

'''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__			(   __A						:Optional[int]  ,__A						:Any=0.999  ,__A						:Any="cosine"  ,):


       """simple docstring"""




       if alpha_transform_type == "cosine":

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

       elif alpha_transform_type == "exp":

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

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

       __snake_case					  =       []
       for i in range(__A		):
              __snake_case					  =       i / num_diffusion_timesteps
              __snake_case					  =       (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 						__snake_case						(							snake_case__ ,  snake_case__     ):
       """simple docstring"""







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


       @register_to_config
       def __init__(		self			,   _UpperCamelCase = 10_00			,   _UpperCamelCase = 0.0_0085			,   _UpperCamelCase = 0.012			,   _UpperCamelCase = "linear"			,   _UpperCamelCase = None			,   _UpperCamelCase = "epsilon"			,   _UpperCamelCase = "linspace"			,   _UpperCamelCase = 0			,   )			->       List[Any]:






              """simple docstring"""


              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  a		(		self			,   _UpperCamelCase			,   _UpperCamelCase=None      )			->       Dict:






              """simple docstring"""


              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  a		(		self      )			->       str:






              """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			,   _UpperCamelCase			,   _UpperCamelCase			,   )			->       torch.FloatTensor:






              """simple docstring"""


              __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  a		(		self			,   _UpperCamelCase			,   _UpperCamelCase = None			,   _UpperCamelCase = None			,   )			->       Tuple:






              """simple docstring"""


              __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  a		(		self			,   _UpperCamelCase      )			->       int:






              """simple docstring"""


              __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  a		(		self      )			->       Tuple:






              """simple docstring"""


              return self.sample is None


       def  a		(		self			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase = None			,   _UpperCamelCase = True			,   )			->       Union[SchedulerOutput, Tuple]:






              """simple docstring"""


              __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  a		(		self			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   )			->       torch.FloatTensor:






              """simple docstring"""


              __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      )			->       Union[str, Any]:






              """simple docstring"""


              return self.config.num_train_timesteps




from __future__ import annotations

class 						__snake_case						:
       """simple docstring"""







       def __init__(		self			,   _UpperCamelCase      )			->       None:






              """simple docstring"""


              __snake_case					  =       value
              __snake_case					  =       None
              __snake_case					  =       None






class 						__snake_case						:
       """simple docstring"""







       __SCREAMING_SNAKE_CASE													=       42


       def __init__(		self      )			->       None:






              """simple docstring"""


              __snake_case					  =       0


       def  a		(		self			,   _UpperCamelCase			,   _UpperCamelCase      )			->       None:






              """simple docstring"""


              if node is None:
                     return

              if path_sum == self.target:
                     self.paths += 1

              if node.left:
                     self.depth_first_search(node.left			,   path_sum + node.left.value      )
              if node.right:
                     self.depth_first_search(node.right			,   path_sum + node.right.value      )






       def  a		(		self			,   _UpperCamelCase			,   _UpperCamelCase = None      )			->       int:






              """simple docstring"""


              if node is None:
                     return 0
              if target is not None:
                     __snake_case					  =       target

              self.depth_first_search(_UpperCamelCase			,   node.value      )
              self.path_sum(node.left      )
              self.path_sum(node.right      )

              return self.paths


if __name__ == "__main__":
    import doctest

    doctest.testmod()

