




'''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 		UpperCamelCase__     (		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE=0.999							,			__SCREAMING_SNAKE_CASE="cosine"							,			)						->      List[str]:
      if alpha_transform_type == "cosine":

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

      elif alpha_transform_type == "exp":

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

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

      snake_case__					:      Dict  			=						[]
      for i in range(__SCREAMING_SNAKE_CASE		):
            snake_case__					:      Dict  			=						i / num_diffusion_timesteps
            snake_case__					:      str  			=						(i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(__SCREAMING_SNAKE_CASE		) / alpha_bar_fn(__SCREAMING_SNAKE_CASE		)							,			__SCREAMING_SNAKE_CASE		)		)
      return torch.tensor(__SCREAMING_SNAKE_CASE							,			dtype=torch.floataa		)






class 					lowercase_			(	lowerCAmelCase_	,					lowerCAmelCase_       ):
     A_       							= [e.name for e in KarrasDiffusionSchedulers]
     A_       							= 2





     @register_to_config
     def __init__(    self    :				Optional[int]    ,       __lowerCamelCase    :				int = 1000    ,       __lowerCamelCase    :				float = 0.0_0_0_8_5    ,       __lowerCamelCase    :				float = 0.0_1_2    ,       __lowerCamelCase    :				str = "linear"    ,       __lowerCamelCase    :				Optional[Union[np.ndarray, List[float]]] = None    ,       __lowerCamelCase    :				str = "epsilon"    ,       __lowerCamelCase    :				str = "linspace"    ,       __lowerCamelCase    :				int = 0    ,       ):
           if trained_betas is not None:
                 snake_case__					:      Optional[int]  			=						torch.tensor(__lowerCamelCase    ,       dtype=torch.floataa )
           elif beta_schedule == "linear":
                 snake_case__					:      Optional[Any]  			=						torch.linspace(__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase    ,       dtype=torch.floataa )
           elif beta_schedule == "scaled_linear":
                 # this schedule is very specific to the latent diffusion model.
                 snake_case__					:      Optional[Any]  			=						(
                     torch.linspace(beta_start**0.5    ,       beta_end**0.5    ,       __lowerCamelCase    ,       dtype=torch.floataa ) ** 2
                 )
           elif beta_schedule == "squaredcos_cap_v2":
                 # Glide cosine schedule
                 snake_case__					:      Any  			=						betas_for_alpha_bar(__lowerCamelCase )
           else:
                 raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}" )

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

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





     def  _lowerCAmelCase			(    self    :				Union[str, Any]    ,       __lowerCamelCase    :				Tuple    ,       __lowerCamelCase    :				List[Any]=None ):
           if schedule_timesteps is None:
                 snake_case__					:      Any  			=						self.timesteps

           snake_case__					:      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:
                 snake_case__					:      Dict  			=						1 if len(__lowerCamelCase ) > 1 else 0
           else:
                 snake_case__					:      Optional[Any]  			=						timestep.cpu().item() if torch.is_tensor(__lowerCamelCase ) else timestep
                 snake_case__					:      Optional[int]  			=						self._index_counter[timestep_int]

           return indices[pos].item()





     @property
     def  _lowerCAmelCase			(    self    :				List[Any] ):
           # 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  _lowerCAmelCase			(    self    :				List[Any]    ,       __lowerCamelCase    :				torch.FloatTensor    ,       __lowerCamelCase    :				Union[float, torch.FloatTensor]    ,       ):
           snake_case__					:      Any  			=						self.index_for_timestep(__lowerCamelCase )

           if self.state_in_first_order:
                 snake_case__					:      List[Any]  			=						self.sigmas[step_index]
           else:
                 snake_case__					:      Optional[Any]  			=						self.sigmas_interpol[step_index - 1]

           snake_case__					:      Dict  			=						sample / ((sigma**2 + 1) ** 0.5)
           return sample





     def  _lowerCAmelCase			(    self    :				Optional[Any]    ,       __lowerCamelCase    :				int    ,       __lowerCamelCase    :				Union[str, torch.device] = None    ,       __lowerCamelCase    :				Optional[int] = None    ,       ):
           snake_case__					:      Union[str, Any]  			=						num_inference_steps

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

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

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

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

           # set sigmas
           snake_case__					:      List[Any]  			=						torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
           snake_case__					:      Optional[Any]  			=						torch.cat(
               [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
           snake_case__					:      Optional[Any]  			=						torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2 ), sigmas_up[-1:]] )
           snake_case__					:      Any  			=						torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2 ), sigmas_down[-1:]] )

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

           snake_case__					:      Union[str, Any]  			=						self.sigma_to_t(__lowerCamelCase ).to(__lowerCamelCase    ,       dtype=timesteps.dtype )
           snake_case__					:      Tuple  			=						torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])    ,       dim=-1 ).flatten()

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

           snake_case__					:      Optional[int]  			=						None

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





     def  _lowerCAmelCase			(    self    :				Optional[int]    ,       __lowerCamelCase    :				Any ):
           # get log sigma
           snake_case__					:      Optional[int]  			=						sigma.log()

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

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

           snake_case__					:      List[str]  			=						self.log_sigmas[low_idx]
           snake_case__					:      str  			=						self.log_sigmas[high_idx]

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

           # transform interpolation to time range
           snake_case__					:      Any  			=						(1 - w) * low_idx + w * high_idx
           snake_case__					:      str  			=						t.view(sigma.shape )
           return t





     @property
     def  _lowerCAmelCase			(    self    :				Optional[int] ):
           return self.sample is None





     def  _lowerCAmelCase			(    self    :				Any    ,       __lowerCamelCase    :				Union[torch.FloatTensor, np.ndarray]    ,       __lowerCamelCase    :				Union[float, torch.FloatTensor]    ,       __lowerCamelCase    :				Union[torch.FloatTensor, np.ndarray]    ,       __lowerCamelCase    :				Optional[torch.Generator] = None    ,       __lowerCamelCase    :				bool = True    ,       ):
           snake_case__					:      Optional[Any]  			=						self.index_for_timestep(__lowerCamelCase )

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

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

           snake_case__					:      Dict  			=						model_output.device
           snake_case__					:      str  			=						randn_tensor(model_output.shape    ,       dtype=model_output.dtype    ,       device=__lowerCamelCase    ,       generator=__lowerCamelCase )

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

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

                 snake_case__					:      List[Any]  			=						self.sample
                 snake_case__					:      Optional[Any]  			=						None

                 snake_case__					:      List[str]  			=						sample + derivative * dt
                 snake_case__					:      int  			=						prev_sample + noise * sigma_up

           if not return_dict:
                 return (prev_sample,)

           return SchedulerOutput(prev_sample=__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				Tuple    ,       __lowerCamelCase    :				torch.FloatTensor    ,       __lowerCamelCase    :				torch.FloatTensor    ,       __lowerCamelCase    :				torch.FloatTensor    ,       ):
           # Make sure sigmas and timesteps have the same device and dtype as original_samples
           snake_case__					:      List[Any]  			=						self.sigmas.to(device=original_samples.device    ,       dtype=original_samples.dtype )
           if original_samples.device.type == "mps" and torch.is_floating_point(__lowerCamelCase ):
                 # mps does not support float64
                 snake_case__					:      Optional[Any]  			=						self.timesteps.to(original_samples.device    ,       dtype=torch.floataa )
                 snake_case__					:      List[Any]  			=						timesteps.to(original_samples.device    ,       dtype=torch.floataa )
           else:
                 snake_case__					:      Tuple  			=						self.timesteps.to(original_samples.device )
                 snake_case__					:      Any  			=						timesteps.to(original_samples.device )

           snake_case__					:      str  			=						[self.index_for_timestep(__lowerCamelCase    ,       __lowerCamelCase ) for t in timesteps]

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

           snake_case__					:      int  			=						original_samples + noise * sigma
           return noisy_samples





     def __len__(    self    :				Optional[Any] ):
           return self.config.num_train_timesteps






'''simple docstring'''






class 					lowercase_			:





     def __init__(    self    :				Any ):
           snake_case__					:      List[str]  			=						[0]
           snake_case__					:      Optional[Any]  			=						0





     def  _lowerCAmelCase			(    self    :				List[str]    ,       __lowerCamelCase    :				int ):
           snake_case__					:      Optional[int]  			=						self.__heap[i]
           while i // 2 > 0:
                 if self.__heap[i] > self.__heap[i // 2]:
                       snake_case__					:      Union[str, Any]  			=						self.__heap[i // 2]
                       snake_case__					:      Union[str, Any]  			=						temporary
                 i //= 2





     def  _lowerCAmelCase			(    self    :				int    ,       __lowerCamelCase    :				int ):
           self.__heap.append(__lowerCamelCase )
           self.__size += 1
           self.__swap_up(self.__size )





     def  _lowerCAmelCase			(    self    :				Dict    ,       __lowerCamelCase    :				int ):
           while self.__size >= 2 * i:
                 if 2 * i + 1 > self.__size:
                       snake_case__					:      List[str]  			=						2 * i
                 else:
                       if self.__heap[2 * i] > self.__heap[2 * i + 1]:
                             snake_case__					:      Any  			=						2 * i
                       else:
                             snake_case__					:      Tuple  			=						2 * i + 1
                 snake_case__					:      int  			=						self.__heap[i]
                 if self.__heap[i] < self.__heap[bigger_child]:
                       snake_case__					:      Tuple  			=						self.__heap[bigger_child]
                       snake_case__					:      int  			=						temporary
                 snake_case__					:      int  			=						bigger_child





     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           snake_case__					:      Any  			=						self.__heap[1]
           snake_case__					:      List[str]  			=						self.__heap[self.__size]
           self.__size -= 1
           self.__heap.pop()
           self.__swap_down(1 )
           return max_value





     @property
     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           return self.__heap[1:]





     def __len__(    self    :				int ):
           return self.__size


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      # create an instance of BinaryHeap
      A_ 			=		BinaryHeap()
      binary_heap.insert(6)
      binary_heap.insert(10)
      binary_heap.insert(15)
      binary_heap.insert(12)
      # pop root(max-values because it is max heap)
      print(binary_heap.pop())  # 15
      print(binary_heap.pop())  # 12
      # get the list and size after operations
      print(binary_heap.get_list)
      print(len(binary_heap))
