


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__:     Optional[Any]							,					UpperCamelCase__:     List[Any]=0.9_99							,					UpperCamelCase__:     Tuple="cosine"							,					) ->   Any:





	"""simple docstring"""







	if alpha_transform_type == "cosine":

		def alpha_bar_fn(UpperCamelCase__:     Any						):
			return math.cos((t + 0.0_08) / 1.0_08 * 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    (					__snake_case    ,		__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= [e.name for e in KarrasDiffusionSchedulers]
				lowerCAmelCase 		= 2

				@register_to_config
				def __init__(						self				,   a__ = 1000				,   a__ = 0.0_00_85				,   a__ = 0.0_12				,   a__ = "linear"				,   a__ = None				,   a__ = "epsilon"				,   a__ = False				,   a__ = False				,   a__ = 1.0				,   a__ = "linspace"				,   a__ = 0				,   )	->				List[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__				,   alpha_transform_type="""cosine"""   )
					elif beta_schedule == "exp":
						A								=				betas_for_alpha_bar(a__				,   alpha_transform_type="""exp"""   )
					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__   )
					A								=				use_karras_sigmas

				def      _UpperCAmelCase					(						self				,   a__				,   a__=None   )	->				int:

					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      _UpperCAmelCase					(						self   )	->				Any:

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

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

				def      _UpperCAmelCase					(						self				,   a__				,   a__				,   )	->				Dict:
					A								=				self.index_for_timestep(a__   )

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

				def      _UpperCAmelCase					(						self				,   a__				,   a__ = None				,   a__ = None				,   )	->				Tuple:
					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								=				np.log(a__   )
					A								=				np.interp(a__				,   np.arange(0				,   len(a__   )   )				,   a__   )

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

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

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

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

					# empty dt and derivative
					A								=				None
					A								=				None

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

				def      _UpperCAmelCase					(						self				,   a__				,   a__   )	->				Dict:
					A								=				np.log(a__   )

					# 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(a__				,   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				,   a__				,   a__   )	->				str:
					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				,   a__   )
					A								=				sigma_min ** (1 / rho)
					A								=				sigma_max ** (1 / rho)
					A								=				(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
					return sigmas

				@property
				def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
					return self.dt is None

				def      _UpperCAmelCase					(						self				,   a__				,   a__				,   a__				,   a__ = True				,   )	->				Dict:
					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[step_index + 1]
					else:
						# 2nd order / Heun's method
						A								=				self.sigmas[step_index - 1]
						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_next
						A								=				sample - sigma_input * model_output
					elif self.config.prediction_type == "v_prediction":
						A								=				sigma_hat if self.state_in_first_order else sigma_next
						A								=				model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
						    sample / (sigma_input**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`'   )

					if self.config.clip_sample:
						A								=				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
						A								=				(sample - pred_original_sample) / sigma_hat
						# 3. delta timestep
						A								=				sigma_next - sigma_hat

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

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

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

					A								=				sample + derivative * dt

					if not return_dict:
						return (prev_sample,)

					return SchedulerOutput(prev_sample=a__   )

				def      _UpperCAmelCase					(						self				,   a__				,   a__				,   a__				,   )	->				Dict:
					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   )	->				Optional[int]:
					return self.config.num_train_timesteps







def   _lowerCAmelCase				(   UpperCamelCase__:     str						) ->   str:





	"""simple docstring"""







	return " ".join(input_str.split()[::-1]						)


if __name__ == "__main__":
				import doctest

				doctest.testmod()



