



"""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       _lowerCamelCase				(							UpperCAmelCase_ :       int,	UpperCAmelCase_ :       Optional[Any]=0.999,	UpperCAmelCase_ :       Any="cosine",	)	-> Tuple:






	"""simple docstring"""





	if alpha_transform_type == "cosine":

		def alpha_bar_fn(UpperCAmelCase_ :       List[str]     ):
			return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(UpperCAmelCase_ :       Optional[int]     ):
			return math.exp(t * -12.0     )

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

	A__											=					[]
	for i in range(UpperCAmelCase_     ):
		A__											=					i / num_diffusion_timesteps
		A__											=					(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       UpperCamelCase__			(   _lowerCAmelCase       ,		_lowerCAmelCase      ):

			"""simple docstring"""


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


			@register_to_config
			def __init__(      self						,	SCREAMING_SNAKE_CASE__ = 1000						,	SCREAMING_SNAKE_CASE__ = 0.0_0_0_8_5						,	SCREAMING_SNAKE_CASE__ = 0.0_1_2						,	SCREAMING_SNAKE_CASE__ = "linear"						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = "epsilon"						,	SCREAMING_SNAKE_CASE__ = "linspace"						,	SCREAMING_SNAKE_CASE__ = 0						,	)      ->	str:
				if trained_betas is not None:
					A__											=					torch.tensor(a_						,	dtype=torch.floataa		)
				elif beta_schedule == "linear":
					A__											=					torch.linspace(a_						,	a_						,	a_						,	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						,	a_						,	dtype=torch.floataa		) ** 2
					)
				elif beta_schedule == "squaredcos_cap_v2":
					# Glide cosine schedule
					A__											=					betas_for_alpha_bar(a_		)
				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		)

				#  set all values
				self.set_timesteps(a_						,	a_						,	a_		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=None		)      ->	List[Any]:
				if schedule_timesteps is None:
					A__											=					self.timesteps

				A__											=					(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:
					A__											=					1 if len(a_		) > 1 else 0
				else:
					A__											=					timestep.cpu().item() if torch.is_tensor(a_		) else timestep
					A__											=					self._index_counter[timestep_int]

				return indices[pos].item()


			@property
			def   snake_case__	(      self		)      ->	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						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	)      ->	torch.FloatTensor:
				A__											=					self.index_for_timestep(a_		)

				if self.state_in_first_order:
					A__											=					self.sigmas[step_index]
				else:
					A__											=					self.sigmas_interpol[step_index]

				A__											=					sample / ((sigma**2 + 1) ** 0.5)
				return sample


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	)      ->	List[str]:
				A__											=					num_inference_steps

				A__											=					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":
					A__											=					np.linspace(0						,	num_train_timesteps - 1						,	a_						,	dtype=a_		)[::-1].copy()
				elif self.config.timestep_spacing == "leading":
					A__											=					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						,	a_		) * step_ratio).round()[::-1].copy().astype(a_		)
					timesteps += self.config.steps_offset
				elif self.config.timestep_spacing == "trailing":
					A__											=					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(a_						,	0						,	-step_ratio		)).round().copy().astype(a_		)
					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__											=					torch.from_numpy(np.log(a_		)		).to(a_		)

				A__											=					np.interp(a_						,	np.arange(0						,	len(a_		)		)						,	a_		)
				A__											=					np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)
				A__											=					torch.from_numpy(a_		).to(device=a_		)

				# interpolate sigmas
				A__											=					sigmas.log().lerp(sigmas.roll(1		).log()						,	0.5		).exp()

				A__											=					torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2		), sigmas[-1:]]		)
				A__											=					torch.cat(
				    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2		), sigmas_interpol[-1:]]		)

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

				# interpolate timesteps
				A__											=					self.sigma_to_t(a_		).to(a_						,	dtype=timesteps.dtype		)
				A__											=					torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])						,	dim=-1		).flatten()

				A__											=					torch.cat([timesteps[:1], interleaved_timesteps]		)

				A__											=					None

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


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	List[str]:
				# get log sigma
				A__											=					sigma.log()

				# get distribution
				A__											=					log_sigma - self.log_sigmas[:, None]

				# get sigmas range
				A__											=					dists.ge(0		).cumsum(dim=0		).argmax(dim=0		).clamp(max=self.log_sigmas.shape[0] - 2		)
				A__											=					low_idx + 1

				A__											=					self.log_sigmas[low_idx]
				A__											=					self.log_sigmas[high_idx]

				# interpolate sigmas
				A__											=					(low - log_sigma) / (low - high)
				A__											=					w.clamp(0						,	1		)

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


			@property
			def   snake_case__	(      self		)      ->	Any:
				return self.sample is None


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = True						,	)      ->	Union[SchedulerOutput, Tuple]:
				A__											=					self.index_for_timestep(a_		)

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

				if self.state_in_first_order:
					A__											=					self.sigmas[step_index]
					A__											=					self.sigmas_interpol[step_index + 1]
					A__											=					self.sigmas[step_index + 1]
				else:
					# 2nd order / KDPM2's method
					A__											=					self.sigmas[step_index - 1]
					A__											=					self.sigmas_interpol[step_index]
					A__											=					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
				A__											=					0
				A__											=					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":
					A__											=					sigma_hat if self.state_in_first_order else sigma_interpol
					A__											=					sample - sigma_input * model_output
				elif self.config.prediction_type == "v_prediction":
					A__											=					sigma_hat if self.state_in_first_order else sigma_interpol
					A__											=					model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
					    sample / (sigma_input**2 + 1)
					)
				elif self.config.prediction_type == "sample":
					raise NotImplementedError("prediction_type not implemented yet: sample"		)
				else:
					raise ValueError(
					    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""		)

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

					# store for 2nd order step
					A__											=					sample
				else:
					# DPM-Solver-2
					# 2. Convert to an ODE derivative for 2nd order
					A__											=					(sample - pred_original_sample) / sigma_interpol

					# 3. delta timestep
					A__											=					sigma_next - sigma_hat

					A__											=					self.sample
					A__											=					None

				A__											=					sample + derivative * dt

				if not return_dict:
					return (prev_sample,)

				return SchedulerOutput(prev_sample=a_		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	)      ->	torch.FloatTensor:
				# Make sure sigmas and timesteps have the same device and dtype as original_samples
				A__											=					self.sigmas.to(device=original_samples.device						,	dtype=original_samples.dtype		)
				if original_samples.device.type == "mps" and torch.is_floating_point(a_		):
					# 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__											=					[self.index_for_timestep(a_						,	a_		) 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		)      ->	str:
				return self.config.num_train_timesteps








'''simple docstring'''
import baseaa




def 	_A  (       A							)  ->				bytes:
	return baseaa.aaaencode(string.encode("utf-8"							)							)




def 	_A  (       A							)  ->				str:
	return baseaa.aaadecode(A							).decode("utf-8"							)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


