"""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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[int]			,_lowerCamelCase						:       Union[str, Any]=0.9_99			,_lowerCamelCase						:       Optional[int]="cosine"			,)       ->      Optional[Any]:
  if alpha_transform_type == "cosine":

    def alpha_bar_fn(_lowerCamelCase						:       List[Any]	):
      return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2	) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(_lowerCamelCase						:       List[str]	):
      return math.exp(t * -12.0	)

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

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






class 						__A							( SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Union[str, Any]							=		[e.name for e in KarrasDiffusionSchedulers]
     _UpperCamelCase							:			Dict							=		2






     @register_to_config
     def __init__(		self				,     a__ = 1000				,     a__ = 0.0_0_0_8_5				,     a__ = 0.0_1_2				,     a__ = "linear"				,     a__ = None				,     a__ = "epsilon"				,     a__ = "linspace"				,     a__ = 0				,     ):
       if trained_betas is not None:
         _lowerCAmelCase						:       str  			=     torch.tensor(a__				,     dtype=torch.floataa  )
       elif beta_schedule == "linear":
         _lowerCAmelCase						:       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.
         _lowerCAmelCase						:       int  			=     (
             torch.linspace(beta_start**0.5				,     beta_end**0.5				,     a__				,     dtype=torch.floataa  ) ** 2
         )
       elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         _lowerCAmelCase						:       Optional[Any]  			=     betas_for_alpha_bar(a__  )
       else:
         raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"  )

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

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






     def     __A  (		self				,     a__				,     a__=None  ):
       if schedule_timesteps is None:
         _lowerCAmelCase						:       Dict  			=     self.timesteps

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

       return indices[pos].item()






     @property
     def     __A  (		self  ):
       # 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     __A  (		self				,     a__				,     a__				,     ):
       _lowerCAmelCase						:       Union[str, Any]  			=     self.index_for_timestep(a__  )

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

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






     def     __A  (		self				,     a__				,     a__ = None				,     a__ = None				,     ):
       _lowerCAmelCase						:       List[Any]  			=     num_inference_steps

       _lowerCAmelCase						:       Tuple  			=     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						:       List[str]  			=     np.linspace(0				,     num_train_timesteps - 1				,     a__				,     dtype=a__  )[::-1].copy()
       elif self.config.timestep_spacing == "leading":
         _lowerCAmelCase						:       Union[str, 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						:       List[str]  			=     (np.arange(0				,     a__  ) * step_ratio).round()[::-1].copy().astype(a__  )
         timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
         _lowerCAmelCase						:       Union[str, 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						:       str  			=     (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'."  )

       _lowerCAmelCase						:       Dict  			=     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
       _lowerCAmelCase						:       List[str]  			=     torch.from_numpy(np.log(a__  )  ).to(a__  )

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

       # interpolate sigmas
       _lowerCAmelCase						:       List[str]  			=     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(a__  ).startswith("""mps"""  ):
         # mps does not support float64
         _lowerCAmelCase						:       List[Any]  			=     torch.from_numpy(a__  ).to(a__				,     dtype=torch.floataa  )
       else:
         _lowerCAmelCase						:       List[str]  			=     torch.from_numpy(a__  ).to(a__  )

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

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

       _lowerCAmelCase						:       Optional[Any]  			=     None

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






     def     __A  (		self				,     a__  ):
       # get log sigma
       _lowerCAmelCase						:       str  			=     sigma.log()

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

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

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

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

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






     @property
     def     __A  (		self  ):
       return self.sample is None






     def     __A  (		self				,     a__				,     a__				,     a__				,     a__ = True				,     ):
       _lowerCAmelCase						:       List[str]  			=     self.index_for_timestep(a__  )

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

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

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

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

         _lowerCAmelCase						:       int  			=     self.sample
         _lowerCAmelCase						:       List[Any]  			=     None

       _lowerCAmelCase						:       Tuple  			=     sample + derivative * dt

       if not return_dict:
         return (prev_sample,)

       return SchedulerOutput(prev_sample=a__  )






     def     __A  (		self				,     a__				,     a__				,     a__				,     ):
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       _lowerCAmelCase						:       Optional[Any]  			=     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
         _lowerCAmelCase						:       str  			=     self.timesteps.to(original_samples.device				,     dtype=torch.floataa  )
         _lowerCAmelCase						:       List[Any]  			=     timesteps.to(original_samples.device				,     dtype=torch.floataa  )
       else:
         _lowerCAmelCase						:       Any  			=     self.timesteps.to(original_samples.device  )
         _lowerCAmelCase						:       int  			=     timesteps.to(original_samples.device  )

       _lowerCAmelCase						:       str  			=     [self.index_for_timestep(a__				,     a__  ) for t in timesteps]

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

       _lowerCAmelCase						:       Tuple  			=     original_samples + noise * sigma
       return noisy_samples






     def __len__(		self  ):
       return self.config.num_train_timesteps





def       _snake_case	(   lowerCAmelCase							:   int	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Tuple				      =    int(lowerCAmelCase	)
		if decimal in (0, 1):  # Exit cases for the recursion
				return str(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Dict				      =    divmod(lowerCAmelCase   , 2	)
		return binary_recursive(lowerCAmelCase	) + str(lowerCAmelCase	)




def       _snake_case	(   lowerCAmelCase							:   str	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		str				      =    str(lowerCAmelCase	).strip()
		if not number:
				raise ValueError("No input value was provided"	)
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    "-" if number.startswith("-"	) else ""
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    number.lstrip("-"	)
		if not number.isnumeric():
				raise ValueError("Input value is not an integer"	)
		return f'{negative}0b{binary_recursive(int(lowerCAmelCase	)	)}'


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

		testmod()
