0.0_4_9_2, 0.0_4_6_8, 0.0_4_0_8, 0.0_3_8_3, 0.0_4_0_8, 0.0_3_5_4, 0.0_3_8_0, 0.0_3_3_9])

							assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


	def  A(    self):
							__UpperCAmelCase			:							Optional[int]	    =	StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''')
							__UpperCAmelCase			:							int	    =	sd_pipe.to(lowercase__)
							sd_pipe.set_progress_bar_config(disable=lowercase__)

							sd_pipe.set_scheduler('''sample_euler''')

							__UpperCAmelCase			:							Tuple	    =	'''A painting of a squirrel eating a burger'''
							__UpperCAmelCase			:							int	    =	torch.manual_seed(0)
							__UpperCAmelCase			:							int	    =	sd_pipe([prompt]				,     generator=lowercase__				,     guidance_scale=9.0				,     num_inference_steps=2_0				,     output_type='''np''')

							__UpperCAmelCase			:							Union[str, Any]	    =	output.images

							__UpperCAmelCase			:							Union[str, Any]	    =	image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							__UpperCAmelCase			:							Optional[int]	    =	np.array([0.1_2_3_7, 0.1_3_2_0, 0.1_4_3_8, 0.1_3_5_9, 0.1_3_9_0, 0.1_1_3_2, 0.1_2_7_7, 0.1_1_7_5, 0.1_1_1_2])

							assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-1


	def  A(    self):
							__UpperCAmelCase			:							Optional[int]	    =	StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''')
							__UpperCAmelCase			:							List[str]	    =	sd_pipe.to(lowercase__)
							sd_pipe.set_progress_bar_config(disable=lowercase__)

							sd_pipe.set_scheduler('''sample_dpmpp_2m''')

							__UpperCAmelCase			:							Optional[Any]	    =	'''A painting of a squirrel eating a burger'''
							__UpperCAmelCase			:							Tuple	    =	torch.manual_seed(0)
							__UpperCAmelCase			:							Tuple	    =	sd_pipe(
							    [prompt]				,     generator=lowercase__				,     guidance_scale=7.5				,     num_inference_steps=1_5				,     output_type='''np'''				,     use_karras_sigmas=lowercase__				,     )

							__UpperCAmelCase			:							str	    =	output.images

							__UpperCAmelCase			:							Tuple	    =	image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							__UpperCAmelCase			:							List[Any]	    =	np.array(
							    [0.1_1_3_8_1_6_8_9, 0.1_2_1_1_2_9_2_1, 0.1_3_8_9_4_5_7, 0.1_2_5_4_9_6_0_6, 0.1_2_4_4_9_6_4, 0.1_0_8_3_1_5_1_7, 0.1_1_5_6_2_8_6_6, 0.1_0_8_6_7_8_1_6, 0.1_0_4_9_9_0_4_8])

							assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin




@dataclass
class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		torch.FloatTensor
	_lowerCAmelCase     :		torch.FloatTensor
	_lowerCAmelCase     :		Optional[torch.FloatTensor]										=						None






class 							lowerCamelCase   (  _UpperCamelCase							,						_UpperCamelCase ):
	_lowerCAmelCase     :		Tuple										=						2


	@register_to_config
	def __init__(    self				,     lowercase__ = 0.0_2				,     lowercase__ = 1_0_0				,     lowercase__ = 1.0_0_7				,     lowercase__ = 8_0				,     lowercase__ = 0.0_5				,     lowercase__ = 5_0				,     ):
							# standard deviation of the initial noise distribution
							__UpperCAmelCase			:							Union[str, Any]	    =	sigma_max

							# setable values
							__UpperCAmelCase			:							int	    =	None
							__UpperCAmelCase			:							np.IntTensor	    =	None
							__UpperCAmelCase			:							torch.FloatTensor	    =	None  # sigma(t_i)


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							return sample


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							__UpperCAmelCase			:							int	    =	num_inference_steps
							__UpperCAmelCase			:							List[str]	    =	np.arange(0				,     self.num_inference_steps)[::-1].copy()
							__UpperCAmelCase			:							Any	    =	torch.from_numpy(lowercase__).to(lowercase__)
							__UpperCAmelCase			:							Union[str, Any]	    =	[
							    (
							        self.config.sigma_max**2
							        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
							    )
							    for i in self.timesteps
							]
							__UpperCAmelCase			:							Tuple	    =	torch.tensor(lowercase__				,     dtype=torch.floataa				,     device=lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__ = None):
							if self.config.s_min <= sigma <= self.config.s_max:
													__UpperCAmelCase			:							int	    =	min(self.config.s_churn / self.num_inference_steps				,     2**0.5 - 1)
							else:
													__UpperCAmelCase			:							int	    =	0

							# sample eps ~ N(0, S_noise^2 * I)
							__UpperCAmelCase			:							List[str]	    =	self.config.s_noise * randn_tensor(sample.shape				,     generator=lowercase__).to(sample.device)
							__UpperCAmelCase			:							Optional[int]	    =	sigma + gamma * sigma
							__UpperCAmelCase			:							List[Any]	    =	sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

							return sample_hat, sigma_hat


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = True				,     ):
							__UpperCAmelCase			:							str	    =	sample_hat + sigma_hat * model_output
							__UpperCAmelCase			:							Tuple	    =	(sample_hat - pred_original_sample) / sigma_hat
							__UpperCAmelCase			:							str	    =	sample_hat + (sigma_prev - sigma_hat) * derivative

							if not return_dict:
													return (sample_prev, derivative)

							return KarrasVeOutput(
							    prev_sample=lowercase__				,     derivative=lowercase__				,     pred_original_sample=lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = True				,     ):
							__UpperCAmelCase			:							Any	    =	sample_prev + sigma_prev * model_output
							__UpperCAmelCase			:							List[str]	    =	(sample_prev - pred_original_sample) / sigma_prev
							__UpperCAmelCase			:							Union[str, Any]	    =	sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

							if not return_dict:
													return (sample_prev, derivative)

							return KarrasVeOutput(
							    prev_sample=lowercase__				,     derivative=lowercase__				,     pred_original_sample=lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__):
							raise NotImplementedError()

