




"""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 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase=0.9_9_9,  _lowerCamelCase="cosine",  )			->int:






		"""simple docstring"""


		if alpha_transform_type == "cosine":

				def alpha_bar_fn(_lowerCamelCase				):
						return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2				) ** 2

		elif alpha_transform_type == "exp":

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

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

		__lowercase				:		Any												=				[]
		for i in range(_lowerCamelCase				):
				__lowercase				:		List[Any]												=				i / num_diffusion_timesteps
				__lowercase				:		Dict												=				(i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(_lowerCamelCase				) / alpha_bar_fn(_lowerCamelCase				),  _lowerCamelCase				)				)
		return torch.tensor(_lowerCamelCase,  dtype=torch.floataa				)




class       lowerCAmelCase__       (			lowerCAmelCase_  ,			lowerCAmelCase_    ):


				"""simple docstring"""





				__UpperCAmelCase :     Any       =      [e.name for e in KarrasDiffusionSchedulers]
				__UpperCAmelCase :     int       =      2



				@register_to_config
				def __init__(     self					:	Dict    ,   lowercase__					:	int = 1_0_0_0    ,   lowercase__					:	float = 0.0_0_0_8_5    ,   lowercase__					:	float = 0.0_1_2    ,   lowercase__					:	str = "linear"    ,   lowercase__					:	Optional[Union[np.ndarray, List[float]]] = None    ,   lowercase__					:	str = "epsilon"    ,   lowercase__					:	Optional[bool] = False    ,   lowercase__					:	Optional[bool] = False    ,   lowercase__					:	float = 1.0    ,   lowercase__					:	str = "linspace"    ,   lowercase__					:	int = 0    ,   ):
						if trained_betas is not None:
								__lowercase				:		Optional[int]												=				torch.tensor(lowercase__    ,   dtype=torch.floataa						)
						elif beta_schedule == "linear":
								__lowercase				:		List[Any]												=				torch.linspace(lowercase__    ,   lowercase__    ,   lowercase__    ,   dtype=torch.floataa						)
						elif beta_schedule == "scaled_linear":
								# this schedule is very specific to the latent diffusion model.
								__lowercase				:		Tuple												=				(
								    torch.linspace(beta_start**0.5    ,   beta_end**0.5    ,   lowercase__    ,   dtype=torch.floataa						) ** 2
								)
						elif beta_schedule == "squaredcos_cap_v2":
								# Glide cosine schedule
								__lowercase				:		Optional[int]												=				betas_for_alpha_bar(lowercase__    ,   alpha_transform_type="cosine"						)
						elif beta_schedule == "exp":
								__lowercase				:		List[Any]												=				betas_for_alpha_bar(lowercase__    ,   alpha_transform_type="exp"						)
						else:
								raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'						)

						__lowercase				:		Tuple												=				1.0 - self.betas
						__lowercase				:		List[Any]												=				torch.cumprod(self.alphas    ,   dim=0						)

						#  set all values
						self.set_timesteps(lowercase__    ,   lowercase__    ,   lowercase__						)
						__lowercase				:		Any												=				use_karras_sigmas



				def       snake_case    (     self					:	Tuple    ,   lowercase__					:	List[str]    ,   lowercase__					:	Optional[int]=None						):
						if schedule_timesteps is None:
								__lowercase				:		Dict												=				self.timesteps

						__lowercase				:		Optional[Any]												=				(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:
								__lowercase				:		List[str]												=				1 if len(lowercase__						) > 1 else 0
						else:
								__lowercase				:		str												=				timestep.cpu().item() if torch.is_tensor(lowercase__						) else timestep
								__lowercase				:		str												=				self._index_counter[timestep_int]

						return indices[pos].item()



				@property
				def       snake_case    (     self					:	List[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       snake_case    (     self					:	Optional[Any]    ,   lowercase__					:	torch.FloatTensor    ,   lowercase__					:	Union[float, torch.FloatTensor]    ,   ):
						__lowercase				:		List[Any]												=				self.index_for_timestep(lowercase__						)

						__lowercase				:		List[Any]												=				self.sigmas[step_index]
						__lowercase				:		List[Any]												=				sample / ((sigma**2 + 1) ** 0.5)
						return sample



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

						__lowercase				:		Dict												=				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":
								__lowercase				:		Dict												=				np.linspace(0    ,   num_train_timesteps - 1    ,   lowercase__    ,   dtype=lowercase__						)[::-1].copy()
						elif self.config.timestep_spacing == "leading":
								__lowercase				:		Optional[int]												=				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
								__lowercase				:		Any												=				(np.arange(0    ,   lowercase__						) * step_ratio).round()[::-1].copy().astype(lowercase__						)
								timesteps += self.config.steps_offset
						elif self.config.timestep_spacing == "trailing":
								__lowercase				:		Optional[Any]												=				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
								__lowercase				:		Any												=				(np.arange(lowercase__    ,   0    ,   -step_ratio						)).round().copy().astype(lowercase__						)
								timesteps -= 1
						else:
								raise ValueError(
								    f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'						)

						__lowercase				:		str												=				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
						__lowercase				:		int												=				np.log(lowercase__						)
						__lowercase				:		List[str]												=				np.interp(lowercase__    ,   np.arange(0    ,   len(lowercase__						)						)    ,   lowercase__						)

						if self.config.use_karras_sigmas:
								__lowercase				:		str												=				self._convert_to_karras(in_sigmas=lowercase__    ,   num_inference_steps=self.num_inference_steps						)
								__lowercase				:		Optional[int]												=				np.array([self._sigma_to_t(lowercase__    ,   lowercase__						) for sigma in sigmas]						)

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

						__lowercase				:		Tuple												=				torch.from_numpy(lowercase__						)
						__lowercase				:		Any												=				torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2						)]						)

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

						# empty dt and derivative
						__lowercase				:		List[str]												=				None
						__lowercase				:		Optional[int]												=				None

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



				def       snake_case    (     self					:	List[str]    ,   lowercase__					:	Union[str, Any]    ,   lowercase__					:	Union[str, Any]						):
						# get log sigma
						__lowercase				:		Tuple												=				np.log(lowercase__						)

						# get distribution
						__lowercase				:		Union[str, Any]												=				log_sigma - log_sigmas[:, np.newaxis]

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

						__lowercase				:		Tuple												=				log_sigmas[low_idx]
						__lowercase				:		int												=				log_sigmas[high_idx]

						# interpolate sigmas
						__lowercase				:		int												=				(low - log_sigma) / (low - high)
						__lowercase				:		Union[str, Any]												=				np.clip(lowercase__    ,   0    ,   1						)

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



				def       snake_case    (     self					:	Dict    ,   lowercase__					:	torch.FloatTensor    ,   lowercase__					:	List[Any]						):
						__lowercase				:		float												=				in_sigmas[-1].item()
						__lowercase				:		float												=				in_sigmas[0].item()

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



				@property
				def       snake_case    (     self					:	int						):
						return self.dt is None



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

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

						if self.state_in_first_order:
								__lowercase				:		List[Any]												=				self.sigmas[step_index]
								__lowercase				:		int												=				self.sigmas[step_index + 1]
						else:
								# 2nd order / Heun's method
								__lowercase				:		List[Any]												=				self.sigmas[step_index - 1]
								__lowercase				:		str												=				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
						__lowercase				:		Dict												=				0
						__lowercase				:		str												=				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":
								__lowercase				:		List[str]												=				sigma_hat if self.state_in_first_order else sigma_next
								__lowercase				:		List[Any]												=				sample - sigma_input * model_output
						elif self.config.prediction_type == "v_prediction":
								__lowercase				:		Optional[int]												=				sigma_hat if self.state_in_first_order else sigma_next
								__lowercase				:		Any												=				model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
								    sample / (sigma_input**2 + 1)
								)
						elif self.config.prediction_type == "sample":
								__lowercase				:		Optional[int]												=				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:
								__lowercase				:		Any												=				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
								__lowercase				:		Union[str, Any]												=				(sample - pred_original_sample) / sigma_hat
								# 3. delta timestep
								__lowercase				:		Optional[Any]												=				sigma_next - sigma_hat

								# store for 2nd order step
								__lowercase				:		List[Any]												=				derivative
								__lowercase				:		str												=				dt
								__lowercase				:		Optional[Any]												=				sample
						else:
								# 2. 2nd order / Heun's method
								__lowercase				:		List[Any]												=				(sample - pred_original_sample) / sigma_next
								__lowercase				:		str												=				(self.prev_derivative + derivative) / 2

								# 3. take prev timestep & sample
								__lowercase				:		str												=				self.dt
								__lowercase				:		Optional[int]												=				self.sample

								# free dt and derivative
								# Note, this puts the scheduler in "first order mode"
								__lowercase				:		List[str]												=				None
								__lowercase				:		Tuple												=				None
								__lowercase				:		int												=				None

						__lowercase				:		str												=				sample + derivative * dt

						if not return_dict:
								return (prev_sample,)

						return SchedulerOutput(prev_sample=lowercase__						)



				def       snake_case    (     self					:	str    ,   lowercase__					:	torch.FloatTensor    ,   lowercase__					:	torch.FloatTensor    ,   lowercase__					:	torch.FloatTensor    ,   ):
						# Make sure sigmas and timesteps have the same device and dtype as original_samples
						__lowercase				:		List[str]												=				self.sigmas.to(device=original_samples.device    ,   dtype=original_samples.dtype						)
						if original_samples.device.type == "mps" and torch.is_floating_point(lowercase__						):
								# mps does not support float64
								__lowercase				:		Optional[Any]												=				self.timesteps.to(original_samples.device    ,   dtype=torch.floataa						)
								__lowercase				:		List[str]												=				timesteps.to(original_samples.device    ,   dtype=torch.floataa						)
						else:
								__lowercase				:		List[Any]												=				self.timesteps.to(original_samples.device						)
								__lowercase				:		Tuple												=				timesteps.to(original_samples.device						)

						__lowercase				:		Any												=				[self.index_for_timestep(lowercase__    ,   lowercase__						) for t in timesteps]

						__lowercase				:		Dict												=				sigmas[step_indices].flatten()
						while len(sigma.shape						) < len(original_samples.shape						):
								__lowercase				:		List[str]												=				sigma.unsqueeze(-1						)

						__lowercase				:		str												=				original_samples + noise * sigma
						return noisy_samples



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






"""simple docstring"""







def 						snake_case__       (					_lowerCamelCase				)			->list:






		"""simple docstring"""


		__lowercase				:		Optional[int]												=				[0] * len(_lowerCamelCase				)

		for i in range(1,  len(_lowerCamelCase				)				):
				# use last results for better performance - dynamic programming
				__lowercase				:		Optional[Any]												=				prefix_result[i - 1]
				while j > 0 and input_string[i] != input_string[j]:
						__lowercase				:		List[str]												=				prefix_result[j - 1]

				if input_string[i] == input_string[j]:
						j += 1
				__lowercase				:		Any												=				j

		return prefix_result

def 						snake_case__       (					_lowerCamelCase				)			->int:






		"""simple docstring"""


		return max(prefix_function(_lowerCamelCase				)				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
