






import numpy as np



class 				_A			:
    """simple docstring"""

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



    def _a							(				self      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]=None ,     __SCREAMING_SNAKE_CASE      :     List[Any]=None ,     __SCREAMING_SNAKE_CASE      :     List[Any]=None ,     __SCREAMING_SNAKE_CASE      :     Tuple=None ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=None      )      ->							List[str]:
           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      :     str ,     __SCREAMING_SNAKE_CASE      :     int="" ,     __SCREAMING_SNAKE_CASE      :     Tuple=None ,     __SCREAMING_SNAKE_CASE      :     int=None ,     __SCREAMING_SNAKE_CASE      :     Any=None ,     __SCREAMING_SNAKE_CASE      :     Dict=None ,     __SCREAMING_SNAKE_CASE      :     List[str]=None      )      ->							Optional[Any]:
           self.set_matricies(red=lowerCamelCase_ ,     green=lowerCamelCase_ ,     blue=lowerCamelCase_ ,     red_edge=lowerCamelCase_ ,     nir=lowerCamelCase_      )
           __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      :     Dict      )      ->							List[Any]:
           return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))



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



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



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



    def _a							(				self      :     Optional[Any]      )      ->							str:
           return (self.nir - self.red) / (self.nir + self.red)



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



    def _a							(				self      :     Optional[Any]      )      ->							int:
           return (self.redEdge - self.red) / (self.redEdge + self.red)



    def _a							(				self      :     int      )      ->							int:
           return (self.nir - self.green) / (self.nir + self.green)



    def _a							(				self      :     int      )      ->							List[str]:
           return (self.nir - (self.green + self.blue)) / (
               self.nir + (self.green + self.blue)
           )



    def _a							(				self      :     str      )      ->							Dict:
           return (self.nir - (self.green + self.red)) / (
               self.nir + (self.green + self.red)
           )



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



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



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



    def _a							(				self      :     List[str]      )      ->							Union[str, Any]:
           return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)



    def _a							(				self      :     List[Any]      )      ->							Any:
           return (self.nir / self.green) - 1



    def _a							(				self      :     Optional[int]      )      ->							Optional[int]:
           return (self.nir / self.redEdge) - 1



    def _a							(				self      :     Optional[int]      )      ->							List[Any]:
           return (self.red - self.blue) / self.red



    def _a							(				self      :     Any      )      ->							Optional[Any]:
           __UpperCAmelCase     				=self.ndvi()
           return ((ndvi + 0.5) / (abs(ndvi + 0.5      ))) * (abs(ndvi + 0.5      ) ** (1 / 2))



    def _a							(				self      :     Optional[Any]      )      ->							Tuple:
           return self.nir - self.green



    def _a							(				self      :     List[Any]      )      ->							int:
           return 2.5 * (
               (self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
           )



    def _a							(				self      :     Dict      )      ->							Tuple:
           __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      :     Any ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=0.16      )      ->							Optional[Any]:
           return (self.nir - self.green) / (self.nir + self.green + y)



    def _a							(				self      :     int ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]=0.5      )      ->							Union[str, Any]:
           return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)



    def _a							(				self      :     Tuple      )      ->							List[str]:
           return np.arctan(
               ((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue)      )



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



    def _a							(				self      :     str      )      ->							Union[str, Any]:
           return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)



    def _a							(				self      :     str      )      ->							Union[str, Any]:
           return (self.red + self.green + self.blue) / 30.5



    def _a							(				self      :     Optional[int]      )      ->							Tuple:
           return self.nir / self.red



    def _a							(				self      :     Tuple      )      ->							int:
           return (self.rvi() - 1) / (self.rvi() + 1)



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



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



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



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



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



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



    def _a							(				self      :     Optional[int]      )      ->							str:
           __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      :     List[str]      )      ->							Union[str, Any]:
           return (2 * self.red - self.green - self.blue) / (self.green - self.blue)



    def _a							(				self      :     Union[str, Any]      )      ->							int:
           return self.nir / self.red



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



    def _a							(				self      :     Union[str, Any]      )      ->							Dict:
           return (self.nir - self.redEdge) / (self.nir + self.redEdge)








'''simple docstring'''






import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory


def    _snake_case  (	)		->       Union[str, Any]:
							raise RuntimeError('''CUDA out of memory.'''			)




class 						a__							(				nn.Module    ):





							'''simple docstring'''






							def __init__(						self  )			->				int:
														super().__init__()
														lowerCAmelCase__			  =      nn.Linear(3   ,       4  )
														lowerCAmelCase__			  =      nn.BatchNormad(4  )
														lowerCAmelCase__			  =      nn.Linear(4   ,       5  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				Optional[Any]:
														return self.lineara(self.batchnorm(self.lineara(lowerCamelCase_  )  )  )




class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														lowerCAmelCase__			  =      []

														@find_executable_batch_size(starting_batch_size=1_28  )
														def mock_training_loop_function(lowerCamelCase_  ):
																					nonlocal batch_sizes
																					batch_sizes.append(lowerCamelCase_  )
																					if batch_size != 8:
																												raise_fake_out_of_memory()

														mock_training_loop_function()
														self.assertListEqual(lowerCamelCase_   ,       [1_28, 64, 32, 16, 8]  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      []

														@find_executable_batch_size(starting_batch_size=1_28  )
														def mock_training_loop_function(lowerCamelCase_   ,       lowerCamelCase_  ):
																					nonlocal batch_sizes
																					batch_sizes.append(lowerCamelCase_  )
																					if batch_size != 8:
																												raise_fake_out_of_memory()
																					return batch_size, arga

														lowerCAmelCase__  ,   lowerCAmelCase__			  =      mock_training_loop_function('''hello'''  )
														self.assertListEqual(lowerCamelCase_   ,       [1_28, 64, 32, 16, 8]  )
														self.assertListEqual([bs, arga]   ,       [8, '''hello''']  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														@find_executable_batch_size(starting_batch_size=0  )
														def mock_training_loop_function(lowerCamelCase_  ):
																					pass

														with self.assertRaises(lowerCamelCase_  ) as cm:
																					mock_training_loop_function()
																					self.assertIn('''No executable batch size found, reached zero.'''   ,       cm.exception.args[0]  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														@find_executable_batch_size(starting_batch_size=16  )
														def mock_training_loop_function(lowerCamelCase_  ):
																					if batch_size > 0:
																												raise_fake_out_of_memory()
																					pass

														with self.assertRaises(lowerCamelCase_  ) as cm:
																					mock_training_loop_function()
																					self.assertIn('''No executable batch size found, reached zero.'''   ,       cm.exception.args[0]  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														@find_executable_batch_size(starting_batch_size=1_28  )
														def mock_training_loop_function(lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  ):
																					if batch_size != 8:
																												raise raise_fake_out_of_memory()

														with self.assertRaises(lowerCamelCase_  ) as cm:
																					mock_training_loop_function(1_28   ,       '''hello'''   ,       '''world'''  )
																					self.assertIn('''Batch size was passed into `f`'''   ,       cm.exception.args[0]  )
																					self.assertIn('''`f(arg1=\'hello\', arg2=\'world\')'''   ,       cm.exception.args[0]  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														@find_executable_batch_size(starting_batch_size=16  )
														def mock_training_loop_function(lowerCamelCase_  ):
																					raise ValueError('''Oops, we had an error!'''  )

														with self.assertRaises(lowerCamelCase_  ) as cm:
																					mock_training_loop_function()
																					self.assertIn('''Oops, we had an error!'''   ,       cm.exception.args[0]  )







							@require_cuda
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														lowerCAmelCase__			  =      torch.cuda.memory_allocated()
														lowerCAmelCase__			  =      ModelForTest()
														model.cuda()
														self.assertGreater(torch.cuda.memory_allocated()   ,       lowerCamelCase_  )
														lowerCAmelCase__			  =      release_memory(lowerCamelCase_  )
														self.assertEqual(torch.cuda.memory_allocated()   ,       lowerCamelCase_  )