



"""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 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	42
	SCREAMING_SNAKE_CASE          =	None






def 			_lowercase				(  __snake_case					,__snake_case=0.999					,__snake_case="cosine"					,)						->   List[Any]:
			if alpha_transform_type == "cosine":

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

			elif alpha_transform_type == "exp":

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

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

			__lowerCAmelCase      :  str												=       []
			for i in range(__snake_case	):
						__lowerCAmelCase      :  Tuple												=       i / num_diffusion_timesteps
						__lowerCAmelCase      :  str												=       (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 			A__   (  __SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	@register_to_config
	def __init__(     self:		str       ,			_SCREAMING_SNAKE_CASE:		int = 1000       ,			_SCREAMING_SNAKE_CASE:		str = "fixed_small_log"       ,			_SCREAMING_SNAKE_CASE:		bool = True       ,			_SCREAMING_SNAKE_CASE:		Optional[float] = 1.0       ,			_SCREAMING_SNAKE_CASE:		str = "epsilon"       ,			_SCREAMING_SNAKE_CASE:		str = "squaredcos_cap_v2"       ,			)				->       int:

				"""simple docstring"""

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

				__lowerCAmelCase      :  Any												=       betas_for_alpha_bar(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  Tuple												=       1.0 - self.betas
				__lowerCAmelCase      :  Any												=       torch.cumprod(self.alphas       ,			dim=0)
				__lowerCAmelCase      :  Union[str, Any]												=       torch.tensor(1.0)

				# standard deviation of the initial noise distribution
				__lowerCAmelCase      :  Optional[int]												=       1.0

				# setable values
				__lowerCAmelCase      :  Optional[int]												=       None
				__lowerCAmelCase      :  Optional[int]												=       torch.from_numpy(np.arange(0       ,			_SCREAMING_SNAKE_CASE)[::-1].copy())

				__lowerCAmelCase      :  Union[str, Any]												=       variance_type

	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict       ,			_SCREAMING_SNAKE_CASE:		torch.FloatTensor       ,			_SCREAMING_SNAKE_CASE:		Optional[int] = None)				->       torch.FloatTensor:

				"""simple docstring"""

				return sample

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str]       ,			_SCREAMING_SNAKE_CASE:		int       ,			_SCREAMING_SNAKE_CASE:		Union[str, torch.device] = None)				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       num_inference_steps
				__lowerCAmelCase      :  Dict												=       (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
				__lowerCAmelCase      :  List[str]												=       (np.arange(0       ,			_SCREAMING_SNAKE_CASE) * step_ratio).round()[::-1].copy().astype(np.intaa)
				__lowerCAmelCase      :  Optional[int]												=       torch.from_numpy(_SCREAMING_SNAKE_CASE).to(_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any]       ,			_SCREAMING_SNAKE_CASE:		int       ,			_SCREAMING_SNAKE_CASE:		Dict=None       ,			_SCREAMING_SNAKE_CASE:		int=None       ,			_SCREAMING_SNAKE_CASE:		str=None)				->       Union[str, Any]:

				"""simple docstring"""

				if prev_timestep is None:
							__lowerCAmelCase      :  int												=       t - 1

				__lowerCAmelCase      :  Dict												=       self.alphas_cumprod[t]
				__lowerCAmelCase      :  Optional[int]												=       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
				__lowerCAmelCase      :  List[str]												=       1 - alpha_prod_t
				__lowerCAmelCase      :  List[str]												=       1 - alpha_prod_t_prev

				if prev_timestep == t - 1:
							__lowerCAmelCase      :  Optional[int]												=       self.betas[t]
				else:
							__lowerCAmelCase      :  Optional[int]												=       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
				__lowerCAmelCase      :  List[Any]												=       beta_prod_t_prev / beta_prod_t * beta

				if variance_type is None:
							__lowerCAmelCase      :  int												=       self.config.variance_type

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

							__lowerCAmelCase      :  str												=       (predicted_variance + 1) / 2
							__lowerCAmelCase      :  Optional[int]												=       frac * max_log + (1 - frac) * min_log

				return variance

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple       ,			_SCREAMING_SNAKE_CASE:		torch.FloatTensor       ,			_SCREAMING_SNAKE_CASE:		int       ,			_SCREAMING_SNAKE_CASE:		torch.FloatTensor       ,			_SCREAMING_SNAKE_CASE:		Optional[int] = None       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=None       ,			_SCREAMING_SNAKE_CASE:		bool = True       ,			)				->       Union[UnCLIPSchedulerOutput, Tuple]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       timestep

				if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
							__lowerCAmelCase						,	__lowerCAmelCase      :  Any												=       torch.split(_SCREAMING_SNAKE_CASE       ,			sample.shape[1]       ,			dim=1)
				else:
							__lowerCAmelCase      :  List[str]												=       None

				# 1. compute alphas, betas
				if prev_timestep is None:
							__lowerCAmelCase      :  Union[str, Any]												=       t - 1

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

				if prev_timestep == t - 1:
							__lowerCAmelCase      :  Optional[Any]												=       self.betas[t]
							__lowerCAmelCase      :  int												=       self.alphas[t]
				else:
							__lowerCAmelCase      :  Optional[int]												=       1 - alpha_prod_t / alpha_prod_t_prev
							__lowerCAmelCase      :  Dict												=       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":
							__lowerCAmelCase      :  int												=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
				elif self.config.prediction_type == "sample":
							__lowerCAmelCase      :  str												=       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:
							__lowerCAmelCase      :  Dict												=       torch.clamp(
							    _SCREAMING_SNAKE_CASE       ,			-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
				__lowerCAmelCase      :  int												=       (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
				__lowerCAmelCase      :  List[Any]												=       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
				__lowerCAmelCase      :  List[Any]												=       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

				# 6. Add noise
				__lowerCAmelCase      :  Optional[Any]												=       0
				if t > 0:
							__lowerCAmelCase      :  Any												=       randn_tensor(
							    model_output.shape       ,			dtype=model_output.dtype       ,			generator=_SCREAMING_SNAKE_CASE       ,			device=model_output.device)

							__lowerCAmelCase      :  List[str]												=       self._get_variance(
							    _SCREAMING_SNAKE_CASE       ,			predicted_variance=_SCREAMING_SNAKE_CASE       ,			prev_timestep=_SCREAMING_SNAKE_CASE       ,			)

							if self.variance_type == "fixed_small_log":
										__lowerCAmelCase      :  Tuple												=       variance
							elif self.variance_type == "learned_range":
										__lowerCAmelCase      :  Optional[Any]												=       (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.")

							__lowerCAmelCase      :  Optional[int]												=       variance * variance_noise

				__lowerCAmelCase      :  int												=       pred_prev_sample + variance

				if not return_dict:
							return (pred_prev_sample,)

				return UnCLIPSchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE       ,			pred_original_sample=_SCREAMING_SNAKE_CASE)







	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple       ,			_SCREAMING_SNAKE_CASE:		torch.FloatTensor       ,			_SCREAMING_SNAKE_CASE:		torch.FloatTensor       ,			_SCREAMING_SNAKE_CASE:		torch.IntTensor       ,			)				->       torch.FloatTensor:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       self.alphas_cumprod.to(device=original_samples.device       ,			dtype=original_samples.dtype)
				__lowerCAmelCase      :  Tuple												=       timesteps.to(original_samples.device)

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

				__lowerCAmelCase      :  Union[str, Any]												=       (1 - alphas_cumprod[timesteps]) ** 0.5
				__lowerCAmelCase      :  Dict												=       sqrt_one_minus_alpha_prod.flatten()
				while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
							__lowerCAmelCase      :  Optional[Any]												=       sqrt_one_minus_alpha_prod.unsqueeze(-1)

				__lowerCAmelCase      :  int												=       sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
				return noisy_samples




"""simple docstring"""






import os
from math import logaa


def 			_lowercase				(  __snake_case = "base_exp.txt"	)						->   int:
			__lowerCAmelCase      :  float												=       0
			__lowerCAmelCase      :  Any												=       0
			for i, line in enumerate(open(os.path.join(os.path.dirname(__snake_case	)					,__snake_case	)	)	):
						__lowerCAmelCase						,	__lowerCAmelCase      :  List[str]												=       list(map(__snake_case					,line.split(","	)	)	)
						if x * logaa(__snake_case	) > largest:
									__lowerCAmelCase      :  Tuple												=       x * logaa(__snake_case	)
									__lowerCAmelCase      :  Optional[Any]												=       i + 1
			return result


if __name__ == "__main__":
	print(solution())