


'''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       lowercase					(_A ,    _A=0.999 ,    _A="cosine" ,    ):







       """simple docstring"""







       if alpha_transform_type == "cosine":

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

       elif alpha_transform_type == "exp":

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

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

       _lowerCAmelCase     :				List[str]       =							[]
       for i in range(_A       ):
              _lowerCAmelCase     :				int       =							i / num_diffusion_timesteps
              _lowerCAmelCase     :				Optional[int]       =							(i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar_fn(_A       ) / alpha_bar_fn(_A       ) ,    _A       )       )
       return torch.tensor(_A ,    dtype=torch.floataa       )
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






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







     @register_to_config
     def __init__(			self   ,						snake_case__ = 1000   ,						snake_case__ = 0.0_0085   ,						snake_case__ = 0.012   ,						snake_case__ = "linear"   ,						snake_case__ = None   ,						snake_case__ = "epsilon"   ,						snake_case__ = "linspace"   ,						snake_case__ = 0   ,						):

            '''simple docstring'''

            if trained_betas is not None:
                   _lowerCAmelCase     :				Optional[int]       =							torch.tensor(snake_case__   ,						dtype=torch.floataa )
            elif beta_schedule == "linear":
                   _lowerCAmelCase     :				int       =							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.
                   _lowerCAmelCase     :				Dict       =							(
                       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
                   _lowerCAmelCase     :				Dict       =							betas_for_alpha_bar(snake_case__ )
            else:
                   raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}' )

            _lowerCAmelCase     :				Optional[int]       =							1.0 - self.betas
            _lowerCAmelCase     :				Optional[int]       =							torch.cumprod(self.alphas   ,						dim=0 )

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







     def a						(			self   ,						snake_case__   ,						snake_case__=None ):

            '''simple docstring'''

            if schedule_timesteps is None:
                   _lowerCAmelCase     :				Union[str, Any]       =							self.timesteps

            _lowerCAmelCase     :				Dict       =							(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:
                   _lowerCAmelCase     :				Tuple       =							1 if len(snake_case__ ) > 1 else 0
            else:
                   _lowerCAmelCase     :				Tuple       =							timestep.cpu().item() if torch.is_tensor(snake_case__ ) else timestep
                   _lowerCAmelCase     :				List[str]       =							self._index_counter[timestep_int]

            return indices[pos].item()







     @property
     def a						(			self ):

            '''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   ,						snake_case__   ,						snake_case__   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							self.index_for_timestep(snake_case__ )

            if self.state_in_first_order:
                   _lowerCAmelCase     :				List[Any]       =							self.sigmas[step_index]
            else:
                   _lowerCAmelCase     :				Optional[int]       =							self.sigmas_interpol[step_index]

            _lowerCAmelCase     :				str       =							sample / ((sigma**2 + 1) ** 0.5)
            return sample







     def a						(			self   ,						snake_case__   ,						snake_case__ = None   ,						snake_case__ = None   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							num_inference_steps

            _lowerCAmelCase     :				Optional[int]       =							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":
                   _lowerCAmelCase     :				Dict       =							np.linspace(0   ,						num_train_timesteps - 1   ,						snake_case__   ,						dtype=snake_case__ )[::-1].copy()
            elif self.config.timestep_spacing == "leading":
                   _lowerCAmelCase     :				List[Any]       =							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
                   _lowerCAmelCase     :				Optional[int]       =							(np.arange(0   ,						snake_case__ ) * step_ratio).round()[::-1].copy().astype(snake_case__ )
                   timesteps += self.config.steps_offset
            elif self.config.timestep_spacing == "trailing":
                   _lowerCAmelCase     :				Optional[int]       =							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
                   _lowerCAmelCase     :				List[Any]       =							(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\'.' )

            _lowerCAmelCase     :				int       =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
            _lowerCAmelCase     :				List[Any]       =							torch.from_numpy(np.log(snake_case__ ) ).to(snake_case__ )

            _lowerCAmelCase     :				List[Any]       =							np.interp(snake_case__   ,						np.arange(0   ,						len(snake_case__ ) )   ,						snake_case__ )
            _lowerCAmelCase     :				str       =							np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
            _lowerCAmelCase     :				List[str]       =							torch.from_numpy(snake_case__ ).to(device=snake_case__ )

            # interpolate sigmas
            _lowerCAmelCase     :				Tuple       =							sigmas.log().lerp(sigmas.roll(1 ).log()   ,						0.5 ).exp()

            _lowerCAmelCase     :				Dict       =							torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
            _lowerCAmelCase     :				Union[str, Any]       =							torch.cat(
                [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )

            if str(snake_case__ ).startswith('mps' ):
                   # mps does not support float64
                   _lowerCAmelCase     :				List[Any]       =							torch.from_numpy(snake_case__ ).to(snake_case__   ,						dtype=torch.floataa )
            else:
                   _lowerCAmelCase     :				int       =							torch.from_numpy(snake_case__ ).to(snake_case__ )

            # interpolate timesteps
            _lowerCAmelCase     :				Tuple       =							self.sigma_to_t(snake_case__ ).to(snake_case__   ,						dtype=timesteps.dtype )
            _lowerCAmelCase     :				Any       =							torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])   ,						dim=-1 ).flatten()

            _lowerCAmelCase     :				Optional[Any]       =							torch.cat([timesteps[:1], interleaved_timesteps] )

            _lowerCAmelCase     :				List[Any]       =							None

            # for exp beta schedules, such as the one for `pipeline_shap_e.py`
            # we need an index counter
            _lowerCAmelCase     :				Union[str, Any]       =							defaultdict(snake_case__ )







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							sigma.log()

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

            # get sigmas range
            _lowerCAmelCase     :				Optional[int]       =							dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
            _lowerCAmelCase     :				List[Any]       =							low_idx + 1

            _lowerCAmelCase     :				Union[str, Any]       =							self.log_sigmas[low_idx]
            _lowerCAmelCase     :				Any       =							self.log_sigmas[high_idx]

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

            # transform interpolation to time range
            _lowerCAmelCase     :				Union[str, Any]       =							(1 - w) * low_idx + w * high_idx
            _lowerCAmelCase     :				Dict       =							t.view(sigma.shape )
            return t







     @property
     def a						(			self ):

            '''simple docstring'''

            return self.sample is None







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__ = True   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							self.index_for_timestep(snake_case__ )

            # advance index counter by 1
            _lowerCAmelCase     :				Union[str, Any]       =							timestep.cpu().item() if torch.is_tensor(snake_case__ ) else timestep
            self._index_counter[timestep_int] += 1

            if self.state_in_first_order:
                   _lowerCAmelCase     :				Optional[int]       =							self.sigmas[step_index]
                   _lowerCAmelCase     :				Union[str, Any]       =							self.sigmas_interpol[step_index + 1]
                   _lowerCAmelCase     :				Any       =							self.sigmas[step_index + 1]
            else:
                   # 2nd order / KDPM2's method
                   _lowerCAmelCase     :				Dict       =							self.sigmas[step_index - 1]
                   _lowerCAmelCase     :				List[str]       =							self.sigmas_interpol[step_index]
                   _lowerCAmelCase     :				Union[str, Any]       =							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
            _lowerCAmelCase     :				Any       =							0
            _lowerCAmelCase     :				str       =							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":
                   _lowerCAmelCase     :				Tuple       =							sigma_hat if self.state_in_first_order else sigma_interpol
                   _lowerCAmelCase     :				List[Any]       =							sample - sigma_input * model_output
            elif self.config.prediction_type == "v_prediction":
                   _lowerCAmelCase     :				Dict       =							sigma_hat if self.state_in_first_order else sigma_interpol
                   _lowerCAmelCase     :				int       =							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
                   _lowerCAmelCase     :				Optional[Any]       =							(sample - pred_original_sample) / sigma_hat
                   # 3. delta timestep
                   _lowerCAmelCase     :				List[Any]       =							sigma_interpol - sigma_hat

                   # store for 2nd order step
                   _lowerCAmelCase     :				List[str]       =							sample
            else:
                   # DPM-Solver-2
                   # 2. Convert to an ODE derivative for 2nd order
                   _lowerCAmelCase     :				Optional[Any]       =							(sample - pred_original_sample) / sigma_interpol

                   # 3. delta timestep
                   _lowerCAmelCase     :				Union[str, Any]       =							sigma_next - sigma_hat

                   _lowerCAmelCase     :				Tuple       =							self.sample
                   _lowerCAmelCase     :				Any       =							None

            _lowerCAmelCase     :				Union[str, Any]       =							sample + derivative * dt

            if not return_dict:
                   return (prev_sample,)

            return SchedulerOutput(prev_sample=snake_case__ )







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							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     :				Any       =							self.timesteps.to(original_samples.device   ,						dtype=torch.floataa )
                   _lowerCAmelCase     :				Any       =							timesteps.to(original_samples.device   ,						dtype=torch.floataa )
            else:
                   _lowerCAmelCase     :				Optional[int]       =							self.timesteps.to(original_samples.device )
                   _lowerCAmelCase     :				str       =							timesteps.to(original_samples.device )

            _lowerCAmelCase     :				Any       =							[self.index_for_timestep(snake_case__   ,						snake_case__ ) for t in timesteps]

            _lowerCAmelCase     :				Optional[int]       =							sigmas[step_indices].flatten()
            while len(sigma.shape ) < len(original_samples.shape ):
                   _lowerCAmelCase     :				List[Any]       =							sigma.unsqueeze(-1 )

            _lowerCAmelCase     :				Dict       =							original_samples + noise * sigma
            return noisy_samples







     def __len__(			self ):

            '''simple docstring'''

            return self.config.num_train_timesteps





'''simple docstring'''







from ..utils import DummyObject, requires_backends
class 						UpperCamelCase__       (			metaclass=SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     ["torch", "torchsde"]







     def __init__(			self   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            requires_backends(self   ,						['torch', 'torchsde'] )







     @classmethod
     def a						(			cls   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            requires_backends(cls   ,						['torch', 'torchsde'] )







     @classmethod
     def a						(			cls   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            requires_backends(cls   ,						['torch', 'torchsde'] )

