





'''simple docstring'''


# Imports
import numpy as np
class 				a_       :





     def __init__(			self		: Tuple     ,				lowercase		: Optional[int]=None     ,				lowercase		: Union[str, Any]=None     ,				lowercase		: Dict=None     ,				lowercase		: Dict=None     ,				lowercase		: Optional[int]=None						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Dict     ,				lowercase		: Optional[Any]=None     ,				lowercase		: Dict=None     ,				lowercase		: List[Any]=None     ,				lowercase		: str=None     ,				lowercase		: Union[str, Any]=None						):





        """simple docstring"""




        if red is not None:
           lowercase_      :Dict         =			red
        if green is not None:
           lowercase_      :Union[str, Any]         =			green
        if blue is not None:
           lowercase_      :Union[str, Any]         =			blue
        if red_edge is not None:
           lowercase_      :Any         =			red_edge
        if nir is not None:
           lowercase_      :str         =			nir
        return True





     def 							lowercase__				(			self		: Tuple     ,				lowercase		: str=""     ,				lowercase		: Union[str, Any]=None     ,				lowercase		: Union[str, Any]=None     ,				lowercase		: Any=None     ,				lowercase		: Union[str, Any]=None     ,				lowercase		: Optional[Any]=None						):





        """simple docstring"""




        self.set_matricies(red=lowercase     ,				green=lowercase     ,				blue=lowercase     ,				red_edge=lowercase     ,				nir=lowercase						)
        lowercase_      :Optional[Any]         =			{
            "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 							lowercase__				(			self		: Dict						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: int						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: List[Any]						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Union[str, Any]						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: List[Any]						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Union[str, Any]						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: int						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Any						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Any     ,				lowercase		: List[Any]=0.08     ,				lowercase		: Tuple=1.22     ,				lowercase		: Optional[int]=0.03						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: str						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Any						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: str						):





        """simple docstring"""




        lowercase_      :Any         =			self.ndvi()
        return ((ndvi + 0.5) / (abs(ndvi + 0.5						))) * (abs(ndvi + 0.5						) ** (1 / 2))





     def 							lowercase__				(			self		: str						):





        """simple docstring"""




        return self.nir - self.green





     def 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Any						):





        """simple docstring"""




        lowercase_      :List[Any]         =			(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 							lowercase__				(			self		: List[Any]     ,				lowercase		: Union[str, Any]=0.16						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Optional[int]     ,				lowercase		: Optional[Any]=0.5						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: str						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: List[str]     ,				lowercase		: Union[str, Any]=None     ,				lowercase		: str=None						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: str						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Any						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Optional[Any]						):





        """simple docstring"""




        return self.nir / self.red





     def 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: str						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: str						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: int						):





        """simple docstring"""




        lowercase_      :Dict         =			np.max([np.max(self.red						), np.max(self.green						), np.max(self.blue						)]						)
        lowercase_      :Optional[int]         =			np.min([np.min(self.red						), np.min(self.green						), np.min(self.blue						)]						)
        return (max_value - min_value) / max_value





     def 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: str						):





        """simple docstring"""




        return self.nir / self.red





     def 							lowercase__				(			self		: Dict						):





        """simple docstring"""




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







     def 							lowercase__				(			self		: List[Any]						):





        """simple docstring"""




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









'''simple docstring'''


from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


lowerCAmelCase :					Tuple 							=logging.get_logger(__name__)
def 							UpperCAmelCase_  (						__lowerCamelCase : Union[tf.Tensor, np.ndarray]			):
   if isinstance(__lowerCamelCase     ,np.ndarray			):
      return list(tensor.shape			)

   lowercase_      :Optional[int]         =			tf.shape(__lowerCamelCase			)

   if tensor.shape == tf.TensorShape(__lowerCamelCase			):
      return dynamic

   lowercase_      :Union[str, Any]         =			tensor.shape.as_list()

   return [dynamic[i] if s is None else s for i, s in enumerate(__lowerCamelCase			)]
def 							UpperCAmelCase_  (						__lowerCamelCase : tf.Tensor     ,__lowerCamelCase : Optional[int] = None     ,__lowerCamelCase : Optional[str] = None			):
   return tf.nn.softmax(logits=logits + 1e-9     ,axis=__lowerCamelCase     ,name=__lowerCamelCase			)
def 							UpperCAmelCase_  (						__lowerCamelCase : str     ,__lowerCamelCase : List[Any]     ,__lowerCamelCase : Any     ,__lowerCamelCase : List[str]=1e-5     ,__lowerCamelCase : List[str]=-1			):
   # This is a very simplified functional layernorm, designed to duplicate
   # the functionality of PyTorch nn.functional.layer_norm when this is needed to port
   # models in Transformers.

   if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__lowerCamelCase     ,__lowerCamelCase			):
      raise NotImplementedError("Only 1D weight and bias tensors are supported for now, with only a single axis."			)

   # Get mean and variance on the axis to be normalized
   lowercase_      ,							lowercase_      :List[str]         =			tf.nn.moments(__lowerCamelCase     ,axes=[axis]     ,keepdims=__lowerCamelCase			)

   if axis != -1:
      # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
      # on every dimension except axis
      lowercase_      :Union[str, Any]         =			[1] * inputs.shape.rank
      lowercase_      :Optional[Any]         =			shape_list(__lowerCamelCase			)[axis]
      lowercase_      :List[str]         =			tf.reshape(__lowerCamelCase     ,__lowerCamelCase			)
      lowercase_      :Dict         =			tf.reshape(__lowerCamelCase     ,__lowerCamelCase			)

   # Compute layer normalization using the batch_normalization
   # function.
   lowercase_      :Union[str, Any]         =			tf.nn.batch_normalization(
       __lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase     ,offset=__lowerCamelCase     ,scale=__lowerCamelCase     ,variance_epsilon=__lowerCamelCase     ,)
   return outputs
def 							UpperCAmelCase_  (						__lowerCamelCase : Optional[int]     ,__lowerCamelCase : Union[str, Any]=0     ,__lowerCamelCase : Dict=-1			):
   # Replicates the behavior of torch.flatten in TF

   # If end_dim or start_dim is negative, count them from the end
   if end_dim < 0:
      end_dim += input.shape.rank
   if start_dim < 0:
      start_dim += input.shape.rank

   if start_dim == end_dim:
      return input

   lowercase_      :Optional[int]         =			tf.shape(__lowerCamelCase			)
   lowercase_      :Optional[int]         =			tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]			)
   lowercase_      :List[str]         =			tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]     ,axis=0			)
   return tf.reshape(__lowerCamelCase     ,__lowerCamelCase			)
def 							UpperCAmelCase_  (						__lowerCamelCase : tf.Tensor			):
   if not isinstance(__lowerCamelCase     ,tf.Tensor			):
      lowercase_      :str         =			tf.convert_to_tensor(__lowerCamelCase			)  # Catches stray NumPy inputs
   if encoder_attention_mask.shape.rank == 3:
      lowercase_      :List[Any]         =			encoder_attention_mask[:, None, :, :]
   if encoder_attention_mask.shape.rank == 2:
      lowercase_      :Optional[int]         =			encoder_attention_mask[:, None, None, :]
   # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
   # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
   # /transformer/transformer_layers.py#L270
   # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
   # encoder_extended_attention_mask.transpose(-1, -2))
   lowercase_      :str         =			(
       tf.cast(1     ,encoder_attention_mask.dtype			) - encoder_extended_attention_mask
   ) * encoder_extended_attention_mask.dtype.min

   return encoder_extended_attention_mask
def 							UpperCAmelCase_  (						__lowerCamelCase : tf.Tensor     ,__lowerCamelCase : int     ,__lowerCamelCase : str = "input_ids"			):
   tf.debugging.assert_less(
       __lowerCamelCase     ,tf.cast(__lowerCamelCase     ,dtype=tensor.dtype			)     ,message=(
           F'The maximum value of {tensor_name} ({tf.math.reduce_max(__lowerCamelCase			)}) must be smaller than the embedding '
           F'layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'
       )     ,)
def 							UpperCAmelCase_  (						__lowerCamelCase : List[str]     ,__lowerCamelCase : Tuple     ,__lowerCamelCase : Dict			):
   lowercase_      :int         =			6_45_12
   # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
   # because in that case even chunking the array would not make the saving
   # possible.
   lowercase_      :Union[str, Any]         =			[x for x in data if len(__lowerCamelCase			) > HDF5_OBJECT_HEADER_LIMIT]

   # Expecting this to never be true.
   if bad_attributes:
      raise RuntimeError(
          "The following attributes cannot be saved to HDF5 file because "
          F'they are larger than {HDF5_OBJECT_HEADER_LIMIT} '
          F'bytes: {bad_attributes}'			)

   lowercase_      :Union[str, Any]         =			np.asarray(__lowerCamelCase			)

   lowercase_      :Optional[int]         =			1
   lowercase_      :int         =			np.array_split(__lowerCamelCase     ,__lowerCamelCase			)

   # This will never loop forever thanks to the test above.
   while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data			):
      num_chunks += 1
      lowercase_      :List[Any]         =			np.array_split(__lowerCamelCase     ,__lowerCamelCase			)

   if num_chunks > 1:
      for chunk_id, chunk_data in enumerate(__lowerCamelCase			):
         lowercase_      :int         =			chunk_data
   else:
      lowercase_      :Tuple         =			data
def 							UpperCAmelCase_  (						__lowerCamelCase : str     ,__lowerCamelCase : Tuple			):
   if name in group.attrs:
      lowercase_      :Optional[Any]         =			[n.decode("utf8"			) if hasattr(__lowerCamelCase     ,"decode"			) else n for n in group.attrs[name]]
   else:
      lowercase_      :List[str]         =			[]
      lowercase_      :str         =			0
      while "%s%d" % (name, chunk_id) in group.attrs:
         data.extend(
             [n.decode("utf8"			) if hasattr(__lowerCamelCase     ,"decode"			) else n for n in group.attrs["%s%d" % (name, chunk_id)]]			)
         chunk_id += 1
   return data


def 							UpperCAmelCase_  (						__lowerCamelCase : str			):

   def _expand_single_ad_tensor(__lowerCamelCase : Tuple			):
      if isinstance(__lowerCamelCase     ,tf.Tensor			) and t.shape.rank == 1:
         return tf.expand_dims(__lowerCamelCase     ,axis=-1			)
      return t

   return tf.nest.map_structure(_expand_single_ad_tensor     ,__lowerCamelCase			)


