




'''simple docstring'''
# Imports
import numpy as np
class   __UpperCAmelCase				:



					'''simple docstring'''





					def __init__(	self     :						Tuple      ,       _lowercase     :						str=None      ,       _lowercase     :						Optional[int]=None      ,       _lowercase     :						Optional[Any]=None      ,       _lowercase     :						List[Any]=None      ,       _lowercase     :						Dict=None)    ->							Union[str, Any]:
								self.set_matricies(red=_lowercase      ,       green=_lowercase      ,       blue=_lowercase      ,       red_edge=_lowercase      ,       nir=_lowercase)



					def     __snake_case						(	self     :						Union[str, Any]      ,       _lowercase     :						List[Any]=None      ,       _lowercase     :						int=None      ,       _lowercase     :						Dict=None      ,       _lowercase     :						Dict=None      ,       _lowercase     :						str=None)    ->							List[Any]:
								if red is not None:
											A_									= red
								if green is not None:
											A_									= green
								if blue is not None:
											A_									= blue
								if red_edge is not None:
											A_									= red_edge
								if nir is not None:
											A_									= nir
								return True



					def     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						Dict=""      ,       _lowercase     :						Tuple=None      ,       _lowercase     :						str=None      ,       _lowercase     :						Dict=None      ,       _lowercase     :						Any=None      ,       _lowercase     :						str=None)    ->							str:
								self.set_matricies(red=_lowercase      ,       green=_lowercase      ,       blue=_lowercase      ,       red_edge=_lowercase      ,       nir=_lowercase)
								A_									= {
								    'ARVI2': self.arvaa,
								    'CCCI': self.ccci,
								    'CVI': self.cvi,
								    'GLI': self.gli,
								    'NDVI': self.ndvi,
								    'BNDVI': self.bndvi,
								    'redEdgeNDVI': self.red_edge_ndvi,
								    'GNDVI': self.gndvi,
								    'GBNDVI': self.gbndvi,
								    'GRNDVI': self.grndvi,
								    'RBNDVI': self.rbndvi,
								    'PNDVI': self.pndvi,
								    'ATSAVI': self.atsavi,
								    'BWDRVI': self.bwdrvi,
								    'CIgreen': self.ci_green,
								    'CIrededge': self.ci_rededge,
								    'CI': self.ci,
								    'CTVI': self.ctvi,
								    'GDVI': self.gdvi,
								    'EVI': self.evi,
								    'GEMI': self.gemi,
								    'GOSAVI': self.gosavi,
								    'GSAVI': self.gsavi,
								    'Hue': self.hue,
								    'IVI': self.ivi,
								    'IPVI': self.ipvi,
								    'I': self.i,
								    'RVI': self.rvi,
								    'MRVI': self.mrvi,
								    'MSAVI': self.m_savi,
								    'NormG': self.norm_g,
								    'NormNIR': self.norm_nir,
								    'NormR': self.norm_r,
								    'NGRDI': self.ngrdi,
								    'RI': self.ri,
								    'S': self.s,
								    'IF': self._if,
								    'DVI': self.dvi,
								    'TVI': self.tvi,
								    'NDRE': self.ndre,
								}

								try:
											return funcs[index]()
								except KeyError:
											print('Index not in the list!')
											return False



					def     __snake_case						(	self     :						Union[str, Any])    ->							Union[str, Any]:
								return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))



					def     __snake_case						(	self     :						Dict)    ->							Optional[int]:
								return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
								    (self.nir - self.red) / (self.nir + self.red)
								)



					def     __snake_case						(	self     :						str)    ->							Optional[Any]:
								return self.nir * (self.red / (self.green**2))



					def     __snake_case						(	self     :						Dict)    ->							Optional[Any]:
								return (2 * self.green - self.red - self.blue) / (
								    2 * self.green + self.red + self.blue
								)



					def     __snake_case						(	self     :						Dict)    ->							Dict:
								return (self.nir - self.red) / (self.nir + self.red)



					def     __snake_case						(	self     :						Union[str, Any])    ->							Optional[int]:
								return (self.nir - self.blue) / (self.nir + self.blue)



					def     __snake_case						(	self     :						List[Any])    ->							List[Any]:
								return (self.redEdge - self.red) / (self.redEdge + self.red)



					def     __snake_case						(	self     :						Tuple)    ->							Any:
								return (self.nir - self.green) / (self.nir + self.green)



					def     __snake_case						(	self     :						Tuple)    ->							Union[str, Any]:
								return (self.nir - (self.green + self.blue)) / (
								    self.nir + (self.green + self.blue)
								)



					def     __snake_case						(	self     :						List[Any])    ->							List[str]:
								return (self.nir - (self.green + self.red)) / (
								    self.nir + (self.green + self.red)
								)



					def     __snake_case						(	self     :						Union[str, Any])    ->							str:
								return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))



					def     __snake_case						(	self     :						Optional[int])    ->							int:
								return (self.nir - (self.green + self.red + self.blue)) / (
								    self.nir + (self.green + self.red + self.blue)
								)



					def     __snake_case						(	self     :						Optional[Any]      ,       _lowercase     :						str=0.08      ,       _lowercase     :						int=1.22      ,       _lowercase     :						Optional[int]=0.03)    ->							str:
								return a * (
								    (self.nir - a * self.red - b)
								    / (a * self.nir + self.red - a * b + x * (1 + a**2))
								)



					def     __snake_case						(	self     :						Optional[Any])    ->							str:
								return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)



					def     __snake_case						(	self     :						Optional[int])    ->							Any:
								return (self.nir / self.green) - 1



					def     __snake_case						(	self     :						str)    ->							List[Any]:
								return (self.nir / self.redEdge) - 1



					def     __snake_case						(	self     :						Tuple)    ->							List[str]:
								return (self.red - self.blue) / self.red



					def     __snake_case						(	self     :						Union[str, Any])    ->							Optional[int]:
								A_									= self.ndvi()
								return ((ndvi + 0.5) / (abs(ndvi + 0.5))) * (abs(ndvi + 0.5) ** (1 / 2))



					def     __snake_case						(	self     :						Union[str, Any])    ->							Tuple:
								return self.nir - self.green



					def     __snake_case						(	self     :						Tuple)    ->							Tuple:
								return 2.5 * (
								    (self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
								)



					def     __snake_case						(	self     :						Union[str, Any])    ->							Union[str, Any]:
								A_									= (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
								    self.nir + self.red + 0.5
								)
								return n * (1 - 0.25 * n) - (self.red - 0.1_25) / (1 - self.red)



					def     __snake_case						(	self     :						Any      ,       _lowercase     :						Union[str, Any]=0.16)    ->							List[Any]:
								return (self.nir - self.green) / (self.nir + self.green + y)



					def     __snake_case						(	self     :						List[str]      ,       _lowercase     :						List[str]=0.5)    ->							Any:
								return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)



					def     __snake_case						(	self     :						Tuple)    ->							Optional[Any]:
								return np.arctan(
								    ((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue))



					def     __snake_case						(	self     :						Dict      ,       _lowercase     :						Union[str, Any]=None      ,       _lowercase     :						Dict=None)    ->							Optional[Any]:
								return (self.nir - b) / (a * self.red)



					def     __snake_case						(	self     :						Optional[Any])    ->							Any:
								return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)



					def     __snake_case						(	self     :						Tuple)    ->							Optional[Any]:
								return (self.red + self.green + self.blue) / 30.5



					def     __snake_case						(	self     :						Tuple)    ->							Tuple:
								return self.nir / self.red



					def     __snake_case						(	self     :						Dict)    ->							Optional[int]:
								return (self.rvi() - 1) / (self.rvi() + 1)



					def     __snake_case						(	self     :						List[str])    ->							Optional[Any]:
								return (
								    (2 * self.nir + 1)
								    - ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
								) / 2



					def     __snake_case						(	self     :						List[Any])    ->							Tuple:
								return self.green / (self.nir + self.red + self.green)



					def     __snake_case						(	self     :						Any)    ->							Optional[int]:
								return self.nir / (self.nir + self.red + self.green)



					def     __snake_case						(	self     :						Union[str, Any])    ->							Any:
								return self.red / (self.nir + self.red + self.green)



					def     __snake_case						(	self     :						Union[str, Any])    ->							Optional[int]:
								return (self.green - self.red) / (self.green + self.red)



					def     __snake_case						(	self     :						Any)    ->							Any:
								return (self.red - self.green) / (self.red + self.green)



					def     __snake_case						(	self     :						Union[str, Any])    ->							List[str]:
								A_									= np.max([np.max(self.red), np.max(self.green), np.max(self.blue)])
								A_									= np.min([np.min(self.red), np.min(self.green), np.min(self.blue)])
								return (max_value - min_value) / max_value



					def     __snake_case						(	self     :						Tuple)    ->							Union[str, Any]:
								return (2 * self.red - self.green - self.blue) / (self.green - self.blue)



					def     __snake_case						(	self     :						Any)    ->							List[Any]:
								return self.nir / self.red



					def     __snake_case						(	self     :						int)    ->							Optional[int]:
								return (self.ndvi() + 0.5) ** (1 / 2)



					def     __snake_case						(	self     :						Any)    ->							Dict:
								return (self.nir - self.redEdge) / (self.nir + self.redEdge)










'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class   __UpperCAmelCase				(     lowerCAmelCase			,lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		1



					@register_to_config
					def __init__(	self     :						str      ,       _lowercase     :						int = 1_000      ,       _lowercase     :						Optional[Union[np.ndarray, List[float]]] = None)    ->							List[str]:
								# set `betas`, `alphas`, `timesteps`
								self.set_timesteps(_lowercase)

								# standard deviation of the initial noise distribution
								A_									= 1.0

								# For now we only support F-PNDM, i.e. the runge-kutta method
								# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
								# mainly at formula (9), (12), (13) and the Algorithm 2.
								A_									= 4

								# running values
								A_									= []



					def     __snake_case						(	self     :						int      ,       _lowercase     :						int      ,       _lowercase     :						Union[str, torch.device] = None)    ->							Any:
								A_									= num_inference_steps
								A_									= torch.linspace(1      ,       0      ,       num_inference_steps + 1)[:-1]
								A_									= torch.cat([steps, torch.tensor([0.0])])

								if self.config.trained_betas is not None:
											A_									= torch.tensor(self.config.trained_betas      ,       dtype=torch.floataa)
								else:
											A_									= torch.sin(steps * math.pi / 2) ** 2

								A_									= (1.0 - self.betas**2) ** 0.5

								A_									= (torch.atana(self.betas      ,       self.alphas) / math.pi * 2)[:-1]
								A_									= timesteps.to(_lowercase)

								A_									= []



					def     __snake_case						(	self     :						Dict      ,       _lowercase     :						torch.FloatTensor      ,       _lowercase     :						int      ,       _lowercase     :						torch.FloatTensor      ,       _lowercase     :						bool = True      ,       )    ->							Union[SchedulerOutput, Tuple]:
								if self.num_inference_steps is None:
											raise ValueError(
											    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler')

								A_									= (self.timesteps == timestep).nonzero().item()
								A_									= timestep_index + 1

								A_									= sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
								self.ets.append(_lowercase)

								if len(self.ets) == 1:
											A_									= self.ets[-1]
								elif len(self.ets) == 2:
											A_									= (3 * self.ets[-1] - self.ets[-2]) / 2
								elif len(self.ets) == 3:
											A_									= (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
								else:
											A_									= (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

								A_									= self._get_prev_sample(_lowercase      ,       _lowercase      ,       _lowercase      ,       _lowercase)

								if not return_dict:
											return (prev_sample,)

								return SchedulerOutput(prev_sample=_lowercase)



					def     __snake_case						(	self     :						Dict      ,       _lowercase     :						torch.FloatTensor      ,       *_lowercase     :						Optional[Any]      ,       **_lowercase     :						int)    ->							torch.FloatTensor:
								return sample



					def     __snake_case						(	self     :						Optional[int]      ,       _lowercase     :						Optional[Any]      ,       _lowercase     :						Dict      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						Union[str, Any])    ->							Union[str, Any]:
								A_									= self.alphas[timestep_index]
								A_									= self.betas[timestep_index]

								A_									= self.alphas[prev_timestep_index]
								A_									= self.betas[prev_timestep_index]

								A_									= (sample - sigma * ets) / max(_lowercase      ,       1E-8)
								A_									= next_alpha * pred + ets * next_sigma

								return prev_sample



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




