
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


snake_case__  							=							logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 							UpperCAmelCase	(   __lowerCamelCase					):
					a__: torch.FloatTensor
					a__: Optional[torch.FloatTensor]			  =				None





def        lowerCamelCase_      (			UpperCAmelCase_							:				Dict							,			UpperCAmelCase_							:				int=0.9_99							,			UpperCAmelCase_							:				Dict="cosine"							,			):
						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

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

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

						lowercase       :    List[str]											=  []
						for i in range(UpperCAmelCase_       ):
												lowercase       :    List[str]											=  i / num_diffusion_timesteps
												lowercase       :    Tuple											=  (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(UpperCAmelCase_       ) / alpha_bar_fn(UpperCAmelCase_       )							,			UpperCAmelCase_       )       )
						return torch.tensor(UpperCAmelCase_							,			dtype=torch.floataa       )







class 							UpperCAmelCase	(   __lowerCamelCase						, __lowerCamelCase					):
					a__: List[str]			  =				[e.name for e in KarrasDiffusionSchedulers]
					a__: Tuple			  =				1

					@register_to_config
					def __init__(			self      :					List[Any] ,      lowerCAmelCase      :					int = 1000 ,      lowerCAmelCase      :					float = 0.00_01 ,      lowerCAmelCase      :					float = 0.02 ,      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:
																	lowercase       :    Optional[Any]											=  torch.tensor(lowerCAmelCase ,      dtype=torch.floataa					)
											elif beta_schedule == "linear":
																	lowercase       :    Dict											=  torch.linspace(lowerCAmelCase ,      lowerCAmelCase ,      lowerCAmelCase ,      dtype=torch.floataa					)
											elif beta_schedule == "scaled_linear":
																	# this schedule is very specific to the latent diffusion model.
																	lowercase       :    List[str]											=  (
																	    torch.linspace(beta_start**0.5 ,      beta_end**0.5 ,      lowerCAmelCase ,      dtype=torch.floataa					) ** 2
																	)
											elif beta_schedule == "squaredcos_cap_v2":
																	# Glide cosine schedule
																	lowercase       :    int											=  betas_for_alpha_bar(lowerCAmelCase					)
											else:
																	raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''					)

											lowercase       :    List[Any]											=  1.0 - self.betas
											lowercase       :    Optional[Any]											=  torch.cumprod(self.alphas ,      dim=0					)

											lowercase       :    List[Any]											=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
											lowercase       :    Tuple											=  np.concatenate([sigmas[::-1], [0.0]]					).astype(np.floataa					)
											lowercase       :    int											=  torch.from_numpy(lowerCAmelCase					)

											# setable values
											lowercase       :    Dict											=  None
											lowercase       :    Union[str, Any]											=  np.linspace(0 ,      num_train_timesteps - 1 ,      lowerCAmelCase ,      dtype=lowerCAmelCase					)[::-1].copy()
											lowercase       :    Union[str, Any]											=  torch.from_numpy(lowerCAmelCase					)
											lowercase       :    Any											=  False

					@property
					def        _lowerCAmelCase	(			self      :					Union[str, 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      :					Union[str, Any] ,      lowerCAmelCase      :					torch.FloatTensor ,      lowerCAmelCase      :					Union[float, torch.FloatTensor]					):
											if isinstance(lowerCAmelCase ,      torch.Tensor					):
																	lowercase       :    Union[str, Any]											=  timestep.to(self.timesteps.device					)
											lowercase       :    List[str]											=  (self.timesteps == timestep).nonzero().item()
											lowercase       :    List[str]											=  self.sigmas[step_index]
											lowercase       :    Optional[Any]											=  sample / ((sigma**2 + 1) ** 0.5)
											lowercase       :    str											=  True
											return sample

					def        _lowerCAmelCase	(			self      :					Optional[int] ,      lowerCAmelCase      :					int ,      lowerCAmelCase      :					Union[str, torch.device] = None					):
											lowercase       :    str											=  num_inference_steps

											# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
											if self.config.timestep_spacing == "linspace":
																	lowercase       :    Optional[Any]											=  np.linspace(0 ,      self.config.num_train_timesteps - 1 ,      lowerCAmelCase ,      dtype=lowerCAmelCase					)[
																	    ::-1
																	].copy()
											elif self.config.timestep_spacing == "leading":
																	lowercase       :    Union[str, Any]											=  self.config.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
																	lowercase       :    Optional[Any]											=  (np.arange(0 ,      lowerCAmelCase					) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase					)
																	timesteps += self.config.steps_offset
											elif self.config.timestep_spacing == "trailing":
																	lowercase       :    Dict											=  self.config.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
																	lowercase       :    Dict											=  (np.arange(self.config.num_train_timesteps ,      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\'.'''					)

											lowercase       :    Any											=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
											lowercase       :    List[Any]											=  np.interp(lowerCAmelCase ,      np.arange(0 ,      len(lowerCAmelCase					)					) ,      lowerCAmelCase					)
											lowercase       :    int											=  np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
											lowercase       :    Dict											=  torch.from_numpy(lowerCAmelCase					).to(device=lowerCAmelCase					)
											if str(lowerCAmelCase					).startswith('''mps'''					):
																	# mps does not support float64
																	lowercase       :    Optional[int]											=  torch.from_numpy(lowerCAmelCase					).to(lowerCAmelCase ,      dtype=torch.floataa					)
											else:
																	lowercase       :    Union[str, Any]											=  torch.from_numpy(lowerCAmelCase					).to(device=lowerCAmelCase					)

					def        _lowerCAmelCase	(			self      :					int ,      lowerCAmelCase      :					torch.FloatTensor ,      lowerCAmelCase      :					Union[float, torch.FloatTensor] ,      lowerCAmelCase      :					torch.FloatTensor ,      lowerCAmelCase      :					Optional[torch.Generator] = None ,      lowerCAmelCase      :					bool = True ,      ):

											if (
											    isinstance(lowerCAmelCase ,      lowerCAmelCase					)
											    or isinstance(lowerCAmelCase ,      torch.IntTensor					)
											    or isinstance(lowerCAmelCase ,      torch.LongTensor					)
											):
																	raise ValueError(
																	    (
																	        '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
																	        ''' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'''
																	        ''' one of the `scheduler.timesteps` as a timestep.'''
																	    ) ,      )

											if not self.is_scale_input_called:
																	logger.warning(
																	    '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
																	    '''See `StableDiffusionPipeline` for a usage example.'''					)

											if isinstance(lowerCAmelCase ,      torch.Tensor					):
																	lowercase       :    str											=  timestep.to(self.timesteps.device					)

											lowercase       :    Union[str, Any]											=  (self.timesteps == timestep).nonzero().item()
											lowercase       :    Optional[int]											=  self.sigmas[step_index]

											# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
											if self.config.prediction_type == "epsilon":
																	lowercase       :    List[str]											=  sample - sigma * model_output
											elif self.config.prediction_type == "v_prediction":
																	# * c_out + input * c_skip
																	lowercase       :    str											=  model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**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`'''					)

											lowercase       :    Dict											=  self.sigmas[step_index]
											lowercase       :    Optional[Any]											=  self.sigmas[step_index + 1]
											lowercase       :    List[str]											=  (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
											lowercase       :    int											=  (sigma_to**2 - sigma_up**2) ** 0.5

											# 2. Convert to an ODE derivative
											lowercase       :    int											=  (sample - pred_original_sample) / sigma

											lowercase       :    str											=  sigma_down - sigma

											lowercase       :    Dict											=  sample + derivative * dt

											lowercase       :    Union[str, Any]											=  model_output.device
											lowercase       :    List[Any]											=  randn_tensor(model_output.shape ,      dtype=model_output.dtype ,      device=lowerCAmelCase ,      generator=lowerCAmelCase					)

											lowercase       :    Optional[Any]											=  prev_sample + noise * sigma_up

											if not return_dict:
																	return (prev_sample,)

											return EulerAncestralDiscreteSchedulerOutput(
											    prev_sample=lowerCAmelCase ,      pred_original_sample=lowerCAmelCase					)

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

											lowercase       :    Dict											=  [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

											lowercase       :    List[str]											=  original_samples + noise * sigma
											return noisy_samples




					def __len__(			self      :					Union[str, Any]					):
											return self.config.num_train_timesteps




class 							UpperCAmelCase	:

					def __init__(			self      :					Any ,      lowerCAmelCase      :					int					):
											lowercase       :    Tuple											=  data
											lowercase       :    BinaryTreeNode | None											=  None
											lowercase       :    BinaryTreeNode | None											=  None





def        lowerCamelCase_      (			UpperCAmelCase_							:				BinaryTreeNode | None							,			UpperCAmelCase_							:				int       ):
						if node is None:
												lowercase       :    Union[str, Any]											=  BinaryTreeNode(UpperCAmelCase_       )
												return node

						# binary search tree is not empty,
						# so we will insert it into the tree
						# if new_value is less than value of data in node,
						#  add it to left subtree and proceed recursively
						if new_value < node.data:
												lowercase       :    List[Any]											=  insert(node.left_child							,			UpperCAmelCase_       )
						else:
												# if new_value is greater than value of data in node,
												#  add it to right subtree and proceed recursively
												lowercase       :    Dict											=  insert(node.right_child							,			UpperCAmelCase_       )
						return node

def        lowerCamelCase_      (			UpperCAmelCase_							:				None | BinaryTreeNode       ):  # if node is None,return
						if node:
												lowercase       :    Optional[Any]											=  inorder(node.left_child       )
												lowercase       :    Tuple											=  [*inorder_array, node.data]
												lowercase       :    Optional[Any]											=  inorder_array + inorder(node.right_child       )
						else:
												lowercase       :    Optional[int]											=  []
						return inorder_array

def        lowerCamelCase_      (			):
						lowercase       :    Any											=  insert(UpperCAmelCase_							,			15       )
						insert(UpperCAmelCase_							,			10       )
						insert(UpperCAmelCase_							,			25       )
						insert(UpperCAmelCase_							,			6       )
						insert(UpperCAmelCase_							,			14       )
						insert(UpperCAmelCase_							,			20       )
						insert(UpperCAmelCase_							,			60       )
						return root

def        lowerCamelCase_      (			):
						# main function
						lowercase       :    Any											=  make_tree()
						print('''Printing values of binary search tree in Inorder Traversal.'''       )
						inorder(UpperCAmelCase_       )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()


