'''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 SCREAMING_SNAKE_CASE__   (							snake_case    :							Any							,     snake_case    :							str=0.9_99							,     snake_case    :							Any="cosine"							,     )					->							Union[str, Any]:







     """simple docstring"""
     if alpha_transform_type == "cosine":

          def alpha_bar_fn(snake_case    :							str   ):
               return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2   ) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(snake_case    :							Optional[Any]   ):
               return math.exp(t * -12.0   )

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

     a :				Optional[int]				=      []
     for i in range(snake_case   ):
          a :				int				=      i / num_diffusion_timesteps
          a :				Any				=      (i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(snake_case   ) / alpha_bar_fn(snake_case   )							,     snake_case   )   )
     return torch.tensor(snake_case							,     dtype=torch.floataa   )





class    UpperCamelCase (    lowerCAmelCase__  ,			lowerCAmelCase__    ):







    """simple docstring"""





    A			:  List[Any]            =       [e.name for e in KarrasDiffusionSchedulers]
    A			:  Tuple            =       2





    @register_to_config
    def __init__( self			:		Any				,				UpperCAmelCase_			:		List[str] = 1_0_0_0				,				UpperCAmelCase_			:		List[Any] = 0.0_00_85				,				UpperCAmelCase_			:		Dict = 0.0_12				,				UpperCAmelCase_			:		Optional[Any] = "linear"				,				UpperCAmelCase_			:		Dict = None				,				UpperCAmelCase_			:		Union[str, Any] = "epsilon"				,				UpperCAmelCase_			:		Dict = "linspace"				,				UpperCAmelCase_			:		List[str] = 0				,				):


         """simple docstring"""







         if trained_betas is not None:
              a :				Tuple				=      torch.tensor(a__				,				dtype=torch.floataa)
         elif beta_schedule == "linear":
              a :				Optional[Any]				=      torch.linspace(a__				,				a__				,				a__				,				dtype=torch.floataa)
         elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              a :				Dict				=      (
                  torch.linspace(beta_start**0.5				,				beta_end**0.5				,				a__				,				dtype=torch.floataa) ** 2
              )
         elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              a :				int				=      betas_for_alpha_bar(a__)
         else:
              raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}""")

         a :				Tuple				=      1.0 - self.betas
         a :				Dict				=      torch.cumprod(self.alphas				,				dim=0)

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





    def 					SCREAMING_SNAKE_CASE_			( self			:		List[str]				,				UpperCAmelCase_			:		List[Any]				,				UpperCAmelCase_			:		Optional[int]=None):


         """simple docstring"""







         if schedule_timesteps is None:
              a :				List[str]				=      self.timesteps

         a :				Tuple				=      (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 :				str				=      1 if len(a__) > 1 else 0
         else:
              a :				Union[str, Any]				=      timestep.cpu().item() if torch.is_tensor(a__) else timestep
              a :				List[Any]				=      self._index_counter[timestep_int]

         return indices[pos].item()





    @property
    def 					SCREAMING_SNAKE_CASE_			( self			:		int):


         """simple docstring"""







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

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





    def 					SCREAMING_SNAKE_CASE_			( self			:		List[Any]				,				UpperCAmelCase_			:		List[str]				,				UpperCAmelCase_			:		Tuple				,				):


         """simple docstring"""







         a :				int				=      self.index_for_timestep(a__)

         if self.state_in_first_order:
              a :				Dict				=      self.sigmas[step_index]
         else:
              a :				str				=      self.sigmas_interpol[step_index - 1]

         a :				List[str]				=      sample / ((sigma**2 + 1) ** 0.5)
         return sample





    def 					SCREAMING_SNAKE_CASE_			( self			:		List[str]				,				UpperCAmelCase_			:		Tuple				,				UpperCAmelCase_			:		int = None				,				UpperCAmelCase_			:		List[str] = None				,				):


         """simple docstring"""







         a :				Any				=      num_inference_steps

         a :				Dict				=      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 :				List[Any]				=      np.linspace(0				,				num_train_timesteps - 1				,				a__				,				dtype=a__)[::-1].copy()
         elif self.config.timestep_spacing == "leading":
              a :				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
              a :				int				=      (np.arange(0				,				a__) * step_ratio).round()[::-1].copy().astype(a__)
              timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
              a :				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
              a :				Dict				=      (np.arange(a__				,				0				,				-step_ratio)).round().copy().astype(a__)
              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 :				int				=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
         a :				List[Any]				=      torch.from_numpy(np.log(a__)).to(a__)

         a :				List[str]				=      np.interp(a__				,				np.arange(0				,				len(a__))				,				a__)
         a :				Optional[int]				=      np.concatenate([sigmas, [0.0]]).astype(np.floataa)
         a :				str				=      torch.from_numpy(a__).to(device=a__)

         # compute up and down sigmas
         a :				List[str]				=      sigmas.roll(-1)
         a :				Any				=      0.0
         a :				Optional[Any]				=      (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
         a :				Dict				=      (sigmas_next**2 - sigmas_up**2) ** 0.5
         a :				Union[str, Any]				=      0.0

         # compute interpolated sigmas
         a :				Any				=      sigmas.log().lerp(sigmas_down.log()				,				0.5).exp()
         a :				Optional[int]				=      0.0

         # set sigmas
         a :				Dict				=      torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2), sigmas[-1:]])
         a :				str				=      torch.cat(
             [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2), sigmas_interpol[-1:]])
         a :				List[Any]				=      torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2), sigmas_up[-1:]])
         a :				Tuple				=      torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2), sigmas_down[-1:]])

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

         a :				List[str]				=      self.sigma_to_t(a__).to(a__				,				dtype=timesteps.dtype)
         a :				List[Any]				=      torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])				,				dim=-1).flatten()

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

         a :				Optional[Any]				=      None

         # for exp beta schedules, such as the one for `pipeline_shap_e.py`
         # we need an index counter
         a :				str				=      defaultdict(a__)





    def 					SCREAMING_SNAKE_CASE_			( self			:		Optional[int]				,				UpperCAmelCase_			:		str):


         """simple docstring"""







         a :				Optional[Any]				=      sigma.log()

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

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

         a :				str				=      self.log_sigmas[low_idx]
         a :				List[Any]				=      self.log_sigmas[high_idx]

         # interpolate sigmas
         a :				int				=      (low - log_sigma) / (low - high)
         a :				Any				=      w.clamp(0				,				1)

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





    @property
    def 					SCREAMING_SNAKE_CASE_			( self			:		Any):


         """simple docstring"""







         return self.sample is None





    def 					SCREAMING_SNAKE_CASE_			( self			:		str				,				UpperCAmelCase_			:		Tuple				,				UpperCAmelCase_			:		Tuple				,				UpperCAmelCase_			:		List[Any]				,				UpperCAmelCase_			:		List[str] = None				,				UpperCAmelCase_			:		Any = True				,				):


         """simple docstring"""







         a :				Union[str, Any]				=      self.index_for_timestep(a__)

         # advance index counter by 1
         a :				List[Any]				=      timestep.cpu().item() if torch.is_tensor(a__) else timestep
         self._index_counter[timestep_int] += 1

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

         a :				Dict				=      model_output.device
         a :				int				=      randn_tensor(model_output.shape				,				dtype=model_output.dtype				,				device=a__				,				generator=a__)

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
              a :				Optional[int]				=      sigma_hat if self.state_in_first_order else sigma_interpol
              a :				int				=      sample - sigma_input * model_output
         elif self.config.prediction_type == "v_prediction":
              a :				Union[str, Any]				=      sigma_hat if self.state_in_first_order else sigma_interpol
              a :				str				=      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 :				Optional[int]				=      (sample - pred_original_sample) / sigma_hat
              # 3. delta timestep
              a :				Dict				=      sigma_interpol - sigma_hat

              # store for 2nd order step
              a :				Optional[Any]				=      sample
              a :				Dict				=      dt
              a :				List[str]				=      sample + derivative * dt
         else:
              # DPM-Solver-2
              # 2. Convert to an ODE derivative for 2nd order
              a :				str				=      (sample - pred_original_sample) / sigma_interpol
              # 3. delta timestep
              a :				List[Any]				=      sigma_down - sigma_hat

              a :				List[str]				=      self.sample
              a :				str				=      None

              a :				List[str]				=      sample + derivative * dt
              a :				Tuple				=      prev_sample + noise * sigma_up

         if not return_dict:
              return (prev_sample,)

         return SchedulerOutput(prev_sample=a__)





    def 					SCREAMING_SNAKE_CASE_			( self			:		Tuple				,				UpperCAmelCase_			:		List[str]				,				UpperCAmelCase_			:		Tuple				,				UpperCAmelCase_			:		Tuple				,				):


         """simple docstring"""







         a :				str				=      self.sigmas.to(device=original_samples.device				,				dtype=original_samples.dtype)
         if original_samples.device.type == "mps" and torch.is_floating_point(a__):
              # mps does not support float64
              a :				Tuple				=      self.timesteps.to(original_samples.device				,				dtype=torch.floataa)
              a :				Any				=      timesteps.to(original_samples.device				,				dtype=torch.floataa)
         else:
              a :				List[str]				=      self.timesteps.to(original_samples.device)
              a :				Any				=      timesteps.to(original_samples.device)

         a :				int				=      [self.index_for_timestep(a__				,				a__) for t in timesteps]

         a :				Union[str, Any]				=      sigmas[step_indices].flatten()
         while len(sigma.shape) < len(original_samples.shape):
              a :				Optional[Any]				=      sigma.unsqueeze(-1)

         a :				Dict				=      original_samples + noise * sigma
         return noisy_samples







    def __len__( self			:		List[Any]):


         """simple docstring"""







         return self.config.num_train_timesteps








"""simple docstring"""


def SCREAMING_SNAKE_CASE_   (       snake_case   :					str		)->		list:
       _lowerCamelCase    	=					[]
       _lowerCamelCase    	=					1

       for i in range(len(snake_case		)		):
              if i + 1 < len(snake_case		) and text[i] == text[i + 1]:
                     count += 1
              else:
                     encoded.append((text[i], count)		)
                     _lowerCamelCase    	=					1

       return encoded





def SCREAMING_SNAKE_CASE_   (       snake_case   :					list		)->		str:
       return "".join(char * length for char, length in encoded		)


if __name__ == "__main__":
      from doctest import testmod

      testmod(name="""run_length_encode""", verbose=True)
      testmod(name="""run_length_decode""", verbose=True)






