
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class 			_lowerCAmelCase      (			__a ):
				_lowercase    	=42
				_lowercase    	=None






def      lowerCamelCase__       (       __lowerCAmelCase						:						Union[str, Any]					,							__lowerCAmelCase						:						Optional[Any]=0.999					,							__lowerCAmelCase						:						Union[str, Any]="cosine"					,							):




			"""simple docstring"""


			if alpha_transform_type == "cosine":

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

			elif alpha_transform_type == "exp":

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

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

			lowerCAmelCase_      			=			[]
			for i in range(__lowerCAmelCase       ):
						lowerCAmelCase_      			=			i / num_diffusion_timesteps
						lowerCAmelCase_      			=			(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 			_lowerCAmelCase      (			__a      ,   __a ):
				_lowercase    	=1




				@register_to_config
				def __init__(      self       ,       _UpperCamelCase = 1_000       ,       _UpperCamelCase = 0.0001       ,       _UpperCamelCase = 0.02       ,       _UpperCamelCase = "linear"       ,       _UpperCamelCase = None       ,       _UpperCamelCase = True       ,       _UpperCamelCase = True       ,       _UpperCamelCase = 0       ,       _UpperCamelCase = "epsilon"       ,       _UpperCamelCase = 1.0       ,       **_UpperCamelCase       ,       )       ->   str:
							if kwargs.get("set_alpha_to_one"       ,       _UpperCamelCase  ) is not None:
										lowerCAmelCase_      			=			(
										    "The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
										)
										deprecate("set_alpha_to_one"       ,       "1.0.0"       ,       _UpperCamelCase       ,       standard_warn=_UpperCamelCase  )
										lowerCAmelCase_      			=			kwargs["set_alpha_to_one"]
							if trained_betas is not None:
										lowerCAmelCase_      			=			torch.tensor(_UpperCamelCase       ,       dtype=torch.floataa  )
							elif beta_schedule == "linear":
										lowerCAmelCase_      			=			torch.linspace(_UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       dtype=torch.floataa  )
							elif beta_schedule == "scaled_linear":
										# this schedule is very specific to the latent diffusion model.
										lowerCAmelCase_      			=			(
										    torch.linspace(beta_start**0.5       ,       beta_end**0.5       ,       _UpperCamelCase       ,       dtype=torch.floataa  ) ** 2
										)
							elif beta_schedule == "squaredcos_cap_v2":
										# Glide cosine schedule
										lowerCAmelCase_      			=			betas_for_alpha_bar(_UpperCamelCase  )
							else:
										raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""  )

							lowerCAmelCase_      			=			1.0 - self.betas
							lowerCAmelCase_      			=			torch.cumprod(self.alphas       ,       dim=0  )

							# At every step in inverted ddim, we are looking into the next alphas_cumprod
							# For the final step, there is no next alphas_cumprod, and the index is out of bounds
							# `set_alpha_to_zero` decides whether we set this parameter simply to zero
							# in this case, self.step() just output the predicted noise
							# or whether we use the final alpha of the "non-previous" one.
							lowerCAmelCase_      			=			torch.tensor(0.0  ) if set_alpha_to_zero else self.alphas_cumprod[-1]

							# standard deviation of the initial noise distribution
							lowerCAmelCase_      			=			1.0

							# setable values
							lowerCAmelCase_      			=			None
							lowerCAmelCase_      			=			torch.from_numpy(np.arange(0       ,       _UpperCamelCase  ).copy().astype(np.intaa  )  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = None  )       ->   torch.FloatTensor:
							return sample




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = None  )       ->   Optional[Any]:

							if num_inference_steps > self.config.num_train_timesteps:
										raise ValueError(
										    f"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
										    f""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
										    f""" maximal {self.config.num_train_timesteps} timesteps."""  )

							lowerCAmelCase_      			=			num_inference_steps
							lowerCAmelCase_      			=			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
							lowerCAmelCase_      			=			(np.arange(0       ,       _UpperCamelCase  ) * step_ratio).round().copy().astype(np.intaa  )
							lowerCAmelCase_      			=			torch.from_numpy(_UpperCamelCase  ).to(_UpperCamelCase  )
							self.timesteps += self.config.steps_offset




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase = 0.0       ,       _UpperCamelCase = False       ,       _UpperCamelCase = None       ,       _UpperCamelCase = True       ,       )       ->   Union[DDIMSchedulerOutput, Tuple]:
							# 1. get previous step value (=t+1)
							lowerCAmelCase_      			=			timestep + self.config.num_train_timesteps // self.num_inference_steps

							# 2. compute alphas, betas
							# change original implementation to exactly match noise levels for analogous forward process
							lowerCAmelCase_      			=			self.alphas_cumprod[timestep]
							lowerCAmelCase_      			=			(
							    self.alphas_cumprod[prev_timestep]
							    if prev_timestep < self.config.num_train_timesteps
							    else self.final_alpha_cumprod
							)

							lowerCAmelCase_      			=			1 - alpha_prod_t

							# 3. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							if self.config.prediction_type == "epsilon":
										lowerCAmelCase_      			=			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
										lowerCAmelCase_      			=			model_output
							elif self.config.prediction_type == "sample":
										lowerCAmelCase_      			=			model_output
										lowerCAmelCase_      			=			(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
							elif self.config.prediction_type == "v_prediction":
										lowerCAmelCase_      			=			(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
										lowerCAmelCase_      			=			(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
							else:
										raise ValueError(
										    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
										    " `v_prediction`"  )

							# 4. Clip or threshold "predicted x_0"
							if self.config.clip_sample:
										lowerCAmelCase_      			=			pred_original_sample.clamp(
										    -self.config.clip_sample_range       ,       self.config.clip_sample_range  )

							# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							lowerCAmelCase_      			=			(1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

							# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							lowerCAmelCase_      			=			alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

							if not return_dict:
										return (prev_sample, pred_original_sample)
							return DDIMSchedulerOutput(prev_sample=_UpperCamelCase       ,       pred_original_sample=_UpperCamelCase  )




				def __len__(      self  )       ->   Optional[int]:
							return self.config.num_train_timesteps





from collections.abc import Iterable
from typing import Any





class 			_lowerCAmelCase      :




				def __init__(      self       ,       _UpperCamelCase = None  )       ->   str:
							lowerCAmelCase_      			=			value
							lowerCAmelCase_      			=			None  # Added in order to delete a node easier
							lowerCAmelCase_      			=			None
							lowerCAmelCase_      			=			None




				def __repr__(      self  )       ->   str:
							from pprint import pformat

							if self.left is None and self.right is None:
										return str(self.value  )
							return pformat({f"""{self.value}""": (self.left, self.right)}       ,       indent=1  )





class 			_lowerCAmelCase      :




				def __init__(      self       ,       _UpperCamelCase = None  )       ->   Union[str, Any]:
							lowerCAmelCase_      			=			root




				def __str__(      self  )       ->   str:
							return str(self.root  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   None:
							if new_children is not None:  # reset its kids
										lowerCAmelCase_      			=			node.parent
							if node.parent is not None:  # reset its parent
										if self.is_right(_UpperCamelCase  ):  # If it is the right children
													lowerCAmelCase_      			=			new_children
										else:
													lowerCAmelCase_      			=			new_children
							else:
										lowerCAmelCase_      			=			new_children




				def   __a		(      self       ,       _UpperCamelCase  )       ->   bool:
							if node.parent and node.parent.right:
										return node == node.parent.right
							return False




				def   __a		(      self  )       ->   bool:
							return self.root is None




				def   __a		(      self       ,       _UpperCamelCase  )       ->   None:
							lowerCAmelCase_      			=			Node(_UpperCamelCase  )  # create a new Node
							if self.empty():  # if Tree is empty
										lowerCAmelCase_      			=			new_node  # set its root
							else:  # Tree is not empty
										lowerCAmelCase_      			=			self.root  # from root
										if parent_node is None:
													return
										while True:  # While we don't get to a leaf
													if value < parent_node.value:  # We go left
																if parent_node.left is None:
																			lowerCAmelCase_      			=			new_node  # We insert the new node in a leaf
																			break
																else:
																			lowerCAmelCase_      			=			parent_node.left
													else:
																if parent_node.right is None:
																			lowerCAmelCase_      			=			new_node
																			break
																else:
																			lowerCAmelCase_      			=			parent_node.right
										lowerCAmelCase_      			=			parent_node




				def   __a		(      self       ,       *_UpperCamelCase  )       ->   None:
							for value in values:
										self.__insert(_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Node | None:
							if self.empty():
										raise IndexError("Warning: Tree is empty! please use another."  )
							else:
										lowerCAmelCase_      			=			self.root
										# use lazy evaluation here to avoid NoneType Attribute error
										while node is not None and node.value is not value:
													lowerCAmelCase_      			=			node.left if value < node.value else node.right
										return node




				def   __a		(      self       ,       _UpperCamelCase = None  )       ->   Node | None:
							if node is None:
										if self.root is None:
													return None
										lowerCAmelCase_      			=			self.root

							if not self.empty():
										while node.right is not None:
													lowerCAmelCase_      			=			node.right
							return node




				def   __a		(      self       ,       _UpperCamelCase = None  )       ->   Node | None:
							if node is None:
										lowerCAmelCase_      			=			self.root
							if self.root is None:
										return None
							if not self.empty():
										lowerCAmelCase_      			=			self.root
										while node.left is not None:
													lowerCAmelCase_      			=			node.left
							return node




				def   __a		(      self       ,       _UpperCamelCase  )       ->   None:
							lowerCAmelCase_      			=			self.search(_UpperCamelCase  )  # Look for the node with that label
							if node is not None:
										if node.left is None and node.right is None:  # If it has no children
													self.__reassign_nodes(_UpperCamelCase       ,       _UpperCamelCase  )
										elif node.left is None:  # Has only right children
													self.__reassign_nodes(_UpperCamelCase       ,       node.right  )
										elif node.right is None:  # Has only left children
													self.__reassign_nodes(_UpperCamelCase       ,       node.left  )
										else:
													lowerCAmelCase_      			=			self.get_max(
													    node.left  )  # Gets the max value of the left branch
													self.remove(tmp_node.value  )  # type: ignore
													lowerCAmelCase_      			=			(
													    tmp_node.value  # type: ignore
													)  # Assigns the value to the node to delete and keep tree structure




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Iterable:
							if node is not None:
										yield node  # Preorder Traversal
										yield from self.preorder_traverse(node.left  )
										yield from self.preorder_traverse(node.right  )




				def   __a		(      self       ,       _UpperCamelCase=None  )       ->   Any:
							if traversal_function is None:
										return self.preorder_traverse(self.root  )
							else:
										return traversal_function(self.root  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   None:
							if node:
										self.inorder(_UpperCamelCase       ,       node.left  )
										arr.append(node.value  )
										self.inorder(_UpperCamelCase       ,       node.right  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
							lowerCAmelCase_      			=			[]
							self.inorder(_UpperCamelCase       ,       _UpperCamelCase  )  # append all values to list using inorder traversal
							return arr[k - 1]






def      lowerCamelCase__       (       __lowerCAmelCase						:						Node | None       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			[]
			if curr_node is not None:
						lowerCAmelCase_      			=			postorder(curr_node.left       ) + postorder(curr_node.right       ) + [curr_node]
			return node_list






def      lowerCamelCase__       (       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			(8, 3, 6, 1, 10, 14, 13, 4, 7)
			lowerCAmelCase_      			=			BinarySearchTree()
			for i in testlist:
						t.insert(__lowerCAmelCase       )

			# Prints all the elements of the list in order traversal
			print(__lowerCAmelCase       )

			if t.search(6       ) is not None:
						print("The value 6 exists"       )
			else:
						print("The value 6 doesn't exist"       )

			if t.search(-1       ) is not None:
						print("The value -1 exists"       )
			else:
						print("The value -1 doesn't exist"       )

			if not t.empty():
						print("Max Value: "					,							t.get_max().value       )  # type: ignore
						print("Min Value: "					,							t.get_min().value       )  # type: ignore

			for i in testlist:
						t.remove(__lowerCAmelCase       )
						print(__lowerCAmelCase       )


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)



