






"""simple docstring"""






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_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							torch.FloatTensor
	SCREAMING_SNAKE_CASE_:							Optional[torch.FloatTensor]   			=					None





def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__=0.9_9_9       ,lowerCAmelCase__="cosine"       ,):
				if alpha_transform_type == "cosine":

								def alpha_bar_fn(lowerCAmelCase__				):
												return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2				) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(lowerCAmelCase__				):
												return math.exp(t * -1_2.0				)

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

				A__							 =       []
				for i in range(lowerCAmelCase__				):
								A__							 =       i / num_diffusion_timesteps
								A__							 =       (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       snake_case_       (    _lowerCamelCase    ,							_lowerCamelCase       ):



	"""simple docstring"""

	@register_to_config
	def __init__(      self  ,					__a = 1000  ,					__a = "fixed_small_log"  ,					__a = True  ,					__a = 1.0  ,					__a = "epsilon"  ,					__a = "squaredcos_cap_v2"  ,					):


					"""simple docstring"""

					if beta_schedule != "squaredcos_cap_v2":
									raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''						)

					A__							 =       betas_for_alpha_bar(__a						)

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

					# standard deviation of the initial noise distribution
					A__							 =       1.0

					# setable values
					A__							 =       None
					A__							 =       torch.from_numpy(np.arange(0  ,					__a						)[::-1].copy()						)

					A__							 =       variance_type


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a = None						):


					"""simple docstring"""

					return sample


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a = None						):


					"""simple docstring"""

					A__							 =       num_inference_steps
					A__							 =       (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
					A__							 =       (np.arange(0  ,					__a						) * step_ratio).round()[::-1].copy().astype(np.intaa						)
					A__							 =       torch.from_numpy(__a						).to(__a						)


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a=None  ,					__a=None  ,					__a=None						):


					"""simple docstring"""

					if prev_timestep is None:
									A__							 =       t - 1

					A__							 =       self.alphas_cumprod[t]
					A__							 =       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
					A__							 =       1 - alpha_prod_t
					A__							 =       1 - alpha_prod_t_prev

					if prev_timestep == t - 1:
									A__							 =       self.betas[t]
					else:
									A__							 =       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
					A__							 =       beta_prod_t_prev / beta_prod_t * beta

					if variance_type is None:
									A__							 =       self.config.variance_type

					# hacks - were probably added for training stability
					if variance_type == "fixed_small_log":
									A__							 =       torch.log(torch.clamp(__a  ,					min=1E-20						)						)
									A__							 =       torch.exp(0.5 * variance						)
					elif variance_type == "learned_range":
									# NOTE difference with DDPM scheduler
									A__							 =       variance.log()
									A__							 =       beta.log()

									A__							 =       (predicted_variance + 1) / 2
									A__							 =       frac * max_log + (1 - frac) * min_log

					return variance


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a  ,					__a = None  ,					__a=None  ,					__a = True  ,					):


					"""simple docstring"""

					A__							 =       timestep

					if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
									A__ ,			A__							 =       torch.split(__a  ,					sample.shape[1]  ,					dim=1						)
					else:
									A__							 =       None

					# 1. compute alphas, betas
					if prev_timestep is None:
									A__							 =       t - 1

					A__							 =       self.alphas_cumprod[t]
					A__							 =       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
					A__							 =       1 - alpha_prod_t
					A__							 =       1 - alpha_prod_t_prev

					if prev_timestep == t - 1:
									A__							 =       self.betas[t]
									A__							 =       self.alphas[t]
					else:
									A__							 =       1 - alpha_prod_t / alpha_prod_t_prev
									A__							 =       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":
									A__							 =       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
					elif self.config.prediction_type == "sample":
									A__							 =       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:
									A__							 =       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
					A__							 =       (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
					A__							 =       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
					A__							 =       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

					# 6. Add noise
					A__							 =       0
					if t > 0:
									A__							 =       randn_tensor(
									    model_output.shape  ,					dtype=model_output.dtype  ,					generator=__a  ,					device=model_output.device						)

									A__							 =       self._get_variance(
									    __a  ,					predicted_variance=__a  ,					prev_timestep=__a  ,					)

									if self.variance_type == "fixed_small_log":
													A__							 =       variance
									elif self.variance_type == "learned_range":
													A__							 =       (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.'						)

									A__							 =       variance * variance_noise

					A__							 =       pred_prev_sample + variance

					if not return_dict:
									return (pred_prev_sample,)

					return UnCLIPSchedulerOutput(prev_sample=__a  ,					pred_original_sample=__a						)






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a  ,					):


					"""simple docstring"""

					A__							 =       self.alphas_cumprod.to(device=original_samples.device  ,					dtype=original_samples.dtype						)
					A__							 =       timesteps.to(original_samples.device						)

					A__							 =       alphas_cumprod[timesteps] ** 0.5
					A__							 =       sqrt_alpha_prod.flatten()
					while len(sqrt_alpha_prod.shape						) < len(original_samples.shape						):
									A__							 =       sqrt_alpha_prod.unsqueeze(-1						)

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

					A__							 =       sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
					return noisy_samples










"""simple docstring"""






def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				return 1 if input_a == input_a else 0



def  __lowerCamelCase					(					):
				assert xnor_gate(0       ,0				) == 1
				assert xnor_gate(0       ,1				) == 0
				assert xnor_gate(1       ,0				) == 0
				assert xnor_gate(1       ,1				) == 1


if __name__ == "__main__":
					print(xnor_gate(0, 0))
					print(xnor_gate(0, 1))
					print(xnor_gate(1, 0))
					print(xnor_gate(1, 1))


