
import math
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
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class       a		(   __UpperCAmelCase					):
							lowercase_		:						torch.FloatTensor
							lowercase_		:						Optional[torch.FloatTensor]   							=  None






def 		_UpperCAmelCase   (	UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     Optional[int]=0.999			,							UpperCamelCase:     List[Any]="cosine"			,							):






	"""simple docstring"""


	if alpha_transform_type == "cosine":

		def alpha_bar_fn(UpperCamelCase:     List[str]  ):
			return math.cos((t + 0.008) / 1.008 * math.pi / 2  ) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(UpperCamelCase:     Tuple  ):
			return math.exp(t * -12.0  )

	else:
		raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"  )

	__lowerCAmelCase         =							[]
	for i in range(UpperCamelCase  ):
		__lowerCAmelCase         =							i / num_diffusion_timesteps
		__lowerCAmelCase         =							(i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar_fn(UpperCamelCase  ) / alpha_bar_fn(UpperCamelCase  )			,							UpperCamelCase  )  )
	return torch.tensor(UpperCamelCase			,							dtype=torch.floataa  )







class       a		(   __UpperCAmelCase      ,	__UpperCAmelCase					):







							@register_to_config
							def __init__(						self  :  str      ,				snake_case__  :  int = 1_000      ,				snake_case__  :  str = "fixed_small_log"      ,				snake_case__  :  bool = True      ,				snake_case__  :  Optional[float] = 1.0      ,				snake_case__  :  str = "epsilon"      ,				snake_case__  :  str = "squaredcos_cap_v2"      ,				):
								"""simple docstring"""







								if beta_schedule != "squaredcos_cap_v2":
									raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'" )

								__lowerCAmelCase         =							betas_for_alpha_bar(snake_case__ )

								__lowerCAmelCase         =							1.0 - self.betas
								__lowerCAmelCase         =							torch.cumprod(self.alphas      ,				dim=0 )
								__lowerCAmelCase         =							torch.tensor(1.0 )

								# standard deviation of the initial noise distribution
								__lowerCAmelCase         =							1.0

								# setable values
								__lowerCAmelCase         =							None
								__lowerCAmelCase         =							torch.from_numpy(np.arange(0      ,				snake_case__ )[::-1].copy() )

								__lowerCAmelCase         =							variance_type







							def       UpperCAmelCase__					(						self  :  Optional[Any]      ,				snake_case__  :  torch.FloatTensor      ,				snake_case__  :  Optional[int] = None ):
								"""simple docstring"""







								return sample







							def       UpperCAmelCase__					(						self  :  Dict      ,				snake_case__  :  int      ,				snake_case__  :  Union[str, torch.device] = None ):
								"""simple docstring"""







								__lowerCAmelCase         =							num_inference_steps
								__lowerCAmelCase         =							(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
								__lowerCAmelCase         =							(np.arange(0      ,				snake_case__ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
								__lowerCAmelCase         =							torch.from_numpy(snake_case__ ).to(snake_case__ )







							def       UpperCAmelCase__					(						self  :  Dict      ,				snake_case__  :  Union[str, Any]      ,				snake_case__  :  int=None      ,				snake_case__  :  Tuple=None      ,				snake_case__  :  Optional[Any]=None ):
								"""simple docstring"""







								if prev_timestep is None:
									__lowerCAmelCase         =							t - 1

								__lowerCAmelCase         =							self.alphas_cumprod[t]
								__lowerCAmelCase         =							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
								__lowerCAmelCase         =							1 - alpha_prod_t
								__lowerCAmelCase         =							1 - alpha_prod_t_prev

								if prev_timestep == t - 1:
									__lowerCAmelCase         =							self.betas[t]
								else:
									__lowerCAmelCase         =							1 - alpha_prod_t / alpha_prod_t_prev

								# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
								# and sample from it to get previous sample
								# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
								__lowerCAmelCase         =							beta_prod_t_prev / beta_prod_t * beta

								if variance_type is None:
									__lowerCAmelCase         =							self.config.variance_type

								# hacks - were probably added for training stability
								if variance_type == "fixed_small_log":
									__lowerCAmelCase         =							torch.log(torch.clamp(snake_case__      ,				min=1E-20 ) )
									__lowerCAmelCase         =							torch.exp(0.5 * variance )
								elif variance_type == "learned_range":
									# NOTE difference with DDPM scheduler
									__lowerCAmelCase         =							variance.log()
									__lowerCAmelCase         =							beta.log()

									__lowerCAmelCase         =							(predicted_variance + 1) / 2
									__lowerCAmelCase         =							frac * max_log + (1 - frac) * min_log

								return variance







							def       UpperCAmelCase__					(						self  :  str      ,				snake_case__  :  torch.FloatTensor      ,				snake_case__  :  int      ,				snake_case__  :  torch.FloatTensor      ,				snake_case__  :  Optional[int] = None      ,				snake_case__  :  List[Any]=None      ,				snake_case__  :  bool = True      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							timestep

								if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
									__lowerCAmelCase   ,						__lowerCAmelCase         =							torch.split(snake_case__      ,				sample.shape[1]      ,				dim=1 )
								else:
									__lowerCAmelCase         =							None

								# 1. compute alphas, betas
								if prev_timestep is None:
									__lowerCAmelCase         =							t - 1

								__lowerCAmelCase         =							self.alphas_cumprod[t]
								__lowerCAmelCase         =							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
								__lowerCAmelCase         =							1 - alpha_prod_t
								__lowerCAmelCase         =							1 - alpha_prod_t_prev

								if prev_timestep == t - 1:
									__lowerCAmelCase         =							self.betas[t]
									__lowerCAmelCase         =							self.alphas[t]
								else:
									__lowerCAmelCase         =							1 - alpha_prod_t / alpha_prod_t_prev
									__lowerCAmelCase         =							1 - beta

								# 2. compute predicted original sample from predicted noise also called
								# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
								if self.config.prediction_type == "epsilon":
									__lowerCAmelCase         =							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
								elif self.config.prediction_type == "sample":
									__lowerCAmelCase         =							model_output
								else:
									raise ValueError(
									    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"
									    " for the UnCLIPScheduler." )

								# 3. Clip "predicted x_0"
								if self.config.clip_sample:
									__lowerCAmelCase         =							torch.clamp(
									    snake_case__      ,				-self.config.clip_sample_range      ,				self.config.clip_sample_range )

								# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
								# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
								__lowerCAmelCase         =							(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
								__lowerCAmelCase         =							alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

								# 5. Compute predicted previous sample µ_t
								# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
								__lowerCAmelCase         =							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

								# 6. Add noise
								__lowerCAmelCase         =							0
								if t > 0:
									__lowerCAmelCase         =							randn_tensor(
									    model_output.shape      ,				dtype=model_output.dtype      ,				generator=snake_case__      ,				device=model_output.device )

									__lowerCAmelCase         =							self._get_variance(
									    snake_case__      ,				predicted_variance=snake_case__      ,				prev_timestep=snake_case__      ,				)

									if self.variance_type == "fixed_small_log":
										__lowerCAmelCase         =							variance
									elif self.variance_type == "learned_range":
										__lowerCAmelCase         =							(0.5 * variance).exp()
									else:
										raise ValueError(
										    F"variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"
										    " for the UnCLIPScheduler." )

									__lowerCAmelCase         =							variance * variance_noise

								__lowerCAmelCase         =							pred_prev_sample + variance

								if not return_dict:
									return (pred_prev_sample,)

								return UnCLIPSchedulerOutput(prev_sample=snake_case__      ,				pred_original_sample=snake_case__ )







							def       UpperCAmelCase__					(						self  :  List[Any]      ,				snake_case__  :  torch.FloatTensor      ,				snake_case__  :  torch.FloatTensor      ,				snake_case__  :  torch.IntTensor      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							self.alphas_cumprod.to(device=original_samples.device      ,				dtype=original_samples.dtype )
								__lowerCAmelCase         =							timesteps.to(original_samples.device )

								__lowerCAmelCase         =							alphas_cumprod[timesteps] ** 0.5
								__lowerCAmelCase         =							sqrt_alpha_prod.flatten()
								while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
									__lowerCAmelCase         =							sqrt_alpha_prod.unsqueeze(-1 )

								__lowerCAmelCase         =							(1 - alphas_cumprod[timesteps]) ** 0.5
								__lowerCAmelCase         =							sqrt_one_minus_alpha_prod.flatten()
								while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
									__lowerCAmelCase         =							sqrt_one_minus_alpha_prod.unsqueeze(-1 )

								__lowerCAmelCase         =							sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
								return noisy_samples








import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


UpperCamelCase_   	=	False

class       a		(   unittest.TestCase					):
							pass

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







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase         =							VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion" )
								pipe.to(snake_case__ )
								pipe.set_progress_bar_config(disable=snake_case__ )

								__lowerCAmelCase         =							load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" )
								__lowerCAmelCase         =							torch.manual_seed(0 )
								__lowerCAmelCase         =							pipe(
								    image=snake_case__      ,				generator=snake_case__      ,				guidance_scale=7.5      ,				num_inference_steps=50      ,				output_type="numpy"      ,				).images

								__lowerCAmelCase         =							image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								__lowerCAmelCase         =							np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5] )

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






