






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

lowercase    			=       logging.get_logger(__name__)






def _lowerCAmelCase	(  __lowerCamelCase:List[str] ,						__lowerCamelCase:int ,						__lowerCamelCase:Any   ):

						'''simple docstring'''






						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 _lowerCAmelCase	(  __lowerCamelCase:np.ndarray ,						__lowerCamelCase:Optional[str] ,						__lowerCamelCase:Optional[str] = None   ):

						'''simple docstring'''






						__magic_name__            =     tesseract_config if tesseract_config is not None else ""

						# apply OCR
						__magic_name__            =     to_pil_image(__lowerCamelCase   )
						__magic_name__      ,    __magic_name__            =     pil_image.size
						__magic_name__            =     pytesseract.image_to_data(__lowerCamelCase ,						lang=__lowerCamelCase ,						output_type="dict" ,						config=__lowerCamelCase   )
						__magic_name__      ,    __magic_name__      ,    __magic_name__      ,    __magic_name__      ,    __magic_name__            =     data["text"], data["left"], data["top"], data["width"], data["height"]

						# filter empty words and corresponding coordinates
						__magic_name__            =     [idx for idx, word in enumerate(__lowerCamelCase   ) if not word.strip()]
						__magic_name__            =     [word for idx, word in enumerate(__lowerCamelCase   ) if idx not in irrelevant_indices]
						__magic_name__            =     [coord for idx, coord in enumerate(__lowerCamelCase   ) if idx not in irrelevant_indices]
						__magic_name__            =     [coord for idx, coord in enumerate(__lowerCamelCase   ) if idx not in irrelevant_indices]
						__magic_name__            =     [coord for idx, coord in enumerate(__lowerCamelCase   ) if idx not in irrelevant_indices]
						__magic_name__            =     [coord for idx, coord in enumerate(__lowerCamelCase   ) if idx not in irrelevant_indices]

						# turn coordinates into (left, top, left+width, top+height) format
						__magic_name__            =     []
						for x, y, w, h in zip(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase   ):
												__magic_name__            =     [x, y, x + w, y + h]
												actual_boxes.append(__lowerCamelCase   )

						# finally, normalize the bounding boxes
						__magic_name__            =     []
						for box in actual_boxes:
												normalized_boxes.append(normalize_box(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase   )   )

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

						return words, normalized_boxes


class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				['''pixel_values''']







				def __init__(							self							:						Any						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						Dict[str, int] = None						,    __lowerCamelCase							:						PILImageResampling = PILImageResampling.BILINEAR						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						Optional[str] = None						,    __lowerCamelCase							:						Optional[str] = ""						,    **__lowerCamelCase							:						Optional[Any]						,    )				->					None:
										super().__init__(**__lowerCamelCase	)
										__magic_name__            =     size if size is not None else {"height": 2_2_4, "width": 2_2_4}
										__magic_name__            =     get_size_dict(__lowerCamelCase	)

										__magic_name__            =     do_resize
										__magic_name__            =     size
										__magic_name__            =     resample
										__magic_name__            =     apply_ocr
										__magic_name__            =     ocr_lang
										__magic_name__            =     tesseract_config







				def     _snake_case					(							self							:						Tuple						,    __lowerCamelCase							:						np.ndarray						,    __lowerCamelCase							:						Dict[str, int]						,    __lowerCamelCase							:						PILImageResampling = PILImageResampling.BILINEAR						,    __lowerCamelCase							:						Optional[Union[str, ChannelDimension]] = None						,    **__lowerCamelCase							:						str						,    )				->					np.ndarray:
										__magic_name__            =     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()}'''	)
										__magic_name__            =     (size["height"], size["width"])
										return resize(__lowerCamelCase						,    size=__lowerCamelCase						,    resample=__lowerCamelCase						,    data_format=__lowerCamelCase						,    **__lowerCamelCase	)







				def     _snake_case					(							self							:						Dict						,    __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							:						Any						,    )				->					PIL.Image.Image:
										__magic_name__            =     do_resize if do_resize is not None else self.do_resize
										__magic_name__            =     size if size is not None else self.size
										__magic_name__            =     get_size_dict(__lowerCamelCase	)
										__magic_name__            =     resample if resample is not None else self.resample
										__magic_name__            =     apply_ocr if apply_ocr is not None else self.apply_ocr
										__magic_name__            =     ocr_lang if ocr_lang is not None else self.ocr_lang
										__magic_name__            =     tesseract_config if tesseract_config is not None else self.tesseract_config

										__magic_name__            =     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.
										__magic_name__            =     [to_numpy_array(__lowerCamelCase	) for image in images]

										if apply_ocr:
																requires_backends(self						,    "pytesseract"	)
																__magic_name__            =     []
																__magic_name__            =     []
																for image in images:
																						__magic_name__      ,    __magic_name__            =     apply_tesseract(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)
																						words_batch.append(__lowerCamelCase	)
																						boxes_batch.append(__lowerCamelCase	)

										if do_resize:
																__magic_name__            =     [self.resize(image=__lowerCamelCase						,    size=__lowerCamelCase						,    resample=__lowerCamelCase	) for image in images]

										# flip color channels from RGB to BGR (as Detectron2 requires this)
										__magic_name__            =     [flip_channel_order(__lowerCamelCase	) for image in images]
										__magic_name__            =     [to_channel_dimension_format(__lowerCamelCase						,    __lowerCamelCase	) for image in images]

										__magic_name__            =     BatchFeature(data={"pixel_values": images}						,    tensor_type=__lowerCamelCase	)

										if apply_ocr:
																__magic_name__            =     words_batch
																__magic_name__            =     boxes_batch
										return data











"""simple docstring"""

import colorsys

from PIL import Image  # type: ignore






def _lowerCAmelCase	(  __lowerCamelCase:float ,						__lowerCamelCase:float ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     x
						__magic_name__            =     y
						for step in range(__lowerCamelCase   ):  # noqa: B007
												__magic_name__            =     a * a - b * b + x
												__magic_name__            =     2 * a * b + y
												__magic_name__            =     a_new

												# divergence happens for all complex number with an absolute value
												# greater than 4
												if a * a + b * b > 4:
																		break
						return step / (max_step - 1)






def _lowerCAmelCase	(  __lowerCamelCase:float   ):

						'''simple docstring'''






						if distance == 1:
												return (0, 0, 0)
						else:
												return (2_5_5, 2_5_5, 2_5_5)






def _lowerCAmelCase	(  __lowerCamelCase:float   ):

						'''simple docstring'''






						if distance == 1:
												return (0, 0, 0)
						else:
												return tuple(round(i * 2_5_5   ) for i in colorsys.hsv_to_rgb(__lowerCamelCase ,						1 ,						1   )   )






def _lowerCAmelCase	(  __lowerCamelCase:int = 8_0_0 ,						__lowerCamelCase:int = 6_0_0 ,						__lowerCamelCase:float = -0.6 ,						__lowerCamelCase:float = 0 ,						__lowerCamelCase:float = 3.2 ,						__lowerCamelCase:int = 5_0 ,						__lowerCamelCase:bool = True ,						):

						'''simple docstring'''






						__magic_name__            =     Image.new("RGB" ,						(image_width, image_height)   )
						__magic_name__            =     img.load()

						# loop through the image-coordinates
						for image_x in range(__lowerCamelCase   ):
												for image_y in range(__lowerCamelCase   ):
																		# determine the figure-coordinates based on the image-coordinates
																		__magic_name__            =     figure_width / image_width * image_height
																		__magic_name__            =     figure_center_x + (image_x / image_width - 0.5) * figure_width
																		__magic_name__            =     figure_center_y + (image_y / image_height - 0.5) * figure_height

																		__magic_name__            =     get_distance(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase   )

																		# color the corresponding pixel based on the selected coloring-function
																		if use_distance_color_coding:
																								__magic_name__            =     get_color_coded_rgb(__lowerCamelCase   )
																		else:
																								__magic_name__            =     get_black_and_white_rgb(__lowerCamelCase   )

						return img


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			# colored version, full figure
			lowercase    			=       get_image()

			# uncomment for colored version, different section, zoomed in
			# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
			# figure_width = 0.8)

			# uncomment for black and white version, full figure
			# img = get_image(use_distance_color_coding = False)

			# uncomment to save the image
			# img.save("mandelbrot.png")

			img.show()



