import math
from typing import Union

import torch

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





class       lowercase__		(     UpperCamelCase__			,							UpperCamelCase__):
						UpperCamelCase_  =	1


						@register_to_config
						def __init__( self			:       Union[str, Any]					,	UpperCamelCase__			:       Optional[Any]=2000					,	UpperCamelCase__			:       int=0.1					,	UpperCamelCase__			:       Dict=20					,	UpperCamelCase__			:       Dict=1E-3		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							None
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							None
												SCREAMING_SNAKE_CASE							:					List[Any]           =							None


						def  __A ( self			:       Any					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       Any = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							torch.linspace(1					,	self.config.sampling_eps					,	UpperCamelCase__					,	device=UpperCamelCase__		)


						def  __A ( self			:       Optional[int]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Any					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       Union[str, Any]=None		):





												'''simple docstring'''





												if self.timesteps is None:
																		raise ValueError(
																		    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''		)

												# TODO(Patrick) better comments + non-PyTorch
												# postprocess model score
												SCREAMING_SNAKE_CASE							:					List[str]           =							(
												    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
												)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff		)		)
												SCREAMING_SNAKE_CASE							:					int           =							std.flatten()
												while len(std.shape		) < len(score.shape		):
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							std.unsqueeze(-1		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							-score / std

												# compute
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							-1.0 / len(self.timesteps		)

												SCREAMING_SNAKE_CASE							:					Tuple           =							self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
												SCREAMING_SNAKE_CASE							:					str           =							beta_t.flatten()
												while len(beta_t.shape		) < len(x.shape		):
																		SCREAMING_SNAKE_CASE							:					Any           =							beta_t.unsqueeze(-1		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							-0.5 * beta_t * x

												SCREAMING_SNAKE_CASE							:					str           =							torch.sqrt(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Dict           =							drift - diffusion**2 * score
												SCREAMING_SNAKE_CASE							:					List[str]           =							x + drift * dt

												# add noise
												SCREAMING_SNAKE_CASE							:					Any           =							randn_tensor(x.shape					,	layout=x.layout					,	generator=UpperCamelCase__					,	device=x.device					,	dtype=x.dtype		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							x_mean + diffusion * math.sqrt(-dt		) * noise

												return x, x_mean



						def __len__( self			:       Dict		):





												'''simple docstring'''





												return self.config.num_train_timesteps






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__UpperCamelCase	: str    		=  False





class       lowercase__		(     unittest.TestCase):
						pass






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


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





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


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''					,	torch_dtype=torch.floataa		)
												pipe.to(UpperCamelCase__		)
												pipe.set_progress_bar_config(disable=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Dict           =							pipe.dual_guided(
												    prompt='''first prompt'''					,	image=UpperCamelCase__					,	text_to_image_strength=0.75					,	generator=UpperCamelCase__					,	guidance_scale=7.5					,	num_inference_steps=2					,	output_type='''numpy'''					,	).images

												with tempfile.TemporaryDirectory() as tmpdirname:
																		pipe.save_pretrained(UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							VersatileDiffusionPipeline.from_pretrained(UpperCamelCase__					,	torch_dtype=torch.floataa		)
												pipe.to(UpperCamelCase__		)
												pipe.set_progress_bar_config(disable=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							generator.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					int           =							pipe.dual_guided(
												    prompt='''first prompt'''					,	image=UpperCamelCase__					,	text_to_image_strength=0.75					,	generator=UpperCamelCase__					,	guidance_scale=7.5					,	num_inference_steps=2					,	output_type='''numpy'''					,	).images

												assert np.abs(image - new_image		).sum() < 1E-5, "Models don't have the same forward pass"



						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''					,	torch_dtype=torch.floataa		)
												pipe.to(UpperCamelCase__		)
												pipe.set_progress_bar_config(disable=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							'''cyberpunk 2077'''
												SCREAMING_SNAKE_CASE							:					Tuple           =							load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							pipe.dual_guided(
												    prompt=UpperCamelCase__					,	image=UpperCamelCase__					,	text_to_image_strength=0.75					,	generator=UpperCamelCase__					,	guidance_scale=7.5					,	num_inference_steps=50					,	output_type='''numpy'''					,	).images

												SCREAMING_SNAKE_CASE							:					Tuple           =							image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												SCREAMING_SNAKE_CASE							:					int           =							np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]		)

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

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							'''A painting of a squirrel eating a burger '''
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Any           =							pipe.text_to_image(
												    prompt=UpperCamelCase__					,	generator=UpperCamelCase__					,	guidance_scale=7.5					,	num_inference_steps=50					,	output_type='''numpy'''		).images

												SCREAMING_SNAKE_CASE							:					List[str]           =							image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												SCREAMING_SNAKE_CASE							:					Any           =							np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]		)

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

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							pipe.image_variation(UpperCamelCase__					,	generator=UpperCamelCase__					,	output_type='''numpy'''		).images

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]		)

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





