import numpy as np



class      _A :






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





       '''simple docstring'''





       self.set_matricies(red=_lowercase ,					green=_lowercase ,					blue=_lowercase ,					red_edge=_lowercase ,					nir=_lowercase						)






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_=None ,					SCREAMING_SNAKE_CASE_=None ,					SCREAMING_SNAKE_CASE_=None ,					SCREAMING_SNAKE_CASE_=None ,					SCREAMING_SNAKE_CASE_=None						)		->   List[Any]:





       '''simple docstring'''





       if red is not None:
            UpperCamelCase__					    =   red
       if green is not None:
            UpperCamelCase__					    =   green
       if blue is not None:
            UpperCamelCase__					    =   blue
       if red_edge is not None:
            UpperCamelCase__					    =   red_edge
       if nir is not None:
            UpperCamelCase__					    =   nir
       return True






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_="" ,					SCREAMING_SNAKE_CASE_=None ,					SCREAMING_SNAKE_CASE_=None ,					SCREAMING_SNAKE_CASE_=None ,					SCREAMING_SNAKE_CASE_=None ,					SCREAMING_SNAKE_CASE_=None						)		->   str:





       '''simple docstring'''





       self.set_matricies(red=_lowercase ,					green=_lowercase ,					blue=_lowercase ,					red_edge=_lowercase ,					nir=_lowercase						)
       UpperCamelCase__					    =   {
           '''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 		_a       (self						)		->   Union[str, Any]:





       '''simple docstring'''





       return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))






  def 		_a       (self						)		->   Optional[int]:





       '''simple docstring'''





       return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
           (self.nir - self.red) / (self.nir + self.red)
       )






  def 		_a       (self						)		->   Optional[Any]:





       '''simple docstring'''





       return self.nir * (self.red / (self.green**2))






  def 		_a       (self						)		->   Optional[Any]:





       '''simple docstring'''





       return (2 * self.green - self.red - self.blue) / (
           2 * self.green + self.red + self.blue
       )






  def 		_a       (self						)		->   Dict:





       '''simple docstring'''





       return (self.nir - self.red) / (self.nir + self.red)






  def 		_a       (self						)		->   Optional[int]:





       '''simple docstring'''





       return (self.nir - self.blue) / (self.nir + self.blue)






  def 		_a       (self						)		->   List[Any]:





       '''simple docstring'''





       return (self.redEdge - self.red) / (self.redEdge + self.red)






  def 		_a       (self						)		->   Any:





       '''simple docstring'''





       return (self.nir - self.green) / (self.nir + self.green)






  def 		_a       (self						)		->   Union[str, Any]:





       '''simple docstring'''





       return (self.nir - (self.green + self.blue)) / (
           self.nir + (self.green + self.blue)
       )






  def 		_a       (self						)		->   List[str]:





       '''simple docstring'''





       return (self.nir - (self.green + self.red)) / (
           self.nir + (self.green + self.red)
       )






  def 		_a       (self						)		->   str:





       '''simple docstring'''





       return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))






  def 		_a       (self						)		->   int:





       '''simple docstring'''





       return (self.nir - (self.green + self.red + self.blue)) / (
           self.nir + (self.green + self.red + self.blue)
       )






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_=0.08 ,					SCREAMING_SNAKE_CASE_=1.22 ,					SCREAMING_SNAKE_CASE_=0.03						)		->   str:





       '''simple docstring'''





       return a * (
           (self.nir - a * self.red - b)
           / (a * self.nir + self.red - a * b + x * (1 + a**2))
       )






  def 		_a       (self						)		->   str:





       '''simple docstring'''





       return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)






  def 		_a       (self						)		->   Any:





       '''simple docstring'''





       return (self.nir / self.green) - 1






  def 		_a       (self						)		->   List[Any]:





       '''simple docstring'''





       return (self.nir / self.redEdge) - 1






  def 		_a       (self						)		->   List[str]:





       '''simple docstring'''





       return (self.red - self.blue) / self.red






  def 		_a       (self						)		->   Optional[int]:





       '''simple docstring'''





       UpperCamelCase__					    =   self.ndvi()
       return ((ndvi + 0.5) / (abs(ndvi + 0.5						))) * (abs(ndvi + 0.5						) ** (1 / 2))






  def 		_a       (self						)		->   Tuple:





       '''simple docstring'''





       return self.nir - self.green






  def 		_a       (self						)		->   Tuple:





       '''simple docstring'''





       return 2.5 * (
           (self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
       )






  def 		_a       (self						)		->   Union[str, Any]:





       '''simple docstring'''





       UpperCamelCase__					    =   (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 - 0.125) / (1 - self.red)






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_=0.16						)		->   List[Any]:





       '''simple docstring'''





       return (self.nir - self.green) / (self.nir + self.green + y)






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_=0.5						)		->   Any:





       '''simple docstring'''





       return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)






  def 		_a       (self						)		->   Optional[Any]:





       '''simple docstring'''





       return np.arctan(
           ((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue)						)






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_=None ,					SCREAMING_SNAKE_CASE_=None						)		->   Optional[Any]:





       '''simple docstring'''





       return (self.nir - b) / (a * self.red)






  def 		_a       (self						)		->   Any:





       '''simple docstring'''





       return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)






  def 		_a       (self						)		->   Optional[Any]:





       '''simple docstring'''





       return (self.red + self.green + self.blue) / 30.5






  def 		_a       (self						)		->   Tuple:





       '''simple docstring'''





       return self.nir / self.red






  def 		_a       (self						)		->   Optional[int]:





       '''simple docstring'''





       return (self.rvi() - 1) / (self.rvi() + 1)






  def 		_a       (self						)		->   Optional[Any]:





       '''simple docstring'''





       return (
           (2 * self.nir + 1)
           - ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
       ) / 2






  def 		_a       (self						)		->   Tuple:





       '''simple docstring'''





       return self.green / (self.nir + self.red + self.green)






  def 		_a       (self						)		->   Optional[int]:





       '''simple docstring'''





       return self.nir / (self.nir + self.red + self.green)






  def 		_a       (self						)		->   Any:





       '''simple docstring'''





       return self.red / (self.nir + self.red + self.green)






  def 		_a       (self						)		->   Optional[int]:





       '''simple docstring'''





       return (self.green - self.red) / (self.green + self.red)






  def 		_a       (self						)		->   Any:





       '''simple docstring'''





       return (self.red - self.green) / (self.red + self.green)






  def 		_a       (self						)		->   List[str]:





       '''simple docstring'''





       UpperCamelCase__					    =   np.max([np.max(self.red						), np.max(self.green						), np.max(self.blue						)]						)
       UpperCamelCase__					    =   np.min([np.min(self.red						), np.min(self.green						), np.min(self.blue						)]						)
       return (max_value - min_value) / max_value






  def 		_a       (self						)		->   Union[str, Any]:





       '''simple docstring'''





       return (2 * self.red - self.green - self.blue) / (self.green - self.blue)






  def 		_a       (self						)		->   List[Any]:





       '''simple docstring'''





       return self.nir / self.red






  def 		_a       (self						)		->   Optional[int]:





       '''simple docstring'''





       return (self.ndvi() + 0.5) ** (1 / 2)






  def 		_a       (self						)		->   Dict:





       '''simple docstring'''





       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




