






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
class        lowercase__       (     _UpperCAmelCase		):
	a_             =42
	a_             =42





def     _a					(       UpperCamelCase_   :			str      ,				UpperCamelCase_   :			Union[str, Any]=0.999      ,				UpperCamelCase_   :			Dict="cosine"      ,				)	->				Dict:
						"""simple docstring"""







						if alpha_transform_type == "cosine":

												def alpha_bar_fn(UpperCamelCase_   :			Optional[Any]				):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(UpperCamelCase_   :			List[str]				):
																		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        lowercase__       (     _UpperCAmelCase,   _UpperCAmelCase		):
	a_             =1

	@register_to_config
	def __init__(							self						,      __UpperCAmelCase = 1000						,      __UpperCAmelCase = 0.0_001						,      __UpperCAmelCase = 0.02						,      __UpperCAmelCase = "linear"						,      __UpperCAmelCase = 0.0						,      __UpperCAmelCase = None						,      __UpperCAmelCase = True						,      )->   Any:







							'''simple docstring'''





							if trained_betas is not None:
													lowerCAmelCase__	 =  torch.from_numpy(__UpperCAmelCase    )
							elif beta_schedule == "linear":
													lowerCAmelCase__	 =  torch.linspace(__UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      dtype=torch.floataa    )
							elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													lowerCAmelCase__	 =  (
													    torch.linspace(beta_start**0.5						,      beta_end**0.5						,      __UpperCAmelCase						,      dtype=torch.floataa    ) ** 2
													)
							elif beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													lowerCAmelCase__	 =  betas_for_alpha_bar(__UpperCAmelCase    )
							elif beta_schedule == "sigmoid":
													# GeoDiff sigmoid schedule
													lowerCAmelCase__	 =  torch.linspace(-6						,      6						,      __UpperCAmelCase    )
													lowerCAmelCase__	 =  torch.sigmoid(__UpperCAmelCase    ) * (beta_end - beta_start) + beta_start
							else:
													raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"    )

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

							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						,      __UpperCAmelCase    )[::-1].copy()    )

							lowerCAmelCase__	 =  eta

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase = None    )->   torch.FloatTensor:







							'''simple docstring'''





							return sample

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase = 10						,      __UpperCAmelCase = 10						,      __UpperCAmelCase = None						,      )->   str:







							'''simple docstring'''





							lowerCAmelCase__	 =  min(self.config.num_train_timesteps						,      __UpperCAmelCase    )
							lowerCAmelCase__	 =  num_inference_steps

							lowerCAmelCase__	 =  []

							lowerCAmelCase__	 =  {}
							for j in range(0						,      num_inference_steps - jump_length						,      __UpperCAmelCase    ):
													lowerCAmelCase__	 =  jump_n_sample - 1

							lowerCAmelCase__	 =  num_inference_steps
							while t >= 1:
													lowerCAmelCase__	 =  t - 1
													timesteps.append(__UpperCAmelCase    )

													if jumps.get(__UpperCAmelCase						,      0    ) > 0:
																			lowerCAmelCase__	 =  jumps[t] - 1
																			for _ in range(__UpperCAmelCase    ):
																									lowerCAmelCase__	 =  t + 1
																									timesteps.append(__UpperCAmelCase    )

							lowerCAmelCase__	 =  np.array(__UpperCAmelCase    ) * (self.config.num_train_timesteps // self.num_inference_steps)
							lowerCAmelCase__	 =  torch.from_numpy(__UpperCAmelCase    ).to(__UpperCAmelCase    )

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







							'''simple docstring'''





							lowerCAmelCase__	 =  t - self.config.num_train_timesteps // self.num_inference_steps

							lowerCAmelCase__	 =  self.alphas_cumprod[t]
							lowerCAmelCase__	 =  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
							lowerCAmelCase__	 =  1 - alpha_prod_t
							lowerCAmelCase__	 =  1 - 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
							# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
							# without eta.
							# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
							lowerCAmelCase__	 =  (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

							return variance

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase = None						,      __UpperCAmelCase = True						,      )->   Union[RePaintSchedulerOutput, Tuple]:







							'''simple docstring'''





							lowerCAmelCase__	 =  timestep
							lowerCAmelCase__	 =  timestep - self.config.num_train_timesteps // self.num_inference_steps

							# 1. compute alphas, betas
							lowerCAmelCase__	 =  self.alphas_cumprod[t]
							lowerCAmelCase__	 =  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
							lowerCAmelCase__	 =  1 - alpha_prod_t

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							lowerCAmelCase__	 =  (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
													lowerCAmelCase__	 =  torch.clamp(__UpperCAmelCase						,      -1						,      1    )

							# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
							# substitute formula (7) in the algorithm coming from DDPM paper
							# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
							# DDIM schedule gives the same results as DDPM with eta = 1.0
							# Noise is being reused in 7. and 8., but no impact on quality has
							# been observed.

							# 5. Add noise
							lowerCAmelCase__	 =  model_output.device
							lowerCAmelCase__	 =  randn_tensor(model_output.shape						,      generator=__UpperCAmelCase						,      device=__UpperCAmelCase						,      dtype=model_output.dtype    )
							lowerCAmelCase__	 =  self.eta * self._get_variance(__UpperCAmelCase    ) ** 0.5

							lowerCAmelCase__	 =  0
							if t > 0 and self.eta > 0:
													lowerCAmelCase__	 =  std_dev_t * noise

							# 6. compute "direction pointing to x_t" of formula (12)
							# from https://arxiv.org/pdf/2010.02502.pdf
							lowerCAmelCase__	 =  (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

							# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							lowerCAmelCase__	 =  alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

							# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
							lowerCAmelCase__	 =  (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

							# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
							lowerCAmelCase__	 =  mask * prev_known_part + (1.0 - mask) * prev_unknown_part

							if not return_dict:
													return (
													    pred_prev_sample,
													    pred_original_sample,
													)

							return RePaintSchedulerOutput(prev_sample=__UpperCAmelCase						,      pred_original_sample=__UpperCAmelCase    )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase=None    )->   List[Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  self.config.num_train_timesteps // self.num_inference_steps

							for i in range(__UpperCAmelCase    ):
													lowerCAmelCase__	 =  self.betas[timestep + i]
													if sample.device.type == "mps":
																			# randn does not work reproducibly on mps
																			lowerCAmelCase__	 =  randn_tensor(sample.shape						,      dtype=sample.dtype						,      generator=__UpperCAmelCase    )
																			lowerCAmelCase__	 =  noise.to(sample.device    )
													else:
																			lowerCAmelCase__	 =  randn_tensor(sample.shape						,      generator=__UpperCAmelCase						,      device=sample.device						,      dtype=sample.dtype    )

													# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
													lowerCAmelCase__	 =  (1 - beta) ** 0.5 * sample + beta**0.5 * noise

							return sample

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      )->   torch.FloatTensor:







							'''simple docstring'''





							raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."    )


	def __len__(							self    )->   Optional[int]:







							'''simple docstring'''





							return self.config.num_train_timesteps














import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor


class        lowercase__       (     unittest.TestCase		):

	@property
	def 						UpperCAmelCase					(							self    )->   List[str]:







							'''simple docstring'''





							lowerCAmelCase__	 =  1
							lowerCAmelCase__	 =  3
							lowerCAmelCase__	 =  8
							lowerCAmelCase__	 =  8

							lowerCAmelCase__	 =  torch.rand((batch_size, num_channels, height, width)    )

							return sample

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   Dict:







							'''simple docstring'''





							if isinstance(image[0]						,      PIL.Image.Image    ):
													return np.stack([np.array(__UpperCAmelCase    ) for i in image]						,      axis=0    )
							elif isinstance(__UpperCAmelCase						,      torch.Tensor    ):
													return image.cpu().numpy().transpose(0						,      2						,      3						,      1    )
							return image

	def 						UpperCAmelCase					(							self    )->   Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )

							lowerCAmelCase__	 =  self.dummy_sample
							lowerCAmelCase__	 =  self.to_np(__UpperCAmelCase    )

							for output_type in ["pt", "np", "pil"]:
													lowerCAmelCase__	 =  image_processor.postprocess(
													    image_processor.preprocess(__UpperCAmelCase    )						,      output_type=__UpperCAmelCase						,      )
													lowerCAmelCase__	 =  self.to_np(__UpperCAmelCase    )
													lowerCAmelCase__	 =  (input_np * 255).round() if output_type == "pil" else input_np
													assert (
													    np.abs(in_np - out_np    ).max() < 1E-6
													), F"decoded output does not match input for output_type {output_type}"

	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )
							lowerCAmelCase__	 =  self.dummy_sample.cpu().numpy().transpose(0						,      2						,      3						,      1    )

							for output_type in ["pt", "np", "pil"]:
													lowerCAmelCase__	 =  image_processor.postprocess(image_processor.preprocess(__UpperCAmelCase    )						,      output_type=__UpperCAmelCase    )

													lowerCAmelCase__	 =  self.to_np(__UpperCAmelCase    )
													lowerCAmelCase__	 =  (input_np * 255).round() if output_type == "pil" else input_np
													assert (
													    np.abs(in_np - out_np    ).max() < 1E-6
													), F"decoded output does not match input for output_type {output_type}"

	def 						UpperCAmelCase					(							self    )->   Any:







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )

							lowerCAmelCase__	 =  self.dummy_sample.cpu().numpy().transpose(0						,      2						,      3						,      1    )
							lowerCAmelCase__	 =  image_processor.numpy_to_pil(__UpperCAmelCase    )

							for output_type in ["pt", "np", "pil"]:
													lowerCAmelCase__	 =  image_processor.postprocess(image_processor.preprocess(__UpperCAmelCase    )						,      output_type=__UpperCAmelCase    )
													for i, o in zip(__UpperCAmelCase						,      __UpperCAmelCase    ):
																			lowerCAmelCase__	 =  np.array(__UpperCAmelCase    )
																			lowerCAmelCase__	 =  self.to_np(__UpperCAmelCase    ) if output_type == "pil" else (self.to_np(__UpperCAmelCase    ) * 255).round()
																			assert (
																			    np.abs(in_np - out_np    ).max() < 1E-6
																			), F"decoded output does not match input for output_type {output_type}"

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







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )

							lowerCAmelCase__	 =  self.dummy_sample
							lowerCAmelCase__	 =  input_pt_ad.squeeze(0    )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )
							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							lowerCAmelCase__	 =  self.to_np(self.dummy_sample    )
							lowerCAmelCase__	 =  input_np_ad.squeeze(0    )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )
							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							assert np.abs(out_pt_ad - out_pt_ad    ).max() < 1E-6
							assert np.abs(out_np_ad - out_np_ad    ).max() < 1E-6


	def 						UpperCAmelCase					(							self    )->   List[Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )

							lowerCAmelCase__	 =  self.dummy_sample
							lowerCAmelCase__	 =  list(__UpperCAmelCase    )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							lowerCAmelCase__	 =  self.to_np(self.dummy_sample    )
							list(__UpperCAmelCase    )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							assert np.abs(out_pt_ad - out_pt_list    ).max() < 1E-6
							assert np.abs(out_np_ad - out_np_list    ).max() < 1E-6






