

"""simple docstring"""


# Imports
import numpy as np

class      __a							:






     '''simple docstring'''

     def __init__(						self	,    _a=None	,    _a=None	,    _a=None	,    _a=None	,    _a=None  )  ->      Dict:


           """simple docstring"""


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

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


           """simple docstring"""


           if red is not None:
                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	red
           if green is not None:
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	green
           if blue is not None:
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	blue
           if red_edge is not None:
                 SCREAMING_SNAKE_CASE__   :      List[str]      		=	red_edge
           if nir is not None:
                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	nir
           return True

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


           """simple docstring"""


           self.set_matricies(red=_a	,    green=_a	,    blue=_a	,    red_edge=_a	,    nir=_a  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	{
               """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  )  ->      Optional[int]:


           """simple docstring"""


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

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


           """simple docstring"""


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

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


           """simple docstring"""


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

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


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

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


           """simple docstring"""


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

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


           """simple docstring"""


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

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


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

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


           """simple docstring"""


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

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


           """simple docstring"""


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

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


           """simple docstring"""


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

     def 					_a			(						self	,    _a=0.08	,    _a=1.22	,    _a=0.03  )  ->      List[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  )  ->      Optional[Any]:


           """simple docstring"""


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

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


           """simple docstring"""


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

     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


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

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


           """simple docstring"""


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

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	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  )  ->      Optional[Any]:


           """simple docstring"""


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

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	(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	,    _a=0.16  )  ->      str:


           """simple docstring"""


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

     def 					_a			(						self	,    _a=0.5  )  ->      Optional[Any]:


           """simple docstring"""


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

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


           """simple docstring"""


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

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


           """simple docstring"""


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

     def 					_a			(						self  )  ->      Dict:


           """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  )  ->      int:


           """simple docstring"""


           return self.nir / self.red

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


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

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


           """simple docstring"""


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

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


           """simple docstring"""


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

     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


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

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


           """simple docstring"""


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

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


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

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


           """simple docstring"""


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

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	np.max([np.max(self.red  ), np.max(self.green  ), np.max(self.blue  )]  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	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[Any]:


           """simple docstring"""


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

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           return self.nir / self.red

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


           """simple docstring"""


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





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


           """simple docstring"""


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







"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


a					:Any    = {
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a					:Union[str, Any]    = [
      "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RobertaPreLayerNormForCausalLM",
      "RobertaPreLayerNormForMaskedLM",
      "RobertaPreLayerNormForMultipleChoice",
      "RobertaPreLayerNormForQuestionAnswering",
      "RobertaPreLayerNormForSequenceClassification",
      "RobertaPreLayerNormForTokenClassification",
      "RobertaPreLayerNormModel",
      "RobertaPreLayerNormPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a					:Optional[Any]    = [
      "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFRobertaPreLayerNormForCausalLM",
      "TFRobertaPreLayerNormForMaskedLM",
      "TFRobertaPreLayerNormForMultipleChoice",
      "TFRobertaPreLayerNormForQuestionAnswering",
      "TFRobertaPreLayerNormForSequenceClassification",
      "TFRobertaPreLayerNormForTokenClassification",
      "TFRobertaPreLayerNormMainLayer",
      "TFRobertaPreLayerNormModel",
      "TFRobertaPreLayerNormPreTrainedModel",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a					:List[Any]    = [
      "FlaxRobertaPreLayerNormForCausalLM",
      "FlaxRobertaPreLayerNormForMaskedLM",
      "FlaxRobertaPreLayerNormForMultipleChoice",
      "FlaxRobertaPreLayerNormForQuestionAnswering",
      "FlaxRobertaPreLayerNormForSequenceClassification",
      "FlaxRobertaPreLayerNormForTokenClassification",
      "FlaxRobertaPreLayerNormModel",
      "FlaxRobertaPreLayerNormPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_roberta_prelayernorm import (
      ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
      RobertaPreLayerNormConfig,
      RobertaPreLayerNormOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roberta_prelayernorm import (
        ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
        RobertaPreLayerNormForCausalLM,
        RobertaPreLayerNormForMaskedLM,
        RobertaPreLayerNormForMultipleChoice,
        RobertaPreLayerNormForQuestionAnswering,
        RobertaPreLayerNormForSequenceClassification,
        RobertaPreLayerNormForTokenClassification,
        RobertaPreLayerNormModel,
        RobertaPreLayerNormPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_roberta_prelayernorm import (
        TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRobertaPreLayerNormForCausalLM,
        TFRobertaPreLayerNormForMaskedLM,
        TFRobertaPreLayerNormForMultipleChoice,
        TFRobertaPreLayerNormForQuestionAnswering,
        TFRobertaPreLayerNormForSequenceClassification,
        TFRobertaPreLayerNormForTokenClassification,
        TFRobertaPreLayerNormMainLayer,
        TFRobertaPreLayerNormModel,
        TFRobertaPreLayerNormPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_roberta_prelayernorm import (
        FlaxRobertaPreLayerNormForCausalLM,
        FlaxRobertaPreLayerNormForMaskedLM,
        FlaxRobertaPreLayerNormForMultipleChoice,
        FlaxRobertaPreLayerNormForQuestionAnswering,
        FlaxRobertaPreLayerNormForSequenceClassification,
        FlaxRobertaPreLayerNormForTokenClassification,
        FlaxRobertaPreLayerNormModel,
        FlaxRobertaPreLayerNormPreTrainedModel,
    )

else:
  import sys

  a					:Optional[int]    = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




