
'''simple docstring'''







from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left






@flax.struct.dataclass
class 					lowerCAmelCase__  :
					# setable values
					lowerCAmelCase_	  =	None
					lowerCAmelCase_	  =	None
					lowerCAmelCase_	  =	None






					@classmethod
					def       _snake_case       (  cls						):


											"""simple docstring"""





											return cls()







@dataclass
class 					lowerCAmelCase__  (    lowerCamelCase_	):
					lowerCAmelCase_	  =	42
					lowerCAmelCase_	  =	42
					lowerCAmelCase_	  =	None







class 					lowerCAmelCase__  (    lowerCamelCase_       ,		lowerCamelCase_	):






					@property
					def       _snake_case       (  self						):


											"""simple docstring"""





											return True






					@register_to_config
					def __init__(  self							,				__SCREAMING_SNAKE_CASE = 20_00							,				__SCREAMING_SNAKE_CASE = 0.15							,				__SCREAMING_SNAKE_CASE = 0.01							,				__SCREAMING_SNAKE_CASE = 1_348.0							,				__SCREAMING_SNAKE_CASE = 1E-5							,				__SCREAMING_SNAKE_CASE = 1							,				):


											"""simple docstring"""





											pass






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			ScoreSdeVeSchedulerState.create()
											return self.set_sigmas(
											    __SCREAMING_SNAKE_CASE							,				self.config.num_train_timesteps							,				self.config.sigma_min							,				self.config.sigma_max							,				self.config.sampling_eps							,				)






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE = ()							,				__SCREAMING_SNAKE_CASE = None						):


											"""simple docstring"""





											lowercase_      :	Optional[Any]						=			sampling_eps if sampling_eps is not None else self.config.sampling_eps

											lowercase_      :	List[Any]						=			jnp.linspace(1							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)
											return state.replace(timesteps=__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE = None							,				__SCREAMING_SNAKE_CASE = None							,				__SCREAMING_SNAKE_CASE = None							,				):


											"""simple docstring"""





											lowercase_      :	str						=			sigma_min if sigma_min is not None else self.config.sigma_min
											lowercase_      :	Any						=			sigma_max if sigma_max is not None else self.config.sigma_max
											lowercase_      :	List[Any]						=			sampling_eps if sampling_eps is not None else self.config.sampling_eps
											if state.timesteps is None:
																	lowercase_      :	List[Any]						=			self.set_timesteps(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)

											lowercase_      :	Tuple						=			jnp.exp(jnp.linspace(jnp.log(__SCREAMING_SNAKE_CASE						)							,				jnp.log(__SCREAMING_SNAKE_CASE						)							,				__SCREAMING_SNAKE_CASE						)						)
											lowercase_      :	Optional[Any]						=			jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]						)

											return state.replace(discrete_sigmas=__SCREAMING_SNAKE_CASE							,				sigmas=__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											return jnp.where(timesteps == 0							,				jnp.zeros_like(__SCREAMING_SNAKE_CASE						)							,				state.discrete_sigmas[timesteps - 1]						)






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE = True							,				):


											"""simple docstring"""





											if state.timesteps is None:
																	raise ValueError(
																	    '''`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''						)

											lowercase_      :	List[Any]						=			timestep * jnp.ones(
											    sample.shape[0]							,				)
											lowercase_      :	Union[str, Any]						=			(timestep * (len(state.timesteps						) - 1)).long()

											lowercase_      :	int						=			state.discrete_sigmas[timesteps]
											lowercase_      :	Union[str, Any]						=			self.get_adjacent_sigma(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)
											lowercase_      :	List[str]						=			jnp.zeros_like(__SCREAMING_SNAKE_CASE						)
											lowercase_      :	Any						=			(sigma**2 - adjacent_sigma**2) ** 0.5

											# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
											# also equation 47 shows the analog from SDE models to ancestral sampling methods
											lowercase_      :	Dict						=			diffusion.flatten()
											lowercase_      :	Optional[Any]						=			broadcast_to_shape_from_left(__SCREAMING_SNAKE_CASE							,				sample.shape						)
											lowercase_      :	List[Any]						=			drift - diffusion**2 * model_output

											#  equation 6: sample noise for the diffusion term of
											lowercase_      :	List[str]						=			random.split(__SCREAMING_SNAKE_CASE							,				num=1						)
											lowercase_      :	Any						=			random.normal(key=__SCREAMING_SNAKE_CASE							,				shape=sample.shape						)
											lowercase_      :	Tuple						=			sample - drift  # subtract because `dt` is a small negative timestep
											# TODO is the variable diffusion the correct scaling term for the noise?
											lowercase_      :	Optional[int]						=			prev_sample_mean + diffusion * noise  # add impact of diffusion field g

											if not return_dict:
																	return (prev_sample, prev_sample_mean, state)

											return FlaxSdeVeOutput(prev_sample=__SCREAMING_SNAKE_CASE							,				prev_sample_mean=__SCREAMING_SNAKE_CASE							,				state=__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE = True							,				):


											"""simple docstring"""





											if state.timesteps is None:
																	raise ValueError(
																	    '''`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''						)

											# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
											# sample noise for correction
											lowercase_      :	str						=			random.split(__SCREAMING_SNAKE_CASE							,				num=1						)
											lowercase_      :	int						=			random.normal(key=__SCREAMING_SNAKE_CASE							,				shape=sample.shape						)

											# compute step size from the model_output, the noise, and the snr
											lowercase_      :	Tuple						=			jnp.linalg.norm(__SCREAMING_SNAKE_CASE						)
											lowercase_      :	List[Any]						=			jnp.linalg.norm(__SCREAMING_SNAKE_CASE						)
											lowercase_      :	str						=			(self.config.snr * noise_norm / grad_norm) ** 2 * 2
											lowercase_      :	Any						=			step_size * jnp.ones(sample.shape[0]						)

											# compute corrected sample: model_output term and noise term
											lowercase_      :	Optional[int]						=			step_size.flatten()
											lowercase_      :	List[str]						=			broadcast_to_shape_from_left(__SCREAMING_SNAKE_CASE							,				sample.shape						)
											lowercase_      :	Optional[Any]						=			sample + step_size * model_output
											lowercase_      :	Dict						=			prev_sample_mean + ((step_size * 2) ** 0.5) * noise

											if not return_dict:
																	return (prev_sample, state)

											return FlaxSdeVeOutput(prev_sample=__SCREAMING_SNAKE_CASE							,				state=__SCREAMING_SNAKE_CASE						)






					def __len__(  self						):


											"""simple docstring"""





											return self.config.num_train_timesteps








"""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 ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__=0.9_99					,   lowerCamelCase__="cosine"					,   )			->				int:
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(lowerCamelCase__      ):
																					return math.cos((t + 0.0_08) / 1.0_08 * 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}"""      )

							A    =       []
							for i in range(lowerCamelCase__      ):
														A    =       i / num_diffusion_timesteps
														A    =       (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   UpperCAmelCase__		(   UpperCamelCase ,UpperCamelCase  ):
		lowerCAmelCase_							:						int							=  [e.name for e in KarrasDiffusionSchedulers]
		lowerCAmelCase_							:						Union[str, Any]							=  2



		@register_to_config
		def __init__(			self	:     Any   ,				snake_case	:     int = 1_000   ,				snake_case	:     float = 0.00085   ,				snake_case	:     float = 0.012   ,				snake_case	:     str = "linear"   ,				snake_case	:     Optional[Union[np.ndarray, List[float]]] = None   ,				snake_case	:     str = "epsilon"   ,				snake_case	:     str = "linspace"   ,				snake_case	:     int = 0   ,				)      ->    Dict:






									'''simple docstring'''

									if trained_betas is not None:
																A    =       torch.tensor(snake_case   ,				dtype=torch.floataa					)
									elif beta_schedule == "linear":
																A    =       torch.linspace(snake_case   ,				snake_case   ,				snake_case   ,				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   ,				snake_case   ,				dtype=torch.floataa					) ** 2
																)
									elif beta_schedule == "squaredcos_cap_v2":
																# Glide cosine schedule
																A    =       betas_for_alpha_bar(snake_case					)
									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(snake_case   ,				snake_case   ,				snake_case					)



		def        A_		(			self	:     str   ,				snake_case	:     List[Any]   ,				snake_case	:     str=None					)      ->    Optional[Any]:






									'''simple docstring'''

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

									return indices[pos].item()



		@property
		def        A_		(			self	:     Optional[int]					)      ->    Union[str, Any]:






									'''simple docstring'''

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

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



		def        A_		(			self	:     Union[str, Any]   ,				snake_case	:     torch.FloatTensor   ,				snake_case	:     Union[float, torch.FloatTensor]   ,				)      ->    torch.FloatTensor:






									'''simple docstring'''

									A    =       self.index_for_timestep(snake_case					)

									if self.state_in_first_order:
																A    =       self.sigmas[step_index]
									else:
																A    =       self.sigmas_interpol[step_index - 1]

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



		def        A_		(			self	:     Union[str, Any]   ,				snake_case	:     int   ,				snake_case	:     Union[str, torch.device] = None   ,				snake_case	:     Optional[int] = None   ,				)      ->    Any:






									'''simple docstring'''

									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   ,				snake_case   ,				dtype=snake_case					)[::-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   ,				snake_case					) * step_ratio).round()[::-1].copy().astype(snake_case					)
																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(snake_case   ,				0   ,				-step_ratio					)).round().copy().astype(snake_case					)
																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(snake_case					)					).to(snake_case					)

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

									# compute up and down sigmas
									A    =       sigmas.roll(-1					)
									A    =       0.0
									A    =       (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
									A    =       (sigmas_next**2 - sigmas_up**2) ** 0.5
									A    =       0.0

									# compute interpolated sigmas
									A    =       sigmas.log().lerp(sigmas_down.log()   ,				0.5					).exp()
									A    =       0.0

									# set sigmas
									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:]]					)
									A    =       torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2					), sigmas_up[-1:]]					)
									A    =       torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2					), sigmas_down[-1:]]					)

									if str(snake_case					).startswith('mps'					):
																# mps does not support float64
																A    =       torch.from_numpy(snake_case					).to(snake_case   ,				dtype=torch.floataa					)
									else:
																A    =       torch.from_numpy(snake_case					).to(snake_case					)

									A    =       self.sigma_to_t(snake_case					).to(snake_case   ,				dtype=timesteps.dtype					)
									A    =       torch.stack((timesteps_interpol[:-2, 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(snake_case					)



		def        A_		(			self	:     Dict   ,				snake_case	:     Dict					)      ->    Tuple:






									'''simple docstring'''

									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        A_		(			self	:     Any					)      ->    int:






									'''simple docstring'''

									return self.sample is None



		def        A_		(			self	:     Tuple   ,				snake_case	:     Union[torch.FloatTensor, np.ndarray]   ,				snake_case	:     Union[float, torch.FloatTensor]   ,				snake_case	:     Union[torch.FloatTensor, np.ndarray]   ,				snake_case	:     Optional[torch.Generator] = None   ,				snake_case	:     bool = True   ,				)      ->    Union[SchedulerOutput, Tuple]:






									'''simple docstring'''

									A    =       self.index_for_timestep(snake_case					)

									# advance index counter by 1
									A    =       timestep.cpu().item() if torch.is_tensor(snake_case					) 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]
																A    =       self.sigmas_up[step_index]
																A    =       self.sigmas_down[step_index - 1]
									else:
																# 2nd order / KPDM2's method
																A    =       self.sigmas[step_index - 1]
																A    =       self.sigmas_interpol[step_index - 1]
																A    =       self.sigmas_up[step_index - 1]
																A    =       self.sigmas_down[step_index - 1]

									# 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

									A    =       model_output.device
									A    =       randn_tensor(model_output.shape   ,				dtype=model_output.dtype   ,				device=snake_case   ,				generator=snake_case					)

									# 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
																A    =       dt
																A    =       sample + derivative * dt
									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_down - sigma_hat

																A    =       self.sample
																A    =       None

																A    =       sample + derivative * dt
																A    =       prev_sample + noise * sigma_up

									if not return_dict:
																return (prev_sample,)

									return SchedulerOutput(prev_sample=snake_case					)



		def        A_		(			self	:     Dict   ,				snake_case	:     torch.FloatTensor   ,				snake_case	:     torch.FloatTensor   ,				snake_case	:     torch.FloatTensor   ,				)      ->    torch.FloatTensor:






									'''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(snake_case					):
																# 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(snake_case   ,				snake_case					) 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	:     List[Any]					)      ->    int:






									'''simple docstring'''

									return self.config.num_train_timesteps



