



"""simple docstring"""

import numpy as np







class    __lowercase     :







						"""simple docstring"""







						def __init__(self   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None       ):
											self.set_matricies(red=lowercase__   ,		green=lowercase__   ,		blue=lowercase__   ,		red_edge=lowercase__   ,		nir=lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None       ):
											if red is not None:
																snake_case_					:		int 					=					red
											if green is not None:
																snake_case_					:		List[Any] 					=					green
											if blue is not None:
																snake_case_					:		Tuple 					=					blue
											if red_edge is not None:
																snake_case_					:		Any 					=					red_edge
											if nir is not None:
																snake_case_					:		Optional[int] 					=					nir
											return True







						def 						__UpperCamelCase							(self   ,		lowercase__=""   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None       ):
											self.set_matricies(red=lowercase__   ,		green=lowercase__   ,		blue=lowercase__   ,		red_edge=lowercase__   ,		nir=lowercase__       )
											snake_case_					:		Union[str, 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 						__UpperCamelCase							(self       ):
											return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))







						def 						__UpperCamelCase							(self       ):
											return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
											    (self.nir - self.red) / (self.nir + self.red)
											)







						def 						__UpperCamelCase							(self       ):
											return self.nir * (self.red / (self.green**2))







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







						def 						__UpperCamelCase							(self       ):
											return (self.nir - self.red) / (self.nir + self.red)







						def 						__UpperCamelCase							(self       ):
											return (self.nir - self.blue) / (self.nir + self.blue)







						def 						__UpperCamelCase							(self       ):
											return (self.redEdge - self.red) / (self.redEdge + self.red)







						def 						__UpperCamelCase							(self       ):
											return (self.nir - self.green) / (self.nir + self.green)







						def 						__UpperCamelCase							(self       ):
											return (self.nir - (self.green + self.blue)) / (
											    self.nir + (self.green + self.blue)
											)







						def 						__UpperCamelCase							(self       ):
											return (self.nir - (self.green + self.red)) / (
											    self.nir + (self.green + self.red)
											)







						def 						__UpperCamelCase							(self       ):
											return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))







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







						def 						__UpperCamelCase							(self   ,		lowercase__=0.08   ,		lowercase__=1.22   ,		lowercase__=0.03       ):
											return a * (
											    (self.nir - a * self.red - b)
											    / (a * self.nir + self.red - a * b + x * (1 + a**2))
											)







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







						def 						__UpperCamelCase							(self       ):
											return (self.nir / self.green) - 1







						def 						__UpperCamelCase							(self       ):
											return (self.nir / self.redEdge) - 1







						def 						__UpperCamelCase							(self       ):
											return (self.red - self.blue) / self.red







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Tuple 					=					self.ndvi()
											return ((ndvi + 0.5) / (abs(ndvi + 0.5       ))) * (abs(ndvi + 0.5       ) ** (1 / 2))







						def 						__UpperCamelCase							(self       ):
											return self.nir - self.green







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







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		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 - 0.125) / (1 - self.red)







						def 						__UpperCamelCase							(self   ,		lowercase__=0.16       ):
											return (self.nir - self.green) / (self.nir + self.green + y)







						def 						__UpperCamelCase							(self   ,		lowercase__=0.5       ):
											return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)







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







						def 						__UpperCamelCase							(self   ,		lowercase__=None   ,		lowercase__=None       ):
											return (self.nir - b) / (a * self.red)







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







						def 						__UpperCamelCase							(self       ):
											return (self.red + self.green + self.blue) / 30.5







						def 						__UpperCamelCase							(self       ):
											return self.nir / self.red







						def 						__UpperCamelCase							(self       ):
											return (self.rvi() - 1) / (self.rvi() + 1)







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







						def 						__UpperCamelCase							(self       ):
											return self.green / (self.nir + self.red + self.green)







						def 						__UpperCamelCase							(self       ):
											return self.nir / (self.nir + self.red + self.green)







						def 						__UpperCamelCase							(self       ):
											return self.red / (self.nir + self.red + self.green)







						def 						__UpperCamelCase							(self       ):
											return (self.green - self.red) / (self.green + self.red)







						def 						__UpperCamelCase							(self       ):
											return (self.red - self.green) / (self.red + self.green)







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Union[str, Any] 					=					np.max([np.max(self.red       ), np.max(self.green       ), np.max(self.blue       )]       )
											snake_case_					:		Optional[Any] 					=					np.min([np.min(self.red       ), np.min(self.green       ), np.min(self.blue       )]       )
											return (max_value - min_value) / max_value







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







						def 						__UpperCamelCase							(self       ):
											return self.nir / self.red







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







						def 						__UpperCamelCase							(self       ):
											return (self.nir - self.redEdge) / (self.nir + self.redEdge)









"""simple docstring"""

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
			import PIL


a_			   =					logging.get_logger(__name__)







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							int							       =		["""pixel_values"""]







						def __init__(self   ,		lowercase__ = True   ,		lowercase__ = None   ,		lowercase__ = 0.9   ,		lowercase__ = PILImageResampling.BICUBIC   ,		lowercase__ = True   ,		lowercase__ = None   ,		lowercase__ = 1 / 2_55   ,		lowercase__ = True   ,		lowercase__ = True   ,		lowercase__ = None   ,		lowercase__ = None   ,		**lowercase__   ,		):
											super().__init__(**lowercase__       )
											snake_case_					:		Tuple 					=					size if size is not None else {"""shortest_edge""": 2_24}
											snake_case_					:		Union[str, Any] 					=					get_size_dict(lowercase__   ,		default_to_square=lowercase__       )
											snake_case_					:		str 					=					crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
											snake_case_					:		Dict 					=					get_size_dict(lowercase__   ,		param_name="""crop_size"""       )

											snake_case_					:		Union[str, Any] 					=					do_resize
											snake_case_					:		List[str] 					=					size
											snake_case_					:		str 					=					crop_pct
											snake_case_					:		str 					=					resample
											snake_case_					:		Optional[Any] 					=					do_center_crop
											snake_case_					:		Dict 					=					crop_size
											snake_case_					:		int 					=					do_rescale
											snake_case_					:		Optional[int] 					=					rescale_factor
											snake_case_					:		str 					=					do_normalize
											snake_case_					:		str 					=					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
											snake_case_					:		List[str] 					=					image_std if image_std is not None else IMAGENET_DEFAULT_STD







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__ = None   ,		lowercase__ = PILImageResampling.BICUBIC   ,		lowercase__ = None   ,		**lowercase__   ,		):
											snake_case_					:		Tuple 					=					get_size_dict(lowercase__   ,		default_to_square=lowercase__       )
											if "shortest_edge" not in size and ("height" not in size or "width" not in size):
																raise ValueError(f'size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'       )
											if crop_pct is not None:
																if "shortest_edge" in size:
																					snake_case_					:		Optional[int] 					=					int(size["""shortest_edge"""] / crop_pct       )
																elif "height" in size and "width" in size:
																					if size["height"] == size["width"]:
																										snake_case_					:		Dict 					=					int(size["""height"""] / crop_pct       )
																					else:
																										snake_case_					:		List[str] 					=					(int(size["""height"""] / crop_pct       ), int(size["""width"""] / crop_pct       ))
																else:
																					raise ValueError("""Invalid size for resize: {}""".format(lowercase__       )       )

																snake_case_					:		List[Any] 					=					get_resize_output_image_size(lowercase__   ,		size=lowercase__   ,		default_to_square=lowercase__       )
											else:
																if "shortest_edge" in size:
																					snake_case_					:		Optional[int] 					=					get_resize_output_image_size(lowercase__   ,		size=size["""shortest_edge"""]   ,		default_to_square=lowercase__       )
																elif "height" in size and "width" in size:
																					snake_case_					:		int 					=					(size["""height"""], size["""width"""])
																else:
																					raise ValueError("""Invalid size for resize: {}""".format(lowercase__       )       )

											return resize(lowercase__   ,		size=lowercase__   ,		resample=lowercase__   ,		data_format=lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__ = None   ,		**lowercase__   ,		):
											snake_case_					:		int 					=					get_size_dict(lowercase__       )
											if "height" not in size or "width" not in size:
																raise ValueError(f'size must contain \'height\' and \'width\' as keys. Got {size.keys()}'       )
											return center_crop(lowercase__   ,		size=(size["""height"""], size["""width"""])   ,		data_format=lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__ = None   ,		**lowercase__   ,		):
											return rescale(lowercase__   ,		scale=lowercase__   ,		data_format=lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__ = None   ,		**lowercase__   ,		):
											return normalize(lowercase__   ,		mean=lowercase__   ,		std=lowercase__   ,		data_format=lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = ChannelDimension.FIRST   ,		**lowercase__   ,		):
											snake_case_					:		str 					=					do_resize if do_resize is not None else self.do_resize
											snake_case_					:		Any 					=					crop_pct if crop_pct is not None else self.crop_pct
											snake_case_					:		List[Any] 					=					resample if resample is not None else self.resample
											snake_case_					:		str 					=					do_center_crop if do_center_crop is not None else self.do_center_crop
											snake_case_					:		str 					=					do_rescale if do_rescale is not None else self.do_rescale
											snake_case_					:		Any 					=					rescale_factor if rescale_factor is not None else self.rescale_factor
											snake_case_					:		str 					=					do_normalize if do_normalize is not None else self.do_normalize
											snake_case_					:		List[Any] 					=					image_mean if image_mean is not None else self.image_mean
											snake_case_					:		int 					=					image_std if image_std is not None else self.image_std

											snake_case_					:		List[Any] 					=					size if size is not None else self.size
											snake_case_					:		Optional[Any] 					=					get_size_dict(lowercase__   ,		default_to_square=lowercase__       )
											snake_case_					:		List[Any] 					=					crop_size if crop_size is not None else self.crop_size
											snake_case_					:		int 					=					get_size_dict(lowercase__   ,		param_name="""crop_size"""       )

											snake_case_					:		List[str] 					=					make_list_of_images(lowercase__       )

											if not valid_images(lowercase__       ):
																raise ValueError(
																    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																    """torch.Tensor, tf.Tensor or jax.ndarray."""       )

											if do_resize and size is None or resample is None:
																raise ValueError("""Size and resample must be specified if do_resize is True."""       )

											if do_center_crop and crop_pct is None:
																raise ValueError("""Crop_pct must be specified if do_center_crop is True."""       )

											if do_rescale and rescale_factor is None:
																raise ValueError("""Rescale factor must be specified if do_rescale is True."""       )

											if do_normalize and (image_mean is None or image_std is None):
																raise ValueError("""Image mean and std must be specified if do_normalize is True."""       )

											# All transformations expect numpy arrays.
											snake_case_					:		int 					=					[to_numpy_array(lowercase__       ) for image in images]

											if do_resize:
																snake_case_					:		str 					=					[self.resize(image=lowercase__   ,		size=lowercase__   ,		crop_pct=lowercase__   ,		resample=lowercase__       ) for image in images]

											if do_center_crop:
																snake_case_					:		Optional[int] 					=					[self.center_crop(image=lowercase__   ,		size=lowercase__       ) for image in images]

											if do_rescale:
																snake_case_					:		List[Any] 					=					[self.rescale(image=lowercase__   ,		scale=lowercase__       ) for image in images]

											if do_normalize:
																snake_case_					:		Optional[Any] 					=					[self.normalize(image=lowercase__   ,		mean=lowercase__   ,		std=lowercase__       ) for image in images]

											snake_case_					:		List[Any] 					=					[to_channel_dimension_format(lowercase__   ,		lowercase__       ) for image in images]

											snake_case_					:		Dict 					=					{"""pixel_values""": images}
											return BatchFeature(data=lowercase__   ,		tensor_type=lowercase__       )




