




"""simple docstring"""







class       SCREAMING_SNAKE_CASE__					:


						def __init__(							self				:			Union[str, Any]						,						lowerCAmelCase				:			int					):
										lowerCAmelCase  =				n
										lowerCAmelCase  =				[None] * self.n
										lowerCAmelCase  =				0  # index of the first element
										lowerCAmelCase  =				0
										lowerCAmelCase  =				0


						def __len__(							self				:			Optional[int]					):
										return self.size


						def   __lowercase (							self				:			Union[str, Any]					):
										return self.size == 0


						def   __lowercase (							self				:			Any					):
										return False if self.is_empty() else self.array[self.front]


						def   __lowercase (							self				:			str						,						lowerCAmelCase				:			Optional[Any]					):
										if self.size >= self.n:
														raise Exception("""QUEUE IS FULL"""					)

										lowerCAmelCase  =				data
										lowerCAmelCase  =				(self.rear + 1) % self.n
										self.size += 1
										return self




						def   __lowercase (							self				:			Union[str, Any]					):
										if self.size == 0:
														raise Exception("""UNDERFLOW"""					)

										lowerCAmelCase  =				self.array[self.front]
										lowerCAmelCase  =				None
										lowerCAmelCase  =				(self.front + 1) % self.n
										self.size -= 1
										return temp











"""simple docstring"""







import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def 							lowercase					(snake_case__   : Dict      ,       snake_case__   : int=0.999      ,       snake_case__   : Tuple="cosine"      ,       )     ->  Any:


				'''simple docstring'''






				if alpha_transform_type == "cosine":

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

				elif alpha_transform_type == "exp":

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

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

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






class       SCREAMING_SNAKE_CASE__					(					_a						,				_a					):
						_a    						=						[e.name for e in KarrasDiffusionSchedulers]
						_a    						=						2


						@register_to_config
						def __init__(							self				:			List[Any]						,						lowerCAmelCase				:			int = 1000						,						lowerCAmelCase				:			float = 0.0_0085						,						lowerCAmelCase				:			float = 0.012						,						lowerCAmelCase				:			str = "linear"						,						lowerCAmelCase				:			Optional[Union[np.ndarray, List[float]]] = None						,						lowerCAmelCase				:			str = "epsilon"						,						lowerCAmelCase				:			Optional[bool] = False						,						lowerCAmelCase				:			Optional[bool] = False						,						lowerCAmelCase				:			float = 1.0						,						lowerCAmelCase				:			str = "linspace"						,						lowerCAmelCase				:			int = 0						,						):
										if trained_betas is not None:
														lowerCAmelCase  =				torch.tensor(lowerCAmelCase						,						dtype=torch.floataa					)
										elif beta_schedule == "linear":
														lowerCAmelCase  =				torch.linspace(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase						,						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						,						lowerCAmelCase						,						dtype=torch.floataa					) ** 2
														)
										elif beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														lowerCAmelCase  =				betas_for_alpha_bar(lowerCAmelCase						,						alpha_transform_type="""cosine"""					)
										elif beta_schedule == "exp":
														lowerCAmelCase  =				betas_for_alpha_bar(lowerCAmelCase						,						alpha_transform_type="""exp"""					)
										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					)

										#  set all values
										self.set_timesteps(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)
										lowerCAmelCase  =				use_karras_sigmas


						def   __lowercase (							self				:			Any						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			Optional[int]=None					):
										if schedule_timesteps is None:
														lowerCAmelCase  =				self.timesteps

										lowerCAmelCase  =				(schedule_timesteps == timestep).nonzero()

										# The sigma index that is taken for the **very** first `step`
										# is always the second index (or the last index if there is only 1)
										# This way we can ensure we don't accidentally skip a sigma in
										# case we start in the middle of the denoising schedule (e.g. for image-to-image)
										if len(self._index_counter					) == 0:
														lowerCAmelCase  =				1 if len(lowerCAmelCase					) > 1 else 0
										else:
														lowerCAmelCase  =				timestep.cpu().item() if torch.is_tensor(lowerCAmelCase					) else timestep
														lowerCAmelCase  =				self._index_counter[timestep_int]

										return indices[pos].item()


						@property
						def   __lowercase (							self				:			Optional[Any]					):
										# standard deviation of the initial noise distribution
										if self.config.timestep_spacing in ["linspace", "trailing"]:
														return self.sigmas.max()

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


						def   __lowercase (							self				:			Dict						,						lowerCAmelCase				:			torch.FloatTensor						,						lowerCAmelCase				:			Union[float, torch.FloatTensor]						,						):
										lowerCAmelCase  =				self.index_for_timestep(lowerCAmelCase					)

										lowerCAmelCase  =				self.sigmas[step_index]
										lowerCAmelCase  =				sample / ((sigma**2 + 1) ** 0.5)
										return sample


						def   __lowercase (							self				:			List[Any]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			Union[str, torch.device] = None						,						lowerCAmelCase				:			Optional[int] = None						,						):
										lowerCAmelCase  =				num_inference_steps

										lowerCAmelCase  =				num_train_timesteps or self.config.num_train_timesteps

										# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
										if self.config.timestep_spacing == "linspace":
														lowerCAmelCase  =				np.linspace(0						,						num_train_timesteps - 1						,						lowerCAmelCase						,						dtype=lowerCAmelCase					)[::-1].copy()
										elif self.config.timestep_spacing == "leading":
														lowerCAmelCase  =				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  =				(np.arange(0						,						lowerCAmelCase					) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase					)
														timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
														lowerCAmelCase  =				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  =				(np.arange(lowerCAmelCase						,						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  =				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
										lowerCAmelCase  =				np.log(lowerCAmelCase					)
										lowerCAmelCase  =				np.interp(lowerCAmelCase						,						np.arange(0						,						len(lowerCAmelCase					)					)						,						lowerCAmelCase					)

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

										lowerCAmelCase  =				np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
										lowerCAmelCase  =				torch.from_numpy(lowerCAmelCase					).to(device=lowerCAmelCase					)
										lowerCAmelCase  =				torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2					), sigmas[-1:]]					)

										lowerCAmelCase  =				torch.from_numpy(lowerCAmelCase					)
										lowerCAmelCase  =				torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2					)]					)

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

										# empty dt and derivative
										lowerCAmelCase  =				None
										lowerCAmelCase  =				None

										# for exp beta schedules, such as the one for `pipeline_shap_e.py`
										# we need an index counter
										lowerCAmelCase  =				defaultdict(lowerCAmelCase					)


						def   __lowercase (							self				:			List[Any]						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			Any					):
										# get log sigma
										lowerCAmelCase  =				np.log(lowerCAmelCase					)

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

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

										lowerCAmelCase  =				log_sigmas[low_idx]
										lowerCAmelCase  =				log_sigmas[high_idx]

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

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


						def   __lowercase (							self				:			Union[str, Any]						,						lowerCAmelCase				:			torch.FloatTensor						,						lowerCAmelCase				:			Union[str, Any]					):
										lowerCAmelCase  =				in_sigmas[-1].item()
										lowerCAmelCase  =				in_sigmas[0].item()

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


						@property
						def   __lowercase (							self				:			Dict					):
										return self.dt is None


						def   __lowercase (							self				:			str						,						lowerCAmelCase				:			Union[torch.FloatTensor, np.ndarray]						,						lowerCAmelCase				:			Union[float, torch.FloatTensor]						,						lowerCAmelCase				:			Union[torch.FloatTensor, np.ndarray]						,						lowerCAmelCase				:			bool = True						,						):
										lowerCAmelCase  =				self.index_for_timestep(lowerCAmelCase					)

										# advance index counter by 1
										lowerCAmelCase  =				timestep.cpu().item() if torch.is_tensor(lowerCAmelCase					) else timestep
										self._index_counter[timestep_int] += 1

										if self.state_in_first_order:
														lowerCAmelCase  =				self.sigmas[step_index]
														lowerCAmelCase  =				self.sigmas[step_index + 1]
										else:
														# 2nd order / Heun's method
														lowerCAmelCase  =				self.sigmas[step_index - 1]
														lowerCAmelCase  =				self.sigmas[step_index]

										# currently only gamma=0 is supported. This usually works best anyways.
										# We can support gamma in the future but then need to scale the timestep before
										# passing it to the model which requires a change in API
										lowerCAmelCase  =				0
										lowerCAmelCase  =				sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										if self.config.prediction_type == "epsilon":
														lowerCAmelCase  =				sigma_hat if self.state_in_first_order else sigma_next
														lowerCAmelCase  =				sample - sigma_input * model_output
										elif self.config.prediction_type == "v_prediction":
														lowerCAmelCase  =				sigma_hat if self.state_in_first_order else sigma_next
														lowerCAmelCase  =				model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
														    sample / (sigma_input**2 + 1)
														)
										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 `v_prediction`'''					)

										if self.config.clip_sample:
														lowerCAmelCase  =				pred_original_sample.clamp(
														    -self.config.clip_sample_range						,						self.config.clip_sample_range					)

										if self.state_in_first_order:
														# 2. Convert to an ODE derivative for 1st order
														lowerCAmelCase  =				(sample - pred_original_sample) / sigma_hat
														# 3. delta timestep
														lowerCAmelCase  =				sigma_next - sigma_hat

														# store for 2nd order step
														lowerCAmelCase  =				derivative
														lowerCAmelCase  =				dt
														lowerCAmelCase  =				sample
										else:
														# 2. 2nd order / Heun's method
														lowerCAmelCase  =				(sample - pred_original_sample) / sigma_next
														lowerCAmelCase  =				(self.prev_derivative + derivative) / 2

														# 3. take prev timestep & sample
														lowerCAmelCase  =				self.dt
														lowerCAmelCase  =				self.sample

														# free dt and derivative
														# Note, this puts the scheduler in "first order mode"
														lowerCAmelCase  =				None
														lowerCAmelCase  =				None
														lowerCAmelCase  =				None

										lowerCAmelCase  =				sample + derivative * dt

										if not return_dict:
														return (prev_sample,)

										return SchedulerOutput(prev_sample=lowerCAmelCase					)


						def   __lowercase (							self				:			Optional[int]						,						lowerCAmelCase				:			torch.FloatTensor						,						lowerCAmelCase				:			torch.FloatTensor						,						lowerCAmelCase				:			torch.FloatTensor						,						):
										# Make sure sigmas and timesteps have the same device and dtype as original_samples
										lowerCAmelCase  =				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  =				self.timesteps.to(original_samples.device						,						dtype=torch.floataa					)
														lowerCAmelCase  =				timesteps.to(original_samples.device						,						dtype=torch.floataa					)
										else:
														lowerCAmelCase  =				self.timesteps.to(original_samples.device					)
														lowerCAmelCase  =				timesteps.to(original_samples.device					)

										lowerCAmelCase  =				[self.index_for_timestep(lowerCAmelCase						,						lowerCAmelCase					) for t in timesteps]

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

										lowerCAmelCase  =				original_samples + noise * sigma
										return noisy_samples




						def __len__(							self				:			Union[str, Any]					):
										return self.config.num_train_timesteps





