
"""simple docstring"""
import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest
class      lowerCAmelCase_						(							snake_case_				):



							'''simple docstring'''





							_snake_case		     =						(DDIMScheduler,)
							_snake_case		     =						(("""eta""", 0.0), ("""num_inference_steps""", 5_0))




							def  A__		(				self  ,							**snake_case_							)   ->       List[Any]:
													__lowerCAmelCase        =       {
													    "num_train_timesteps": 1_000,
													    "beta_start": 0.0_001,
													    "beta_end": 0.02,
													    "beta_schedule": "linear",
													    "clip_sample": True,
													}

													config.update(**snake_case_							)
													return config




							def  A__		(				self  ,							**snake_case_							)   ->       Dict:
													__lowerCAmelCase        =       self.scheduler_classes[0]
													__lowerCAmelCase        =       self.get_scheduler_config(**snake_case_							)
													__lowerCAmelCase        =       scheduler_class(**snake_case_							)

													__lowerCAmelCase        =       10, 0.0

													__lowerCAmelCase        =       self.dummy_model()
													__lowerCAmelCase        =       self.dummy_sample_deter

													scheduler.set_timesteps(snake_case_							)

													for t in scheduler.timesteps:
																			__lowerCAmelCase        =       model(snake_case_  ,							snake_case_							)
																			__lowerCAmelCase        =       scheduler.step(snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_							).prev_sample

													return sample




							def  A__		(				self							)   ->       Union[str, Any]:


													for timesteps in [100, 500, 1_000]:
																			self.check_over_configs(num_train_timesteps=snake_case_							)




							def  A__		(				self							)   ->       Dict:


													for steps_offset in [0, 1]:
																			self.check_over_configs(steps_offset=snake_case_							)

													__lowerCAmelCase        =       self.scheduler_classes[0]
													__lowerCAmelCase        =       self.get_scheduler_config(steps_offset=1							)
													__lowerCAmelCase        =       scheduler_class(**snake_case_							)
													scheduler.set_timesteps(5							)
													assert torch.equal(scheduler.timesteps  ,							torch.LongTensor([801, 601, 401, 201, 1]							)							)




							def  A__		(				self							)   ->       List[Any]:


													for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]  ,							[0.002, 0.02, 0.2, 2]							):
																			self.check_over_configs(beta_start=snake_case_  ,							beta_end=snake_case_							)




							def  A__		(				self							)   ->       Optional[int]:


													for schedule in ["linear", "squaredcos_cap_v2"]:
																			self.check_over_configs(beta_schedule=snake_case_							)




							def  A__		(				self							)   ->       Dict:


													for prediction_type in ["epsilon", "v_prediction"]:
																			self.check_over_configs(prediction_type=snake_case_							)




							def  A__		(				self							)   ->       str:


													for clip_sample in [True, False]:
																			self.check_over_configs(clip_sample=snake_case_							)




							def  A__		(				self							)   ->       Optional[Any]:


													for timestep_spacing in ["trailing", "leading"]:
																			self.check_over_configs(timestep_spacing=snake_case_							)




							def  A__		(				self							)   ->       Dict:


													for rescale_betas_zero_snr in [True, False]:
																			self.check_over_configs(rescale_betas_zero_snr=snake_case_							)




							def  A__		(				self							)   ->       int:
													self.check_over_configs(thresholding=snake_case_							)
													for threshold in [0.5, 1.0, 2.0]:
																			for prediction_type in ["epsilon", "v_prediction"]:
																									self.check_over_configs(
																									    thresholding=snake_case_  ,							prediction_type=snake_case_  ,							sample_max_value=snake_case_  ,							)




							def  A__		(				self							)   ->       Any:


													for t in [1, 10, 49]:
																			self.check_over_forward(time_step=snake_case_							)




							def  A__		(				self							)   ->       int:


													for t, num_inference_steps in zip([1, 10, 50]  ,							[10, 50, 500]							):
																			self.check_over_forward(time_step=snake_case_  ,							num_inference_steps=snake_case_							)




							def  A__		(				self							)   ->       Union[str, Any]:


													for t, eta in zip([1, 10, 49]  ,							[0.0, 0.5, 1.0]							):
																			self.check_over_forward(time_step=snake_case_  ,							eta=snake_case_							)




							def  A__		(				self							)   ->       Dict:
													__lowerCAmelCase        =       self.scheduler_classes[0]
													__lowerCAmelCase        =       self.get_scheduler_config()
													__lowerCAmelCase        =       scheduler_class(**snake_case_							)

													assert torch.sum(torch.abs(scheduler._get_variance(0  ,							0							) - 0.0							)							) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(420  ,							400							) - 0.14_771							)							) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(980  ,							960							) - 0.32_460							)							) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(0  ,							0							) - 0.0							)							) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(487  ,							486							) - 0.00_979							)							) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(999  ,							998							) - 0.02							)							) < 1e-5




							def  A__		(				self							)   ->       Any:
													__lowerCAmelCase        =       self.full_loop()

													__lowerCAmelCase        =       torch.sum(torch.abs(snake_case_							)							)
													__lowerCAmelCase        =       torch.mean(torch.abs(snake_case_							)							)

													assert abs(result_sum.item() - 172.0_067							) < 1e-2
													assert abs(result_mean.item() - 0.223_967							) < 1e-3




							def  A__		(				self							)   ->       List[str]:
													__lowerCAmelCase        =       self.full_loop(prediction_type="""v_prediction"""							)

													__lowerCAmelCase        =       torch.sum(torch.abs(snake_case_							)							)
													__lowerCAmelCase        =       torch.mean(torch.abs(snake_case_							)							)

													assert abs(result_sum.item() - 52.5_302							) < 1e-2
													assert abs(result_mean.item() - 0.0_684							) < 1e-3




							def  A__		(				self							)   ->       Optional[int]:
													__lowerCAmelCase        =       self.full_loop(set_alpha_to_one=snake_case_  ,							beta_start=0.01							)
													__lowerCAmelCase        =       torch.sum(torch.abs(snake_case_							)							)
													__lowerCAmelCase        =       torch.mean(torch.abs(snake_case_							)							)

													assert abs(result_sum.item() - 149.8_295							) < 1e-2
													assert abs(result_mean.item() - 0.1_951							) < 1e-3




							def  A__		(				self							)   ->       Any:
													__lowerCAmelCase        =       self.full_loop(set_alpha_to_one=snake_case_  ,							beta_start=0.01							)
													__lowerCAmelCase        =       torch.sum(torch.abs(snake_case_							)							)
													__lowerCAmelCase        =       torch.mean(torch.abs(snake_case_							)							)

													assert abs(result_sum.item() - 149.0_784							) < 1e-2
													assert abs(result_mean.item() - 0.1_941							) < 1e-3


"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


SCREAMING_SNAKE_CASE_			      =					logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							_snake_case		     =						42
class      lowerCAmelCase_						(							A__							,							A__				):



							'''simple docstring'''





							_snake_case		     =						1




							@register_to_config
							def __init__(				self  ,							snake_case_ = 40  ,							snake_case_ = 0.002  ,							snake_case_ = 80.0  ,							snake_case_ = 0.5  ,							snake_case_ = 1.0  ,							snake_case_ = 7.0  ,							snake_case_ = True  ,							)   ->       List[str]:
													# standard deviation of the initial noise distribution
													__lowerCAmelCase        =       sigma_max

													__lowerCAmelCase        =       np.linspace(0  ,							1  ,							snake_case_							)
													__lowerCAmelCase        =       self._convert_to_karras(snake_case_							)
													__lowerCAmelCase        =       self.sigma_to_t(snake_case_							)

													# setable values
													__lowerCAmelCase        =       None
													__lowerCAmelCase        =       torch.from_numpy(snake_case_							)
													__lowerCAmelCase        =       torch.from_numpy(snake_case_							)
													__lowerCAmelCase        =       False
													__lowerCAmelCase        =       False




							def  A__		(				self  ,							snake_case_  ,							snake_case_=None							)   ->       str:
													if schedule_timesteps is None:
																			__lowerCAmelCase        =       self.timesteps

													__lowerCAmelCase        =       (schedule_timesteps == timestep).nonzero()
													return indices.item()




							def  A__		(				self  ,							snake_case_  ,							snake_case_							)   ->       torch.FloatTensor:
													# Get sigma corresponding to timestep
													if isinstance(snake_case_  ,							torch.Tensor							):
																			__lowerCAmelCase        =       timestep.to(self.timesteps.device							)
													__lowerCAmelCase        =       self.index_for_timestep(snake_case_							)
													__lowerCAmelCase        =       self.sigmas[step_idx]

													__lowerCAmelCase        =       sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

													__lowerCAmelCase        =       True
													return sample




							def  A__		(				self  ,							snake_case_							)   ->       Union[str, Any]:
													if not isinstance(snake_case_  ,							np.ndarray							):
																			__lowerCAmelCase        =       np.array(snake_case_  ,							dtype=np.floataa							)

													__lowerCAmelCase        =       1_000 * 0.25 * np.log(sigmas + 1e-4_4							)

													return timesteps




							def  A__		(				self  ,							snake_case_ = None  ,							snake_case_ = None  ,							snake_case_ = None  ,							)   ->       Any:
													if num_inference_steps is None and timesteps is None:
																			raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied."""							)

													if num_inference_steps is not None and timesteps is not None:
																			raise ValueError("""Can only pass one of `num_inference_steps` or `timesteps`."""							)

													# Follow DDPMScheduler custom timesteps logic
													if timesteps is not None:
																			for i in range(1  ,							len(snake_case_							)							):
																									if timesteps[i] >= timesteps[i - 1]:
																															raise ValueError("""`timesteps` must be in descending order."""							)

																			if timesteps[0] >= self.config.num_train_timesteps:
																									raise ValueError(
																									    f"""`timesteps` must start before `self.config.train_timesteps`:"""
																									    f""" {self.config.num_train_timesteps}."""							)

																			__lowerCAmelCase        =       np.array(snake_case_  ,							dtype=np.intaa							)
																			__lowerCAmelCase        =       True
													else:
																			if num_inference_steps > self.config.num_train_timesteps:
																									raise ValueError(
																									    f"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
																									    f""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
																									    f""" maximal {self.config.num_train_timesteps} timesteps."""							)

																			__lowerCAmelCase        =       num_inference_steps

																			__lowerCAmelCase        =       self.config.num_train_timesteps // self.num_inference_steps
																			__lowerCAmelCase        =       (np.arange(0  ,							snake_case_							) * step_ratio).round()[::-1].copy().astype(np.intaa							)
																			__lowerCAmelCase        =       False

													# Map timesteps to Karras sigmas directly for multistep sampling
													# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
													__lowerCAmelCase        =       self.config.num_train_timesteps
													__lowerCAmelCase        =       timesteps[::-1].copy()
													__lowerCAmelCase        =       ramp / (num_train_timesteps - 1)
													__lowerCAmelCase        =       self._convert_to_karras(snake_case_							)
													__lowerCAmelCase        =       self.sigma_to_t(snake_case_							)

													__lowerCAmelCase        =       np.concatenate([sigmas, [self.sigma_min]]							).astype(np.floataa							)
													__lowerCAmelCase        =       torch.from_numpy(snake_case_							).to(device=snake_case_							)

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




							def  A__		(				self  ,							snake_case_							)   ->       str:
													__lowerCAmelCase        =       self.config.sigma_min
													__lowerCAmelCase        =       self.config.sigma_max

													__lowerCAmelCase        =       self.config.rho
													__lowerCAmelCase        =       sigma_min ** (1 / rho)
													__lowerCAmelCase        =       sigma_max ** (1 / rho)
													__lowerCAmelCase        =       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
													return sigmas




							def  A__		(				self  ,							snake_case_							)   ->       Optional[int]:
													__lowerCAmelCase        =       self.config.sigma_data

													__lowerCAmelCase        =       sigma_data**2 / (sigma**2 + sigma_data**2)
													__lowerCAmelCase        =       sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
													return c_skip, c_out




							def  A__		(				self  ,							snake_case_							)   ->       Any:
													__lowerCAmelCase        =       self.config.sigma_min
													__lowerCAmelCase        =       self.config.sigma_data

													__lowerCAmelCase        =       sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
													__lowerCAmelCase        =       (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
													return c_skip, c_out




							def  A__		(				self  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_ = None  ,							snake_case_ = True  ,							)   ->       Union[CMStochasticIterativeSchedulerOutput, Tuple]:

													if (
													    isinstance(snake_case_  ,							snake_case_							)
													    or isinstance(snake_case_  ,							torch.IntTensor							)
													    or isinstance(snake_case_  ,							torch.LongTensor							)
													):
																			raise ValueError(
																			    (
																			        """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
																			        f""" `{self.__class__}.step()` is not supported. Make sure to pass"""
																			        """ one of the `scheduler.timesteps` as a timestep."""
																			    )  ,							)

													if not self.is_scale_input_called:
																			logger.warning(
																			    """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
																			    """See `StableDiffusionPipeline` for a usage example."""							)

													if isinstance(snake_case_  ,							torch.Tensor							):
																			__lowerCAmelCase        =       timestep.to(self.timesteps.device							)

													__lowerCAmelCase        =       self.config.sigma_min
													__lowerCAmelCase        =       self.config.sigma_max

													__lowerCAmelCase        =       self.index_for_timestep(snake_case_							)

													# sigma_next corresponds to next_t in original implementation
													__lowerCAmelCase        =       self.sigmas[step_index]
													if step_index + 1 < self.config.num_train_timesteps:
																			__lowerCAmelCase        =       self.sigmas[step_index + 1]
													else:
																			# Set sigma_next to sigma_min
																			__lowerCAmelCase        =       self.sigmas[-1]

													# Get scalings for boundary conditions
													__lowerCAmelCase ,      __lowerCAmelCase        =       self.get_scalings_for_boundary_condition(snake_case_							)

													# 1. Denoise model output using boundary conditions
													__lowerCAmelCase        =       c_out * model_output + c_skip * sample
													if self.config.clip_denoised:
																			__lowerCAmelCase        =       denoised.clamp(-1  ,							1							)

													# 2. Sample z ~ N(0, s_noise^2 * I)
													# Noise is not used for onestep sampling.
													if len(self.timesteps							) > 1:
																			__lowerCAmelCase        =       randn_tensor(
																			    model_output.shape  ,							dtype=model_output.dtype  ,							device=model_output.device  ,							generator=snake_case_							)
													else:
																			__lowerCAmelCase        =       torch.zeros_like(snake_case_							)
													__lowerCAmelCase        =       noise * self.config.s_noise

													__lowerCAmelCase        =       sigma_next.clamp(min=snake_case_  ,							max=snake_case_							)

													# 3. Return noisy sample
													# tau = sigma_hat, eps = sigma_min
													__lowerCAmelCase        =       denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

													if not return_dict:
																			return (prev_sample,)

													return CMStochasticIterativeSchedulerOutput(prev_sample=snake_case_							)




							def  A__		(				self  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							)   ->       torch.FloatTensor:
													# Make sure sigmas and timesteps have the same device and dtype as original_samples
													__lowerCAmelCase        =       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
																			__lowerCAmelCase        =       self.timesteps.to(original_samples.device  ,							dtype=torch.floataa							)
																			__lowerCAmelCase        =       timesteps.to(original_samples.device  ,							dtype=torch.floataa							)
													else:
																			__lowerCAmelCase        =       self.timesteps.to(original_samples.device							)
																			__lowerCAmelCase        =       timesteps.to(original_samples.device							)

													__lowerCAmelCase        =       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

													__lowerCAmelCase        =       original_samples + noise * sigma
													return noisy_samples




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