



from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION



class 			_lowercase							(     lowerCAmelCase,   lowerCAmelCase			):





	"""simple docstring"""




	def __init__(self	):
						"""simple docstring"""

						super().__init__()

						self.register_buffer("watermark_image"	, torch.zeros((62, 62, 4)	)	)
						a        =     None




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_=None	):
						"""simple docstring"""

						a        =     images[0].height
						a        =     images[0].width

						a        =     sample_size or h

						a        =     min(h / sample_size	, w / sample_size	)
						a  ,       a        =     (int(h / coef	), int(w / coef	)) if coef < 1 else (h, w)

						a  ,       a        =     1024**2, img_w * img_h
						a        =     (Sa / Sa) ** 0.5
						a  ,       a  ,       a        =     int(K * 62	), img_w - int(14 * K	), img_h - int(14 * K	)

						if self.watermark_image_as_pil is None:
											a        =     self.watermark_image.to(torch.uinta	).cpu().numpy()
											a        =     Image.fromarray(lowerCamelCase_	, mode="RGBA"	)
											a        =     watermark_image

						a        =     self.watermark_image_as_pil.resize(
						    (wm_size, wm_size)	, PIL_INTERPOLATION["bicubic"]	, reducing_gap=lowerCamelCase_	)

						for pil_img in images:
											pil_img.paste(lowerCamelCase_	, box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)	, mask=wm_img.split()[-1]	)

						return images





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 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""
	__A					=							42
	__A					=							None






def 					a(   A :					Dict				,  A :					Optional[int]=0.999				,  A :					Optional[int]="cosine"				,  )	-> List[str]:
					"""simple docstring"""





					if alpha_transform_type == "cosine":

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

					elif alpha_transform_type == "exp":

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

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

					a        =     []
					for i in range(A ):
										a        =     i / num_diffusion_timesteps
										a        =     (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 			_lowercase							(     lowerCAmelCase,   lowerCAmelCase			):





	"""simple docstring"""
	__A					=							[e.name for e in KarrasDiffusionSchedulers]
	__A					=							1




	@register_to_config
	def __init__(self	, lowerCamelCase_ = 1000	, lowerCamelCase_ = 0.0001	, lowerCamelCase_ = 0.02	, lowerCamelCase_ = "linear"	, lowerCamelCase_ = None	, lowerCamelCase_ = False	, lowerCamelCase_ = "epsilon"	, lowerCamelCase_ = "linspace"	, lowerCamelCase_ = 0	, ):
						"""simple docstring"""

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

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

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

						# setable values
						a        =     None
						a        =     use_karras_sigmas
						self.set_timesteps(lowerCamelCase_	, lowerCamelCase_	)
						a        =     []
						a        =     False




	@property
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

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

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




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

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




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						def lms_derivative(lowerCamelCase_	):
											a        =     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

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

						return integrated_coeff




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_ = None	):
						"""simple docstring"""

						a        =     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":
											a        =     np.linspace(0	, self.config.num_train_timesteps - 1	, lowerCamelCase_	, dtype=lowerCamelCase_	)[
											    ::-1
											].copy()
						elif self.config.timestep_spacing == "leading":
											a        =     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
											a        =     (np.arange(0	, lowerCamelCase_	) * step_ratio).round()[::-1].copy().astype(lowerCamelCase_	)
											timesteps += self.config.steps_offset
						elif self.config.timestep_spacing == "trailing":
											a        =     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
											a        =     (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\'.'''	)

						a        =     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
						a        =     np.log(lowerCamelCase_	)
						a        =     np.interp(lowerCamelCase_	, np.arange(0	, len(lowerCamelCase_	)	)	, lowerCamelCase_	)

						if self.use_karras_sigmas:
											a        =     self._convert_to_karras(in_sigmas=lowerCamelCase_	)
											a        =     np.array([self._sigma_to_t(lowerCamelCase_	, lowerCamelCase_	) for sigma in sigmas]	)

						a        =     np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)

						a        =     torch.from_numpy(lowerCamelCase_	).to(device=lowerCamelCase_	)
						if str(lowerCamelCase_	).startswith("mps"	):
											# mps does not support float64
											a        =     torch.from_numpy(lowerCamelCase_	).to(lowerCamelCase_	, dtype=torch.floataa	)
						else:
											a        =     torch.from_numpy(lowerCamelCase_	).to(device=lowerCamelCase_	)

						a        =     []




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     np.log(lowerCamelCase_	)

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

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

						a        =     log_sigmas[low_idx]
						a        =     log_sigmas[high_idx]

						# interpolate sigmas
						a        =     (low - log_sigma) / (low - high)
						a        =     np.clip(lowerCamelCase_	, 0	, 1	)

						# transform interpolation to time range
						a        =     (1 - w) * low_idx + w * high_idx
						a        =     t.reshape(sigma.shape	)
						return t




	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     in_sigmas[-1].item()
						a        =     in_sigmas[0].item()

						a        =     7.0  # 7.0 is the value used in the paper
						a        =     np.linspace(0	, 1	, self.num_inference_steps	)
						a        =     sigma_min ** (1 / rho)
						a        =     sigma_max ** (1 / rho)
						a        =     (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
						return sigmas




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = 4	, lowerCamelCase_ = 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	):
											a        =     timestep.to(self.timesteps.device	)
						a        =     (self.timesteps == timestep).nonzero().item()
						a        =     self.sigmas[step_index]

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
											a        =     sample - sigma * model_output
						elif self.config.prediction_type == "v_prediction":
											# * c_out + input * c_skip
											a        =     model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
						elif self.config.prediction_type == "sample":
											a        =     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
						a        =     (sample - pred_original_sample) / sigma
						self.derivatives.append(lowerCamelCase_	)
						if len(self.derivatives	) > order:
											self.derivatives.pop(0	)

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

						# 4. Compute previous sample based on the derivatives path
						a        =     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   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, ):
						"""simple docstring"""

						a        =     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
											a        =     self.timesteps.to(original_samples.device	, dtype=torch.floataa	)
											a        =     timesteps.to(original_samples.device	, dtype=torch.floataa	)
						else:
											a        =     self.timesteps.to(original_samples.device	)
											a        =     timesteps.to(original_samples.device	)

						a        =     [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

						a        =     original_samples + noise * sigma
						return noisy_samples




	def __len__(self	):
						"""simple docstring"""

						return self.config.num_train_timesteps
