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

import numpy as np
import torch

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





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    42
			UpperCamelCase__          =    None







def   lowerCAmelCase_						(					_snake_case      :  Dict      ,   _snake_case      :  List[str]=0.999      ,   _snake_case      :  Optional[Any]="cosine"      ,   )				->		Union[str, Any]:







	'''simple docstring'''


	if alpha_transform_type == "cosine":

		def alpha_bar_fn(_snake_case      :  Optional[int]   ):
			return math.cos((t + 0.008) / 1.008 * math.pi / 2   ) ** 2

	elif alpha_transform_type == "exp":

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

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

	__magic_name__		:		Tuple	   =     []
	for i in range(_snake_case   ):
		__magic_name__		:		List[str]	   =     i / num_diffusion_timesteps
		__magic_name__		:		Optional[int]	   =     (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       _snake_case					(     snake_case				,						snake_case				):







			@register_to_config
			def __init__(				self						,   _a = 1_000						,   _a = "fixed_small_log"						,   _a = True						,   _a = 1.0						,   _a = "epsilon"						,   _a = "squaredcos_cap_v2"						,   ):
				if beta_schedule != "squaredcos_cap_v2":
					raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"		)

				__magic_name__		:		Dict	   =     betas_for_alpha_bar(_a		)

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

				# standard deviation of the initial noise distribution
				__magic_name__		:		Any	   =     1.0

				# setable values
				__magic_name__		:		int	   =     None
				__magic_name__		:		Union[str, Any]	   =     torch.from_numpy(np.arange(0						,   _a		)[::-1].copy()		)

				__magic_name__		:		Any	   =     variance_type







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):
				return sample







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):
				__magic_name__		:		Any	   =     num_inference_steps
				__magic_name__		:		str	   =     (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
				__magic_name__		:		Any	   =     (np.arange(0						,   _a		) * step_ratio).round()[::-1].copy().astype(np.intaa		)
				__magic_name__		:		Optional[Any]	   =     torch.from_numpy(_a		).to(_a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a=None						,   _a=None						,   _a=None		):
				if prev_timestep is None:
					__magic_name__		:		Union[str, Any]	   =     t - 1

				__magic_name__		:		Optional[int]	   =     self.alphas_cumprod[t]
				__magic_name__		:		str	   =     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
				__magic_name__		:		Union[str, Any]	   =     1 - alpha_prod_t
				__magic_name__		:		int	   =     1 - alpha_prod_t_prev

				if prev_timestep == t - 1:
					__magic_name__		:		Dict	   =     self.betas[t]
				else:
					__magic_name__		:		Dict	   =     1 - alpha_prod_t / alpha_prod_t_prev

				# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
				# and sample from it to get previous sample
				# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
				__magic_name__		:		List[str]	   =     beta_prod_t_prev / beta_prod_t * beta

				if variance_type is None:
					__magic_name__		:		Any	   =     self.config.variance_type

				# hacks - were probably added for training stability
				if variance_type == "fixed_small_log":
					__magic_name__		:		Any	   =     torch.log(torch.clamp(_a						,   min=1e-20		)		)
					__magic_name__		:		Optional[int]	   =     torch.exp(0.5 * variance		)
				elif variance_type == "learned_range":
					# NOTE difference with DDPM scheduler
					__magic_name__		:		Any	   =     variance.log()
					__magic_name__		:		str	   =     beta.log()

					__magic_name__		:		List[str]	   =     (predicted_variance + 1) / 2
					__magic_name__		:		str	   =     frac * max_log + (1 - frac) * min_log

				return variance







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a						,   _a = None						,   _a=None						,   _a = True						,   ):
				__magic_name__		:		Tuple	   =     timestep

				if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
					__magic_name__   ,    __magic_name__		:		Optional[int]	   =     torch.split(_a						,   sample.shape[1]						,   dim=1		)
				else:
					__magic_name__		:		Tuple	   =     None

				# 1. compute alphas, betas
				if prev_timestep is None:
					__magic_name__		:		Optional[Any]	   =     t - 1

				__magic_name__		:		Tuple	   =     self.alphas_cumprod[t]
				__magic_name__		:		List[str]	   =     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
				__magic_name__		:		List[Any]	   =     1 - alpha_prod_t
				__magic_name__		:		int	   =     1 - alpha_prod_t_prev

				if prev_timestep == t - 1:
					__magic_name__		:		List[str]	   =     self.betas[t]
					__magic_name__		:		List[str]	   =     self.alphas[t]
				else:
					__magic_name__		:		Optional[int]	   =     1 - alpha_prod_t / alpha_prod_t_prev
					__magic_name__		:		List[Any]	   =     1 - beta

				# 2. compute predicted original sample from predicted noise also called
				# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
				if self.config.prediction_type == "epsilon":
					__magic_name__		:		Dict	   =     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
				elif self.config.prediction_type == "sample":
					__magic_name__		:		Dict	   =     model_output
				else:
					raise ValueError(
					    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
					    " for the UnCLIPScheduler."		)

				# 3. Clip "predicted x_0"
				if self.config.clip_sample:
					__magic_name__		:		Any	   =     torch.clamp(
					    _a						,   -self.config.clip_sample_range						,   self.config.clip_sample_range		)

				# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
				# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
				__magic_name__		:		Optional[int]	   =     (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
				__magic_name__		:		str	   =     alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

				# 5. Compute predicted previous sample µ_t
				# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
				__magic_name__		:		Dict	   =     pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

				# 6. Add noise
				__magic_name__		:		str	   =     0
				if t > 0:
					__magic_name__		:		Union[str, Any]	   =     randn_tensor(
					    model_output.shape						,   dtype=model_output.dtype						,   generator=_a						,   device=model_output.device		)

					__magic_name__		:		Tuple	   =     self._get_variance(
					    _a						,   predicted_variance=_a						,   prev_timestep=_a						,   )

					if self.variance_type == "fixed_small_log":
						__magic_name__		:		Tuple	   =     variance
					elif self.variance_type == "learned_range":
						__magic_name__		:		Tuple	   =     (0.5 * variance).exp()
					else:
						raise ValueError(
						    f'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
						    " for the UnCLIPScheduler."		)

					__magic_name__		:		str	   =     variance * variance_noise

				__magic_name__		:		int	   =     pred_prev_sample + variance

				if not return_dict:
					return (pred_prev_sample,)

				return UnCLIPSchedulerOutput(prev_sample=_a						,   pred_original_sample=_a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a						,   ):
				# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
				__magic_name__		:		str	   =     self.alphas_cumprod.to(device=original_samples.device						,   dtype=original_samples.dtype		)
				__magic_name__		:		List[Any]	   =     timesteps.to(original_samples.device		)

				__magic_name__		:		int	   =     alphas_cumprod[timesteps] ** 0.5
				__magic_name__		:		List[str]	   =     sqrt_alpha_prod.flatten()
				while len(sqrt_alpha_prod.shape		) < len(original_samples.shape		):
					__magic_name__		:		Optional[int]	   =     sqrt_alpha_prod.unsqueeze(-1		)

				__magic_name__		:		int	   =     (1 - alphas_cumprod[timesteps]) ** 0.5
				__magic_name__		:		str	   =     sqrt_one_minus_alpha_prod.flatten()
				while len(sqrt_one_minus_alpha_prod.shape		) < len(original_samples.shape		):
					__magic_name__		:		int	   =     sqrt_one_minus_alpha_prod.unsqueeze(-1		)

				__magic_name__		:		List[str]	   =     sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
				return noisy_samples


from __future__ import annotations





class       _snake_case					:







			def __init__(				self						,   _a		):
				__magic_name__		:		Optional[Any]	   =     data
				__magic_name__		:		Node | None	   =     None
				__magic_name__		:		Node | None	   =     None







def   lowerCAmelCase_						(					_snake_case      :  Node | None   )				->		None:  # In Order traversal of the tree







	'''simple docstring'''


	if tree:
		display(tree.left   )
		print(tree.data   )
		display(tree.right   )






def   lowerCAmelCase_						(					_snake_case      :  Node | None   )				->		int:







	'''simple docstring'''


	return 1 + max(depth_of_tree(tree.left   )      ,   depth_of_tree(tree.right   )   ) if tree else 0






def   lowerCAmelCase_						(					_snake_case      :  Node   )				->		bool:







	'''simple docstring'''


	if not tree:
		return True
	if tree.left and tree.right:
		return is_full_binary_tree(tree.left   ) and is_full_binary_tree(tree.right   )
	else:
		return not tree.left and not tree.right






def   lowerCAmelCase_						(					)				->		None:  # Main function for testing.







	'''simple docstring'''


	__magic_name__		:		int	   =     Node(1   )
	__magic_name__		:		Union[str, Any]	   =     Node(2   )
	__magic_name__		:		Tuple	   =     Node(3   )
	__magic_name__		:		Optional[Any]	   =     Node(4   )
	__magic_name__		:		Union[str, Any]	   =     Node(5   )
	__magic_name__		:		Any	   =     Node(6   )
	__magic_name__		:		int	   =     Node(7   )
	__magic_name__		:		List[str]	   =     Node(8   )
	__magic_name__		:		Union[str, Any]	   =     Node(9   )

	print(is_full_binary_tree(_snake_case   )   )
	print(depth_of_tree(_snake_case   )   )
	print("Tree is: "   )
	display(_snake_case   )


if __name__ == "__main__":
				main()
