



'''simple docstring'''
# Imports
import numpy as np






class 				lowercase__      :





	def __init__(					self   :							List[str]					,lowerCamelCase__   :							Dict=None					,lowerCamelCase__   :							Dict=None					,lowerCamelCase__   :							Union[str, Any]=None					,lowerCamelCase__   :							str=None					,lowerCamelCase__   :							List[Any]=None					):







							'''simple docstring'''
							self.set_matricies(red=lowerCamelCase__					,green=lowerCamelCase__					,blue=lowerCamelCase__					,red_edge=lowerCamelCase__					,nir=lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							List[Any]					,lowerCamelCase__   :							List[Any]=None					,lowerCamelCase__   :							int=None					,lowerCamelCase__   :							Optional[int]=None					,lowerCamelCase__   :							Union[str, Any]=None					,lowerCamelCase__   :							Optional[Any]=None					):







							'''simple docstring'''
							if red is not None:
													_UpperCamelCase   :						Optional[int]												=  red
							if green is not None:
													_UpperCamelCase   :						List[Any]												=  green
							if blue is not None:
													_UpperCamelCase   :						Any												=  blue
							if red_edge is not None:
													_UpperCamelCase   :						List[str]												=  red_edge
							if nir is not None:
													_UpperCamelCase   :						Any												=  nir
							return True





	def       UpperCamelCase_    (					self   :							List[Any]					,lowerCamelCase__   :							Optional[Any]=""					,lowerCamelCase__   :							List[str]=None					,lowerCamelCase__   :							Optional[int]=None					,lowerCamelCase__   :							List[str]=None					,lowerCamelCase__   :							int=None					,lowerCamelCase__   :							Dict=None					):







							'''simple docstring'''
							self.set_matricies(red=lowerCamelCase__					,green=lowerCamelCase__					,blue=lowerCamelCase__					,red_edge=lowerCamelCase__					,nir=lowerCamelCase__					)
							_UpperCamelCase   :						List[str]												=  {
							    '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   :							int					):







							'''simple docstring'''
							return -0.1_8 + (1.1_7 * ((self.nir - self.red) / (self.nir + self.red)))





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
							    (self.nir - self.red) / (self.nir + self.red)
							)





	def       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							return self.nir * (self.red / (self.green**2))





	def       UpperCamelCase_    (					self   :							Dict					):







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





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							return (self.nir - self.red) / (self.nir + self.red)





	def       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							return (self.nir - self.blue) / (self.nir + self.blue)





	def       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							return (self.redEdge - self.red) / (self.redEdge + self.red)





	def       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							return (self.nir - self.green) / (self.nir + self.green)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							return (self.nir - (self.green + self.blue)) / (
							    self.nir + (self.green + self.blue)
							)





	def       UpperCamelCase_    (					self   :							Any					):







							'''simple docstring'''
							return (self.nir - (self.green + self.red)) / (
							    self.nir + (self.green + self.red)
							)





	def       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))





	def       UpperCamelCase_    (					self   :							Optional[int]					):







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





	def       UpperCamelCase_    (					self   :							Optional[int]					,lowerCamelCase__   :							str=0.0_8					,lowerCamelCase__   :							List[str]=1.2_2					,lowerCamelCase__   :							Tuple=0.0_3					):







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





	def       UpperCamelCase_    (					self   :							str					):







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





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







							'''simple docstring'''
							return (self.nir / self.green) - 1





	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							return (self.nir / self.redEdge) - 1





	def       UpperCamelCase_    (					self   :							Any					):







							'''simple docstring'''
							return (self.red - self.blue) / self.red





	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						str												=  self.ndvi()
							return ((ndvi + 0.5) / (abs(ndvi + 0.5					))) * (abs(ndvi + 0.5					) ** (1 / 2))





	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							return self.nir - self.green





	def       UpperCamelCase_    (					self   :							List[str]					):







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





	def       UpperCamelCase_    (					self   :							Any					):







							'''simple docstring'''
							_UpperCamelCase   :						Union[str, 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 - 0.2_5 * n) - (self.red - 0.1_2_5) / (1 - self.red)





	def       UpperCamelCase_    (					self   :							Tuple					,lowerCamelCase__   :							Tuple=0.1_6					):







							'''simple docstring'''
							return (self.nir - self.green) / (self.nir + self.green + y)





	def       UpperCamelCase_    (					self   :							Union[str, Any]					,lowerCamelCase__   :							List[str]=0.5					):







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





	def       UpperCamelCase_    (					self   :							Optional[Any]					):







							'''simple docstring'''
							return np.arctan(
							    ((2 * self.red - self.green - self.blue) / 3_0.5) * (self.green - self.blue)					)





	def       UpperCamelCase_    (					self   :							str					,lowerCamelCase__   :							Tuple=None					,lowerCamelCase__   :							List[Any]=None					):







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





	def       UpperCamelCase_    (					self   :							Dict					):







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





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							return (self.red + self.green + self.blue) / 3_0.5





	def       UpperCamelCase_    (					self   :							Optional[Any]					):







							'''simple docstring'''
							return self.nir / self.red





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							return (self.rvi() - 1) / (self.rvi() + 1)





	def       UpperCamelCase_    (					self   :							Optional[int]					):







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





	def       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							return self.green / (self.nir + self.red + self.green)





	def       UpperCamelCase_    (					self   :							Optional[Any]					):







							'''simple docstring'''
							return self.nir / (self.nir + self.red + self.green)





	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							return self.red / (self.nir + self.red + self.green)





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







							'''simple docstring'''
							return (self.green - self.red) / (self.green + self.red)





	def       UpperCamelCase_    (					self   :							Any					):







							'''simple docstring'''
							return (self.red - self.green) / (self.red + self.green)





	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Union[str, Any]												=  np.max([np.max(self.red					), np.max(self.green					), np.max(self.blue					)]					)
							_UpperCamelCase   :						List[str]												=  np.min([np.min(self.red					), np.min(self.green					), np.min(self.blue					)]					)
							return (max_value - min_value) / max_value





	def       UpperCamelCase_    (					self   :							Dict					):







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





	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							return self.nir / self.red





	def       UpperCamelCase_    (					self   :							List[Any]					):







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





	def       UpperCamelCase_    (					self   :							Optional[Any]					):







							'''simple docstring'''
							return (self.nir - self.redEdge) / (self.nir + self.redEdge)







'''simple docstring'''
from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
					import PIL

# soft dependency
if is_pytesseract_available():
					import pytesseract

snake_case_					:      List[str]    			=    logging.get_logger(__name__)






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						return [
						    int(1_0_0_0 * (box[0] / width)				),
						    int(1_0_0_0 * (box[1] / height)				),
						    int(1_0_0_0 * (box[2] / width)				),
						    int(1_0_0_0 * (box[3] / height)				),
						]






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_ = None				):
						_UpperCamelCase   :						Optional[int]												=  tesseract_config if tesseract_config is not None else ''

						# apply OCR
						_UpperCamelCase   :						int												=  to_pil_image(UpperCAmelCase_				)
						_UpperCamelCase							,     _UpperCamelCase   :						Dict												=  pil_image.size
						_UpperCamelCase   :						Tuple												=  pytesseract.image_to_data(UpperCAmelCase_    ,    lang=UpperCAmelCase_    ,    output_type='dict'    ,    config=UpperCAmelCase_				)
						_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase   :						int												=  data['text'], data['left'], data['top'], data['width'], data['height']

						# filter empty words and corresponding coordinates
						_UpperCamelCase   :						Optional[Any]												=  [idx for idx, word in enumerate(UpperCAmelCase_				) if not word.strip()]
						_UpperCamelCase   :						Any												=  [word for idx, word in enumerate(UpperCAmelCase_				) if idx not in irrelevant_indices]
						_UpperCamelCase   :						Any												=  [coord for idx, coord in enumerate(UpperCAmelCase_				) if idx not in irrelevant_indices]
						_UpperCamelCase   :						Union[str, Any]												=  [coord for idx, coord in enumerate(UpperCAmelCase_				) if idx not in irrelevant_indices]
						_UpperCamelCase   :						Union[str, Any]												=  [coord for idx, coord in enumerate(UpperCAmelCase_				) if idx not in irrelevant_indices]
						_UpperCamelCase   :						List[str]												=  [coord for idx, coord in enumerate(UpperCAmelCase_				) if idx not in irrelevant_indices]

						# turn coordinates into (left, top, left+width, top+height) format
						_UpperCamelCase   :						Tuple												=  []
						for x, y, w, h in zip(UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
												_UpperCamelCase   :						List[str]												=  [x, y, x + w, y + h]
												actual_boxes.append(UpperCAmelCase_				)

						# finally, normalize the bounding boxes
						_UpperCamelCase   :						List[Any]												=  []
						for box in actual_boxes:
												normalized_boxes.append(normalize_box(UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				)				)

						assert len(UpperCAmelCase_				) == len(UpperCAmelCase_				), "Not as many words as there are bounding boxes"

						return words, normalized_boxes






class 				lowercase__      (				lowercase  ):
	lowercase__    							=     ["""pixel_values"""]





	def __init__(					self   :							Dict					,lowerCamelCase__   :							bool = True					,lowerCamelCase__   :							Dict[str, int] = None					,lowerCamelCase__   :							PILImageResampling = PILImageResampling.BILINEAR					,lowerCamelCase__   :							bool = True					,lowerCamelCase__   :							Optional[str] = None					,lowerCamelCase__   :							Optional[str] = ""					,**lowerCamelCase__   :							List[Any]					,):







							'''simple docstring'''
							super().__init__(**lowerCamelCase__					)
							_UpperCamelCase   :						Optional[Any]												=  size if size is not None else {'height': 224, 'width': 224}
							_UpperCamelCase   :						Union[str, Any]												=  get_size_dict(lowerCamelCase__					)

							_UpperCamelCase   :						Optional[int]												=  do_resize
							_UpperCamelCase   :						str												=  size
							_UpperCamelCase   :						Any												=  resample
							_UpperCamelCase   :						List[str]												=  apply_ocr
							_UpperCamelCase   :						int												=  ocr_lang
							_UpperCamelCase   :						Union[str, Any]												=  tesseract_config





	def       UpperCamelCase_    (					self   :							List[Any]					,lowerCamelCase__   :							np.ndarray					,lowerCamelCase__   :							Dict[str, int]					,lowerCamelCase__   :							PILImageResampling = PILImageResampling.BILINEAR					,lowerCamelCase__   :							Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase__   :							Union[str, Any]					,):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[int]												=  get_size_dict(lowerCamelCase__					)
							if "height" not in size or "width" not in size:
													raise ValueError(F'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'					)
							_UpperCamelCase   :						List[str]												=  (size['height'], size['width'])
							return resize(lowerCamelCase__					,size=lowerCamelCase__					,resample=lowerCamelCase__					,data_format=lowerCamelCase__					,**lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							List[str]					,lowerCamelCase__   :							ImageInput					,lowerCamelCase__   :							bool = None					,lowerCamelCase__   :							Dict[str, int] = None					,lowerCamelCase__   :							PILImageResampling = None					,lowerCamelCase__   :							bool = None					,lowerCamelCase__   :							Optional[str] = None					,lowerCamelCase__   :							Optional[str] = None					,lowerCamelCase__   :							Optional[Union[str, TensorType]] = None					,lowerCamelCase__   :							ChannelDimension = ChannelDimension.FIRST					,**lowerCamelCase__   :							List[Any]					,):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  do_resize if do_resize is not None else self.do_resize
							_UpperCamelCase   :						Tuple												=  size if size is not None else self.size
							_UpperCamelCase   :						Optional[int]												=  get_size_dict(lowerCamelCase__					)
							_UpperCamelCase   :						Optional[Any]												=  resample if resample is not None else self.resample
							_UpperCamelCase   :						Union[str, Any]												=  apply_ocr if apply_ocr is not None else self.apply_ocr
							_UpperCamelCase   :						Union[str, Any]												=  ocr_lang if ocr_lang is not None else self.ocr_lang
							_UpperCamelCase   :						int												=  tesseract_config if tesseract_config is not None else self.tesseract_config

							_UpperCamelCase   :						Dict												=  make_list_of_images(lowerCamelCase__					)

							if not valid_images(lowerCamelCase__					):
													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:
													raise ValueError('Size must be specified if do_resize is True.'					)

							# All transformations expect numpy arrays.
							_UpperCamelCase   :						Union[str, Any]												=  [to_numpy_array(lowerCamelCase__					) for image in images]

							if apply_ocr:
													requires_backends(self					,'pytesseract'					)
													_UpperCamelCase   :						Tuple												=  []
													_UpperCamelCase   :						Tuple												=  []
													for image in images:
																			_UpperCamelCase							,     _UpperCamelCase   :						Optional[Any]												=  apply_tesseract(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					)
																			words_batch.append(lowerCamelCase__					)
																			boxes_batch.append(lowerCamelCase__					)

							if do_resize:
													_UpperCamelCase   :						Any												=  [self.resize(image=lowerCamelCase__					,size=lowerCamelCase__					,resample=lowerCamelCase__					) for image in images]

							# flip color channels from RGB to BGR (as Detectron2 requires this)
							_UpperCamelCase   :						Optional[int]												=  [flip_channel_order(lowerCamelCase__					) for image in images]
							_UpperCamelCase   :						Dict												=  [to_channel_dimension_format(lowerCamelCase__					,lowerCamelCase__					) for image in images]

							_UpperCamelCase   :						Union[str, Any]												=  BatchFeature(data={'pixel_values': images}					,tensor_type=lowerCamelCase__					)

							if apply_ocr:
													_UpperCamelCase   :						Dict												=  words_batch
													_UpperCamelCase   :						str												=  boxes_batch
							return data


