




import unittest
import warnings

from datasets.utils import experimental


@experimental
def 						__SCREAMING_SNAKE_CASE      ():
						return "success"





class 			snake_case_     (	unittest.TestCase      ):







			'''simple docstring'''

			def        snake_case__(  self				:       Any							)   ->Any:
									with warnings.catch_warnings(record=_UpperCamelCase							) as w:
															warnings.simplefilter('''always'''							)
															self.assertEqual(dummy_function()							,     '''success'''							)
									self.assertEqual(len(_UpperCamelCase							)							,     1							)


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 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=0.999						,	SCREAMING_SNAKE_CASE__="cosine"						,	):
						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

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

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

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





class 			snake_case_     (	__A			, __A      ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     Tuple		    =       [e.name for e in KarrasDiffusionSchedulers]
			SCREAMING_SNAKE_CASE  :     Optional[Any]		    =       2







			@register_to_config
			def __init__(  self				:       Dict							,     _UpperCamelCase				:       int = 1_0_0_0							,     _UpperCamelCase				:       float = 0.00085							,     _UpperCamelCase				:       float = 0.012							,     _UpperCamelCase				:       str = "linear"							,     _UpperCamelCase				:       Optional[Union[np.ndarray, List[float]]] = None							,     _UpperCamelCase				:       str = "epsilon"							,     _UpperCamelCase				:       str = "linspace"							,     _UpperCamelCase				:       int = 0							,     )   ->Any:
									if trained_betas is not None:
															snake_case_     =			torch.tensor(_UpperCamelCase							,     dtype=torch.floataa							)
									elif beta_schedule == "linear":
															snake_case_     =			torch.linspace(_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     dtype=torch.floataa							)
									elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															snake_case_     =			(
															    torch.linspace(beta_start**0.5							,     beta_end**0.5							,     _UpperCamelCase							,     dtype=torch.floataa							) ** 2
															)
									elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															snake_case_     =			betas_for_alpha_bar(_UpperCamelCase							)
									else:
															raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''							)

									snake_case_     =			1.0 - self.betas
									snake_case_     =			torch.cumprod(self.alphas							,     dim=0							)

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







			def        snake_case__(  self				:       Optional[int]							,     _UpperCamelCase				:       Tuple							,     _UpperCamelCase				:       Dict=None							)   ->Dict:
									if schedule_timesteps is None:
															snake_case_     =			self.timesteps

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

									return indices[pos].item()







			@property
			def        snake_case__(  self				:       int							)   ->Optional[int]:
									# 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[int]							,     _UpperCamelCase				:       torch.FloatTensor							,     _UpperCamelCase				:       Union[float, torch.FloatTensor]							,     )   ->torch.FloatTensor:
									snake_case_     =			self.index_for_timestep(_UpperCamelCase							)

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

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







			def        snake_case__(  self				:       Optional[Any]							,     _UpperCamelCase				:       int							,     _UpperCamelCase				:       Union[str, torch.device] = None							,     _UpperCamelCase				:       Optional[int] = None							,     )   ->str:
									snake_case_     =			num_inference_steps

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

									snake_case_     =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
									snake_case_     =			torch.from_numpy(np.log(_UpperCamelCase							)							).to(_UpperCamelCase							)

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

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

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

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

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

									snake_case_     =			self.sigma_to_t(_UpperCamelCase							).to(_UpperCamelCase							,     dtype=timesteps.dtype							)
									snake_case_     =			torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])							,     dim=-1							).flatten()

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

									snake_case_     =			None

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







			def        snake_case__(  self				:       int							,     _UpperCamelCase				:       Any							)   ->Optional[Any]:
									# get log sigma
									snake_case_     =			sigma.log()

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

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

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

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

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







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







			def        snake_case__(  self				:       Optional[Any]							,     _UpperCamelCase				:       Union[torch.FloatTensor, np.ndarray]							,     _UpperCamelCase				:       Union[float, torch.FloatTensor]							,     _UpperCamelCase				:       Union[torch.FloatTensor, np.ndarray]							,     _UpperCamelCase				:       Optional[torch.Generator] = None							,     _UpperCamelCase				:       bool = True							,     )   ->Union[SchedulerOutput, Tuple]:
									snake_case_     =			self.index_for_timestep(_UpperCamelCase							)

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

									if self.state_in_first_order:
															snake_case_     =			self.sigmas[step_index]
															snake_case_     =			self.sigmas_interpol[step_index]
															snake_case_     =			self.sigmas_up[step_index]
															snake_case_     =			self.sigmas_down[step_index - 1]
									else:
															# 2nd order / KPDM2's method
															snake_case_     =			self.sigmas[step_index - 1]
															snake_case_     =			self.sigmas_interpol[step_index - 1]
															snake_case_     =			self.sigmas_up[step_index - 1]
															snake_case_     =			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
									snake_case_     =			0
									snake_case_     =			sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

									snake_case_     =			model_output.device
									snake_case_     =			randn_tensor(model_output.shape							,     dtype=model_output.dtype							,     device=_UpperCamelCase							,     generator=_UpperCamelCase							)

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
															snake_case_     =			sigma_hat if self.state_in_first_order else sigma_interpol
															snake_case_     =			sample - sigma_input * model_output
									elif self.config.prediction_type == "v_prediction":
															snake_case_     =			sigma_hat if self.state_in_first_order else sigma_interpol
															snake_case_     =			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
															snake_case_     =			(sample - pred_original_sample) / sigma_hat
															# 3. delta timestep
															snake_case_     =			sigma_interpol - sigma_hat

															# store for 2nd order step
															snake_case_     =			sample
															snake_case_     =			dt
															snake_case_     =			sample + derivative * dt
									else:
															# DPM-Solver-2
															# 2. Convert to an ODE derivative for 2nd order
															snake_case_     =			(sample - pred_original_sample) / sigma_interpol
															# 3. delta timestep
															snake_case_     =			sigma_down - sigma_hat

															snake_case_     =			self.sample
															snake_case_     =			None

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

									if not return_dict:
															return (prev_sample,)

									return SchedulerOutput(prev_sample=_UpperCamelCase							)







			def        snake_case__(  self				:       List[Any]							,     _UpperCamelCase				:       torch.FloatTensor							,     _UpperCamelCase				:       torch.FloatTensor							,     _UpperCamelCase				:       torch.FloatTensor							,     )   ->torch.FloatTensor:
									# Make sure sigmas and timesteps have the same device and dtype as original_samples
									snake_case_     =			self.sigmas.to(device=original_samples.device							,     dtype=original_samples.dtype							)
									if original_samples.device.type == "mps" and torch.is_floating_point(_UpperCamelCase							):
															# mps does not support float64
															snake_case_     =			self.timesteps.to(original_samples.device							,     dtype=torch.floataa							)
															snake_case_     =			timesteps.to(original_samples.device							,     dtype=torch.floataa							)
									else:
															snake_case_     =			self.timesteps.to(original_samples.device							)
															snake_case_     =			timesteps.to(original_samples.device							)

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

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

									snake_case_     =			original_samples + noise * sigma
									return noisy_samples







			def __len__(  self				:       Tuple							)   ->List[str]:
									return self.config.num_train_timesteps