

'''simple docstring'''




import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

				if is_vision_available():
								from transformers import OneFormerImageProcessor
								from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
								from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
				from PIL import Image
def       A__	(							A  :     Any							,       A  :     Tuple="shi-labs/oneformer_demo"):


			'''simple docstring'''


			with open(hf_hub_download(A							,       A							,       repo_type="dataset")							,       "r") as f:
						UpperCamelCase				:	List[str]    						=					json.load(A)
			UpperCamelCase				:	Tuple    						=					{}
			UpperCamelCase				:	Tuple    						=					[]
			UpperCamelCase				:	str    						=					[]
			for key, info in class_info.items():
						UpperCamelCase				:	Any    						=					info["name"]
						class_names.append(info["name"])
						if info["isthing"]:
									thing_ids.append(int(A))
			UpperCamelCase				:	Optional[Any]    						=					thing_ids
			UpperCamelCase				:	int    						=					class_names
			return metadata

class     UpperCAmelCase_ (	unittest.TestCase    ):



	"""simple docstring"""





	def __init__(    self				,			lowerCamelCase				,			lowerCamelCase=7				,			lowerCamelCase=3				,			lowerCamelCase=30				,			lowerCamelCase=4_00				,			lowerCamelCase=None				,			lowerCamelCase=True				,			lowerCamelCase=True				,			lowerCamelCase=[0.5, 0.5, 0.5]				,			lowerCamelCase=[0.5, 0.5, 0.5]				,			lowerCamelCase=10				,			lowerCamelCase=False				,			lowerCamelCase=2_55				,			lowerCamelCase="shi-labs/oneformer_demo"				,			lowerCamelCase="ade20k_panoptic.json"				,			lowerCamelCase=10				,			)							->     Dict:




				'''simple docstring'''






				UpperCamelCase				:	List[Any]    						=					parent
				UpperCamelCase				:	Optional[int]    						=					batch_size
				UpperCamelCase				:	Any    						=					num_channels
				UpperCamelCase				:	Any    						=					min_resolution
				UpperCamelCase				:	int    						=					max_resolution
				UpperCamelCase				:	int    						=					do_resize
				UpperCamelCase				:	Optional[int]    						=					{"shortest_edge": 32, "longest_edge": 13_33} if size is None else size
				UpperCamelCase				:	List[Any]    						=					do_normalize
				UpperCamelCase				:	Optional[Any]    						=					image_mean
				UpperCamelCase				:	Optional[int]    						=					image_std
				UpperCamelCase				:	List[str]    						=					class_info_file
				UpperCamelCase				:	Optional[Any]    						=					prepare_metadata(lowerCAmelCase__				,			lowerCAmelCase__   )
				UpperCamelCase				:	List[str]    						=					num_text
				UpperCamelCase				:	Optional[Any]    						=					repo_path

				# for the post_process_functions
				UpperCamelCase				:	str    						=					2
				UpperCamelCase				:	int    						=					10
				UpperCamelCase				:	Dict    						=					10
				UpperCamelCase				:	Union[str, Any]    						=					3
				UpperCamelCase				:	Any    						=					4
				UpperCamelCase				:	Dict    						=					num_labels
				UpperCamelCase				:	Dict    						=					do_reduce_labels
				UpperCamelCase				:	str    						=					ignore_index

	def 	SCREAMING_SNAKE_CASE__							(    self   )							->     Tuple:




				'''simple docstring'''






				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "num_labels": self.num_labels,
				    "do_reduce_labels": self.do_reduce_labels,
				    "ignore_index": self.ignore_index,
				    "class_info_file": self.class_info_file,
				    "metadata": self.metadata,
				    "num_text": self.num_text,
				}

	def 	SCREAMING_SNAKE_CASE__							(    self				,			lowerCamelCase				,			lowerCamelCase=False   )							->     List[Any]:




				'''simple docstring'''






				if not batched:
							UpperCamelCase				:	Any    						=					image_inputs[0]
							if isinstance(lowerCAmelCase__				,			Image.Image   ):
										UpperCamelCase						,   UpperCamelCase				:	Optional[Any]    						=					image.size
							else:
										UpperCamelCase						,   UpperCamelCase				:	int    						=					image.shape[1], image.shape[2]
							if w < h:
										UpperCamelCase				:	Optional[Any]    						=					int(self.size["shortest_edge"] * h / w   )
										UpperCamelCase				:	Optional[int]    						=					self.size["shortest_edge"]
							elif w > h:
										UpperCamelCase				:	Optional[Any]    						=					self.size["shortest_edge"]
										UpperCamelCase				:	str    						=					int(self.size["shortest_edge"] * w / h   )
							else:
										UpperCamelCase				:	Dict    						=					self.size["shortest_edge"]
										UpperCamelCase				:	Optional[Any]    						=					self.size["shortest_edge"]

				else:
							UpperCamelCase				:	str    						=					[]
							for image in image_inputs:
										UpperCamelCase						,   UpperCamelCase				:	Union[str, Any]    						=					self.get_expected_values([image]   )
										expected_values.append((expected_height, expected_width)   )
							UpperCamelCase				:	Any    						=					max(lowerCAmelCase__				,			key=lambda lowerCamelCase   : item[0]   )[0]
							UpperCamelCase				:	int    						=					max(lowerCAmelCase__				,			key=lambda lowerCamelCase   : item[1]   )[1]

				return expected_height, expected_width



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




				'''simple docstring'''






				return OneFormerForUniversalSegmentationOutput(
				    # +1 for null class
				    class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)   )				,			masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)   )				,			)

@require_torch
@require_vision
class     UpperCAmelCase_ (	__a				,	unittest.TestCase    ):



	"""simple docstring"""





	__SCREAMING_SNAKE_CASE					=       OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
	# only for test_image_processing_common.test_image_proc_to_json_string
	__SCREAMING_SNAKE_CASE					=       image_processing_class

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




				'''simple docstring'''






				UpperCamelCase				:	Tuple    						=					OneFormerImageProcessorTester(self   )

	@property
	def 	SCREAMING_SNAKE_CASE__							(    self   )							->     List[str]:




				'''simple docstring'''






				return self.image_processing_tester.prepare_image_processor_dict()

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




				'''simple docstring'''






				UpperCamelCase				:	Optional[int]    						=					self.image_processing_class(**self.image_processor_dict   )
				self.assertTrue(hasattr(lowerCAmelCase__				,			"image_mean"   )   )
				self.assertTrue(hasattr(lowerCAmelCase__				,			"image_std"   )   )
				self.assertTrue(hasattr(lowerCAmelCase__				,			"do_normalize"   )   )
				self.assertTrue(hasattr(lowerCAmelCase__				,			"do_resize"   )   )
				self.assertTrue(hasattr(lowerCAmelCase__				,			"size"   )   )
				self.assertTrue(hasattr(lowerCAmelCase__				,			"ignore_index"   )   )
				self.assertTrue(hasattr(lowerCAmelCase__				,			"class_info_file"   )   )
				self.assertTrue(hasattr(lowerCAmelCase__				,			"num_text"   )   )
				self.assertTrue(hasattr(lowerCAmelCase__				,			"repo_path"   )   )
				self.assertTrue(hasattr(lowerCAmelCase__				,			"metadata"   )   )
				self.assertTrue(hasattr(lowerCAmelCase__				,			"do_reduce_labels"   )   )

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




				'''simple docstring'''






				pass

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




				'''simple docstring'''






				UpperCamelCase				:	Optional[int]    						=					self.image_processing_class(**self.image_processor_dict   )
				# create random PIL images
				UpperCamelCase				:	Union[str, Any]    						=					prepare_image_inputs(self.image_processing_tester				,			equal_resolution=lowerCAmelCase__   )
				for image in image_inputs:
							self.assertIsInstance(lowerCAmelCase__				,			Image.Image   )

				# Test not batched input
				UpperCamelCase				:	Optional[int]    						=					image_processor(image_inputs[0]				,			["semantic"]				,			return_tensors="pt"   ).pixel_values

				UpperCamelCase						,   UpperCamelCase				:	List[str]    						=					self.image_processing_tester.get_expected_values(lowerCAmelCase__   )

				self.assertEqual(
				    encoded_images.shape				,			(1, self.image_processing_tester.num_channels, expected_height, expected_width)				,			)

				# Test batched
				UpperCamelCase						,   UpperCamelCase				:	List[str]    						=					self.image_processing_tester.get_expected_values(lowerCAmelCase__				,			batched=lowerCAmelCase__   )

				UpperCamelCase				:	Optional[Any]    						=					image_processor(
				    lowerCAmelCase__				,			["semantic"] * len(lowerCAmelCase__   )				,			return_tensors="pt"   ).pixel_values
				self.assertEqual(
				    encoded_images.shape				,			(
				        self.image_processing_tester.batch_size,
				        self.image_processing_tester.num_channels,
				        expected_height,
				        expected_width,
				    )				,			)

	def 	SCREAMING_SNAKE_CASE__							(    self   )							->     int:




				'''simple docstring'''






				UpperCamelCase				:	int    						=					self.image_processing_class(**self.image_processor_dict   )
				# create random numpy tensors
				UpperCamelCase				:	Optional[Any]    						=					prepare_image_inputs(self.image_processing_tester				,			equal_resolution=lowerCAmelCase__				,			numpify=lowerCAmelCase__   )
				for image in image_inputs:
							self.assertIsInstance(lowerCAmelCase__				,			np.ndarray   )

				# Test not batched input
				UpperCamelCase				:	int    						=					image_processor(image_inputs[0]				,			["semantic"]				,			return_tensors="pt"   ).pixel_values

				UpperCamelCase						,   UpperCamelCase				:	List[Any]    						=					self.image_processing_tester.get_expected_values(lowerCAmelCase__   )

				self.assertEqual(
				    encoded_images.shape				,			(1, self.image_processing_tester.num_channels, expected_height, expected_width)				,			)

				# Test batched
				UpperCamelCase						,   UpperCamelCase				:	int    						=					self.image_processing_tester.get_expected_values(lowerCAmelCase__				,			batched=lowerCAmelCase__   )

				UpperCamelCase				:	List[Any]    						=					image_processor(
				    lowerCAmelCase__				,			["semantic"] * len(lowerCAmelCase__   )				,			return_tensors="pt"   ).pixel_values
				self.assertEqual(
				    encoded_images.shape				,			(
				        self.image_processing_tester.batch_size,
				        self.image_processing_tester.num_channels,
				        expected_height,
				        expected_width,
				    )				,			)

	def 	SCREAMING_SNAKE_CASE__							(    self   )							->     int:




				'''simple docstring'''






				UpperCamelCase				:	List[Any]    						=					self.image_processing_class(**self.image_processor_dict   )
				# create random PyTorch tensors
				UpperCamelCase				:	Tuple    						=					prepare_image_inputs(self.image_processing_tester				,			equal_resolution=lowerCAmelCase__				,			torchify=lowerCAmelCase__   )
				for image in image_inputs:
							self.assertIsInstance(lowerCAmelCase__				,			torch.Tensor   )

				# Test not batched input
				UpperCamelCase				:	int    						=					image_processor(image_inputs[0]				,			["semantic"]				,			return_tensors="pt"   ).pixel_values

				UpperCamelCase						,   UpperCamelCase				:	Optional[Any]    						=					self.image_processing_tester.get_expected_values(lowerCAmelCase__   )

				self.assertEqual(
				    encoded_images.shape				,			(1, self.image_processing_tester.num_channels, expected_height, expected_width)				,			)

				# Test batched
				UpperCamelCase						,   UpperCamelCase				:	str    						=					self.image_processing_tester.get_expected_values(lowerCAmelCase__				,			batched=lowerCAmelCase__   )

				UpperCamelCase				:	List[str]    						=					image_processor(
				    lowerCAmelCase__				,			["semantic"] * len(lowerCAmelCase__   )				,			return_tensors="pt"   ).pixel_values
				self.assertEqual(
				    encoded_images.shape				,			(
				        self.image_processing_tester.batch_size,
				        self.image_processing_tester.num_channels,
				        expected_height,
				        expected_width,
				    )				,			)

	def 	SCREAMING_SNAKE_CASE__							(    self				,			lowerCamelCase=False				,			lowerCamelCase=False				,			lowerCamelCase="np"   )							->     Optional[Any]:




				'''simple docstring'''






				UpperCamelCase				:	Dict    						=					self.image_processing_class(**self.image_processor_dict   )
				# prepare image and target
				UpperCamelCase				:	Any    						=					self.image_processing_tester.num_labels
				UpperCamelCase				:	int    						=					None
				UpperCamelCase				:	Dict    						=					None
				UpperCamelCase				:	List[Any]    						=					prepare_image_inputs(self.image_processing_tester				,			equal_resolution=lowerCAmelCase__   )
				if with_segmentation_maps:
							UpperCamelCase				:	List[str]    						=					num_labels
							if is_instance_map:
										UpperCamelCase				:	Tuple    						=					list(range(lowerCAmelCase__   )   ) * 2
										UpperCamelCase				:	str    						=					dict(enumerate(lowerCAmelCase__   )   )
							UpperCamelCase				:	Union[str, Any]    						=					[
							    np.random.randint(0				,			high * 2				,			(img.size[1], img.size[0])   ).astype(np.uinta   ) for img in image_inputs
							]
							if segmentation_type == "pil":
										UpperCamelCase				:	Optional[Any]    						=					[Image.fromarray(lowerCAmelCase__   ) for annotation in annotations]

				UpperCamelCase				:	List[str]    						=					image_processor(
				    lowerCAmelCase__				,			["semantic"] * len(lowerCAmelCase__   )				,			lowerCAmelCase__				,			return_tensors="pt"				,			instance_id_to_semantic_id=lowerCAmelCase__				,			pad_and_return_pixel_mask=lowerCAmelCase__				,			)

				return inputs

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




				'''simple docstring'''






				pass

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




				'''simple docstring'''






				def common(lowerCamelCase=False				,			lowerCamelCase=None   ):
							UpperCamelCase				:	Dict    						=					self.comm_get_image_processor_inputs(
							    with_segmentation_maps=lowerCAmelCase__				,			is_instance_map=lowerCAmelCase__				,			segmentation_type=lowerCAmelCase__   )

							UpperCamelCase				:	str    						=					inputs["mask_labels"]
							UpperCamelCase				:	Optional[Any]    						=					inputs["class_labels"]
							UpperCamelCase				:	Optional[Any]    						=					inputs["pixel_values"]
							UpperCamelCase				:	Tuple    						=					inputs["text_inputs"]

							# check the batch_size
							for mask_label, class_label, text_input in zip(lowerCAmelCase__				,			lowerCAmelCase__				,			lowerCAmelCase__   ):
										self.assertEqual(mask_label.shape[0]				,			class_label.shape[0]   )
										# this ensure padding has happened
										self.assertEqual(mask_label.shape[1:]				,			pixel_values.shape[2:]   )
										self.assertEqual(len(lowerCAmelCase__   )				,			self.image_processing_tester.num_text   )

				common()
				common(is_instance_map=lowerCAmelCase__   )
				common(is_instance_map=lowerCAmelCase__				,			segmentation_type="pil"   )
				common(is_instance_map=lowerCAmelCase__				,			segmentation_type="pil"   )

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




				'''simple docstring'''






				UpperCamelCase				:	Optional[Any]    						=					np.zeros((20, 50)   )
				UpperCamelCase				:	Any    						=					1
				UpperCamelCase				:	str    						=					1
				UpperCamelCase				:	List[Any]    						=					1

				UpperCamelCase				:	Tuple    						=					binary_mask_to_rle(lowerCAmelCase__   )
				self.assertEqual(len(lowerCAmelCase__   )				,			4   )
				self.assertEqual(rle[0]				,			21   )
				self.assertEqual(rle[1]				,			45   )

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




				'''simple docstring'''






				UpperCamelCase				:	str    						=					self.image_processing_class(
				    num_labels=self.image_processing_tester.num_classes				,			max_seq_length=77				,			task_seq_length=77				,			class_info_file="ade20k_panoptic.json"				,			num_text=self.image_processing_tester.num_text				,			repo_path="shi-labs/oneformer_demo"				,			)
				UpperCamelCase				:	Dict    						=					self.image_processing_tester.get_fake_oneformer_outputs()

				UpperCamelCase				:	List[str]    						=					fature_extractor.post_process_semantic_segmentation(lowerCAmelCase__   )

				self.assertEqual(len(lowerCAmelCase__   )				,			self.image_processing_tester.batch_size   )
				self.assertEqual(
				    segmentation[0].shape				,			(
				        self.image_processing_tester.height,
				        self.image_processing_tester.width,
				    )				,			)

				UpperCamelCase				:	Optional[int]    						=					[(1, 4) for i in range(self.image_processing_tester.batch_size   )]
				UpperCamelCase				:	Any    						=					fature_extractor.post_process_semantic_segmentation(lowerCAmelCase__				,			target_sizes=lowerCAmelCase__   )

				self.assertEqual(segmentation[0].shape				,			target_sizes[0]   )

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




				'''simple docstring'''






				UpperCamelCase				:	Any    						=					self.image_processing_class(
				    num_labels=self.image_processing_tester.num_classes				,			max_seq_length=77				,			task_seq_length=77				,			class_info_file="ade20k_panoptic.json"				,			num_text=self.image_processing_tester.num_text				,			repo_path="shi-labs/oneformer_demo"				,			)
				UpperCamelCase				:	List[Any]    						=					self.image_processing_tester.get_fake_oneformer_outputs()
				UpperCamelCase				:	Optional[Any]    						=					image_processor.post_process_instance_segmentation(lowerCAmelCase__				,			threshold=0   )

				self.assertTrue(len(lowerCAmelCase__   ) == self.image_processing_tester.batch_size   )
				for el in segmentation:
							self.assertTrue("segmentation" in el   )
							self.assertTrue("segments_info" in el   )
							self.assertEqual(type(el["segments_info"]   )				,			lowerCAmelCase__   )
							self.assertEqual(
							    el["segmentation"].shape				,			(self.image_processing_tester.height, self.image_processing_tester.width)   )



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




				'''simple docstring'''






				UpperCamelCase				:	int    						=					self.image_processing_class(
				    num_labels=self.image_processing_tester.num_classes				,			max_seq_length=77				,			task_seq_length=77				,			class_info_file="ade20k_panoptic.json"				,			num_text=self.image_processing_tester.num_text				,			repo_path="shi-labs/oneformer_demo"				,			)
				UpperCamelCase				:	Any    						=					self.image_processing_tester.get_fake_oneformer_outputs()
				UpperCamelCase				:	Union[str, Any]    						=					image_processor.post_process_panoptic_segmentation(lowerCAmelCase__				,			threshold=0   )

				self.assertTrue(len(lowerCAmelCase__   ) == self.image_processing_tester.batch_size   )
				for el in segmentation:
							self.assertTrue("segmentation" in el   )
							self.assertTrue("segments_info" in el   )
							self.assertEqual(type(el["segments_info"]   )				,			lowerCAmelCase__   )
							self.assertEqual(
							    el["segmentation"].shape				,			(self.image_processing_tester.height, self.image_processing_tester.width)   )


# Imports
import numpy as np





class   UpperCamelCase_      :







						'''simple docstring'''



						def __init__(			self							:Optional[int]		,    lowerCAmelCase__							:int=None		,    lowerCAmelCase__							:List[str]=None		,    lowerCAmelCase__							:int=None		,    lowerCAmelCase__							:Optional[int]=None		,    lowerCAmelCase__							:List[str]=None     )  ->int:
										self.set_matricies(red=lowerCAmelCase__		,    green=lowerCAmelCase__		,    blue=lowerCAmelCase__		,    red_edge=lowerCAmelCase__		,    nir=lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]		,    lowerCAmelCase__							:List[Any]=None		,    lowerCAmelCase__							:Any=None		,    lowerCAmelCase__							:Tuple=None		,    lowerCAmelCase__							:Optional[Any]=None		,    lowerCAmelCase__							:Optional[Any]=None     )  ->List[Any]:
										if red is not None:
														lowercase         =      red
										if green is not None:
														lowercase         =      green
										if blue is not None:
														lowercase         =      blue
										if red_edge is not None:
														lowercase         =      red_edge
										if nir is not None:
														lowercase         =      nir
										return True





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]		,    lowerCAmelCase__							:Dict=""		,    lowerCAmelCase__							:Optional[Any]=None		,    lowerCAmelCase__							:Union[str, Any]=None		,    lowerCAmelCase__							:int=None		,    lowerCAmelCase__							:int=None		,    lowerCAmelCase__							:Union[str, Any]=None     )  ->Optional[Any]:
										self.set_matricies(red=lowerCAmelCase__		,    green=lowerCAmelCase__		,    blue=lowerCAmelCase__		,    red_edge=lowerCAmelCase__		,    nir=lowerCAmelCase__     )
										lowercase         =      {
										    "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        SCREAMING_SNAKE_CASE(			self							:Any     )  ->int:
										return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->int:
										return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
										    (self.nir - self.red) / (self.nir + self.red)
										)





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->Tuple:
										return self.nir * (self.red / (self.green**2))





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->Dict:
										return (2 * self.green - self.red - self.blue) / (
										    2 * self.green + self.red + self.blue
										)





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





						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->int:
										return (self.nir - self.blue) / (self.nir + self.blue)





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->List[str]:
										return (self.redEdge - self.red) / (self.redEdge + self.red)





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->str:
										return (self.nir - self.green) / (self.nir + self.green)





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->Any:
										return (self.nir - (self.green + self.blue)) / (
										    self.nir + (self.green + self.blue)
										)





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





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





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





						def        SCREAMING_SNAKE_CASE(			self							:Tuple		,    lowerCAmelCase__							:Union[str, Any]=0.08		,    lowerCAmelCase__							:Dict=1.22		,    lowerCAmelCase__							:Tuple=0.03     )  ->Tuple:
										return a * (
										    (self.nir - a * self.red - b)
										    / (a * self.nir + self.red - a * b + x * (1 + a**2))
										)





						def        SCREAMING_SNAKE_CASE(			self							:Dict     )  ->Any:
										return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->int:
										return (self.nir / self.green) - 1





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->List[str]:
										return (self.nir / self.redEdge) - 1





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->List[Any]:
										return (self.red - self.blue) / self.red





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->int:
										lowercase         =      self.ndvi()
										return ((ndvi + 0.5) / (abs(ndvi + 0.5     ))) * (abs(ndvi + 0.5     ) ** (1 / 2))





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->Optional[Any]:
										return self.nir - self.green





						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->int:
										return 2.5 * (
										    (self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
										)





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->Optional[Any]:
										lowercase         =      (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        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:Tuple=0.16     )  ->Any:
										return (self.nir - self.green) / (self.nir + self.green + y)





						def        SCREAMING_SNAKE_CASE(			self							:Dict		,    lowerCAmelCase__							:Any=0.5     )  ->str:
										return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)





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





						def        SCREAMING_SNAKE_CASE(			self							:Any		,    lowerCAmelCase__							:str=None		,    lowerCAmelCase__							:Tuple=None     )  ->Dict:
										return (self.nir - b) / (a * self.red)





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->Dict:
										return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)





						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->List[Any]:
										return (self.red + self.green + self.blue) / 30.5





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





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->Dict:
										return (self.rvi() - 1) / (self.rvi() + 1)





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->Dict:
										return (
										    (2 * self.nir + 1)
										    - ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
										) / 2





						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->int:
										return self.green / (self.nir + self.red + self.green)





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->Any:
										return self.nir / (self.nir + self.red + self.green)





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->Tuple:
										return self.red / (self.nir + self.red + self.green)





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





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





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->List[str]:
										lowercase         =      np.max([np.max(self.red     ), np.max(self.green     ), np.max(self.blue     )]     )
										lowercase         =      np.min([np.min(self.red     ), np.min(self.green     ), np.min(self.blue     )]     )
										return (max_value - min_value) / max_value





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->Any:
										return (2 * self.red - self.green - self.blue) / (self.green - self.blue)





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->List[Any]:
										return self.nir / self.red





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->List[str]:
										return (self.ndvi() + 0.5) ** (1 / 2)





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->Union[str, Any]:
										return (self.nir - self.redEdge) / (self.nir + self.redEdge)
