




import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

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


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class        __SCREAMING_SNAKE_CASE							(	A__						):
					_lowerCamelCase							=				42
					_lowerCamelCase							=				None






def       snake_case      (							A		,     A=0.999		,     A="cosine"		,     )							->							List[str]:




	'''simple docstring'''


	if alpha_transform_type == "cosine":

		def alpha_bar_fn(A    ):
			return math.cos((t + 0.008) / 1.008 * math.pi / 2    ) ** 2

	elif alpha_transform_type == "exp":

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

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

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



class        __SCREAMING_SNAKE_CASE							(	A__     ,  A__						):
					_lowerCamelCase							=				[e.name for e in KarrasDiffusionSchedulers]
					_lowerCamelCase							=				1

					@register_to_config
					def __init__(       self				, __lowercase = 1_000				, __lowercase = 0.00_01				, __lowercase = 0.02				, __lowercase = "linear"				, __lowercase = None				, __lowercase = False				, __lowercase = "epsilon"				, __lowercase = "linspace"				, __lowercase = 0				, ):







						'''simple docstring'''




						if trained_betas is not None:
							lowerCamelCase_							:	Optional[int]							     =							torch.tensor(_lowerCAmelCase				, dtype=torch.floataa      )
						elif beta_schedule == "linear":
							lowerCamelCase_							:	Optional[Any]							     =							torch.linspace(_lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase				, dtype=torch.floataa      )
						elif beta_schedule == "scaled_linear":
							# this schedule is very specific to the latent diffusion model.
							lowerCamelCase_							:	Optional[Any]							     =							(
							    torch.linspace(beta_start**0.5				, beta_end**0.5				, _lowerCAmelCase				, dtype=torch.floataa      ) ** 2
							)
						elif beta_schedule == "squaredcos_cap_v2":
							# Glide cosine schedule
							lowerCamelCase_							:	int							     =							betas_for_alpha_bar(_lowerCAmelCase      )
						else:
							raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"      )

						lowerCamelCase_							:	Tuple							     =							1.0 - self.betas
						lowerCamelCase_							:	int							     =							torch.cumprod(self.alphas				, dim=0      )

						lowerCamelCase_							:	List[Any]							     =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
						lowerCamelCase_							:	int							     =							np.concatenate([sigmas[::-1], [0.0]]      ).astype(np.floataa      )
						lowerCamelCase_							:	List[Any]							     =							torch.from_numpy(_lowerCAmelCase      )

						# setable values
						lowerCamelCase_							:	Optional[Any]							     =							None
						lowerCamelCase_							:	Union[str, Any]							     =							use_karras_sigmas
						self.set_timesteps(_lowerCAmelCase				, _lowerCAmelCase      )
						lowerCamelCase_							:	Any							     =							[]
						lowerCamelCase_							:	Union[str, Any]							     =							False

					@property
					def 			lowercase_							(       self      ):







						'''simple docstring'''




						if self.config.timestep_spacing in ["linspace", "trailing"]:
							return self.sigmas.max()

						return (self.sigmas.max() ** 2 + 1) ** 0.5

					def 			lowercase_							(       self				, __lowercase				, __lowercase      ):







						'''simple docstring'''




						if isinstance(_lowerCAmelCase				, torch.Tensor      ):
							lowerCamelCase_							:	List[Any]							     =							timestep.to(self.timesteps.device      )
						lowerCamelCase_							:	str							     =							(self.timesteps == timestep).nonzero().item()
						lowerCamelCase_							:	Tuple							     =							self.sigmas[step_index]
						lowerCamelCase_							:	int							     =							sample / ((sigma**2 + 1) ** 0.5)
						lowerCamelCase_							:	List[str]							     =							True
						return sample

					def 			lowercase_							(       self				, __lowercase				, __lowercase				, __lowercase      ):







						'''simple docstring'''




						def lms_derivative(__lowercase      ):
							lowerCamelCase_							:	Union[str, Any]							     =							1.0
							for k in range(_lowerCAmelCase      ):
								if current_order == k:
									continue
								prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
							return prod

						lowerCamelCase_							:	Optional[Any]							     =							integrate.quad(_lowerCAmelCase				, self.sigmas[t]				, self.sigmas[t + 1]				, epsrel=1E-4      )[0]

						return integrated_coeff

					def 			lowercase_							(       self				, __lowercase				, __lowercase = None      ):







						'''simple docstring'''




						lowerCamelCase_							:	Union[str, Any]							     =							num_inference_steps

						# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
						if self.config.timestep_spacing == "linspace":
							lowerCamelCase_							:	int							     =							np.linspace(0				, self.config.num_train_timesteps - 1				, _lowerCAmelCase				, dtype=_lowerCAmelCase      )[
							    ::-1
							].copy()
						elif self.config.timestep_spacing == "leading":
							lowerCamelCase_							:	Dict							     =							self.config.num_train_timesteps // self.num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# casting to int to avoid issues when num_inference_step is power of 3
							lowerCamelCase_							:	Optional[Any]							     =							(np.arange(0				, _lowerCAmelCase      ) * step_ratio).round()[::-1].copy().astype(_lowerCAmelCase      )
							timesteps += self.config.steps_offset
						elif self.config.timestep_spacing == "trailing":
							lowerCamelCase_							:	Any							     =							self.config.num_train_timesteps / self.num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# casting to int to avoid issues when num_inference_step is power of 3
							lowerCamelCase_							:	Union[str, Any]							     =							(np.arange(self.config.num_train_timesteps				, 0				, -step_ratio      )).round().copy().astype(_lowerCAmelCase      )
							timesteps -= 1
						else:
							raise ValueError(
							    f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."      )

						lowerCamelCase_							:	List[str]							     =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
						lowerCamelCase_							:	Dict							     =							np.log(_lowerCAmelCase      )
						lowerCamelCase_							:	Tuple							     =							np.interp(_lowerCAmelCase				, np.arange(0				, len(_lowerCAmelCase      )      )				, _lowerCAmelCase      )

						if self.use_karras_sigmas:
							lowerCamelCase_							:	Union[str, Any]							     =							self._convert_to_karras(in_sigmas=_lowerCAmelCase      )
							lowerCamelCase_							:	int							     =							np.array([self._sigma_to_t(_lowerCAmelCase				, _lowerCAmelCase      ) for sigma in sigmas]      )

						lowerCamelCase_							:	Tuple							     =							np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )

						lowerCamelCase_							:	int							     =							torch.from_numpy(_lowerCAmelCase      ).to(device=_lowerCAmelCase      )
						if str(_lowerCAmelCase      ).startswith('mps'      ):
							# mps does not support float64
							lowerCamelCase_							:	str							     =							torch.from_numpy(_lowerCAmelCase      ).to(_lowerCAmelCase				, dtype=torch.floataa      )
						else:
							lowerCamelCase_							:	Optional[int]							     =							torch.from_numpy(_lowerCAmelCase      ).to(device=_lowerCAmelCase      )

						lowerCamelCase_							:	List[Any]							     =							[]

					def 			lowercase_							(       self				, __lowercase				, __lowercase      ):







						'''simple docstring'''




						lowerCamelCase_							:	Optional[Any]							     =							np.log(_lowerCAmelCase      )

						# get distribution
						lowerCamelCase_							:	int							     =							log_sigma - log_sigmas[:, np.newaxis]

						# get sigmas range
						lowerCamelCase_							:	int							     =							np.cumsum((dists >= 0)				, axis=0      ).argmax(axis=0      ).clip(max=log_sigmas.shape[0] - 2      )
						lowerCamelCase_							:	Tuple							     =							low_idx + 1

						lowerCamelCase_							:	Tuple							     =							log_sigmas[low_idx]
						lowerCamelCase_							:	str							     =							log_sigmas[high_idx]

						# interpolate sigmas
						lowerCamelCase_							:	str							     =							(low - log_sigma) / (low - high)
						lowerCamelCase_							:	List[str]							     =							np.clip(_lowerCAmelCase				, 0				, 1      )

						# transform interpolation to time range
						lowerCamelCase_							:	Union[str, Any]							     =							(1 - w) * low_idx + w * high_idx
						lowerCamelCase_							:	Optional[Any]							     =							t.reshape(sigma.shape      )
						return t

					def 			lowercase_							(       self				, __lowercase      ):







						'''simple docstring'''




						lowerCamelCase_							:	Dict							     =							in_sigmas[-1].item()
						lowerCamelCase_							:	str							     =							in_sigmas[0].item()

						lowerCamelCase_							:	List[Any]							     =							7.0  # 7.0 is the value used in the paper
						lowerCamelCase_							:	Optional[int]							     =							np.linspace(0				, 1				, self.num_inference_steps      )
						lowerCamelCase_							:	Optional[Any]							     =							sigma_min ** (1 / rho)
						lowerCamelCase_							:	List[Any]							     =							sigma_max ** (1 / rho)
						lowerCamelCase_							:	List[Any]							     =							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
						return sigmas

					def 			lowercase_							(       self				, __lowercase				, __lowercase				, __lowercase				, __lowercase = 4				, __lowercase = True				, ):







						'''simple docstring'''




						if not self.is_scale_input_called:
							warnings.warn(
							    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
							    'See `StableDiffusionPipeline` for a usage example.'      )

						if isinstance(_lowerCAmelCase				, torch.Tensor      ):
							lowerCamelCase_							:	List[Any]							     =							timestep.to(self.timesteps.device      )
						lowerCamelCase_							:	Tuple							     =							(self.timesteps == timestep).nonzero().item()
						lowerCamelCase_							:	Tuple							     =							self.sigmas[step_index]

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
							lowerCamelCase_							:	List[str]							     =							sample - sigma * model_output
						elif self.config.prediction_type == "v_prediction":
							# * c_out + input * c_skip
							lowerCamelCase_							:	str							     =							model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
						elif self.config.prediction_type == "sample":
							lowerCamelCase_							:	Any							     =							model_output
						else:
							raise ValueError(
							    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"      )

						# 2. Convert to an ODE derivative
						lowerCamelCase_							:	Dict							     =							(sample - pred_original_sample) / sigma
						self.derivatives.append(_lowerCAmelCase      )
						if len(self.derivatives      ) > order:
							self.derivatives.pop(0      )

						# 3. Compute linear multistep coefficients
						lowerCamelCase_							:	Any							     =							min(step_index + 1				, _lowerCAmelCase      )
						lowerCamelCase_							:	int							     =							[self.get_lms_coefficient(_lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase      ) for curr_order in range(_lowerCAmelCase      )]

						# 4. Compute previous sample based on the derivatives path
						lowerCamelCase_							:	Tuple							     =							sample + sum(
						    coeff * derivative for coeff, derivative in zip(_lowerCAmelCase				, reversed(self.derivatives      )      )      )

						if not return_dict:
							return (prev_sample,)

						return LMSDiscreteSchedulerOutput(prev_sample=_lowerCAmelCase				, pred_original_sample=_lowerCAmelCase      )

					def 			lowercase_							(       self				, __lowercase				, __lowercase				, __lowercase				, ):







						'''simple docstring'''




						lowerCamelCase_							:	str							     =							self.sigmas.to(device=original_samples.device				, dtype=original_samples.dtype      )
						if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCAmelCase      ):
							# mps does not support float64
							lowerCamelCase_							:	Union[str, Any]							     =							self.timesteps.to(original_samples.device				, dtype=torch.floataa      )
							lowerCamelCase_							:	Dict							     =							timesteps.to(original_samples.device				, dtype=torch.floataa      )
						else:
							lowerCamelCase_							:	Any							     =							self.timesteps.to(original_samples.device      )
							lowerCamelCase_							:	List[Any]							     =							timesteps.to(original_samples.device      )

						lowerCamelCase_							:	Any							     =							[(schedule_timesteps == t).nonzero().item() for t in timesteps]

						lowerCamelCase_							:	Tuple							     =							sigmas[step_indices].flatten()
						while len(sigma.shape      ) < len(original_samples.shape      ):
							lowerCamelCase_							:	int							     =							sigma.unsqueeze(-1      )

						lowerCamelCase_							:	Tuple							     =							original_samples + noise * sigma
						return noisy_samples





					def __len__(       self      ):







						'''simple docstring'''




						return self.config.num_train_timesteps






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

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
							from .image_encoder import PaintByExampleImageEncoder
							from .pipeline_paint_by_example import PaintByExamplePipeline
