from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    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

SCREAMING_SNAKE_CASE__		:       Dict       						=       logging.get_logger(__name__)







def      _a					(   lowercase__	:      Optional[Any]  ,	lowercase__	:      List[str]  ,	lowercase__	:      List[Any]			):







     '''simple docstring'''


     return [
         int(10_00 * (box[0] / width)			),
         int(10_00 * (box[1] / height)			),
         int(10_00 * (box[2] / width)			),
         int(10_00 * (box[3] / height)			),
     ]







def      _a					(   lowercase__	:      np.ndarray  ,	lowercase__	:      Optional[str]  ,	lowercase__	:      Optional[str]			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Any      =		to_pil_image(lowercase__			)
     SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		pil_image.size
     SCREAMING_SNAKE_CASE__					:					List[Any]      =		pytesseract.image_to_data(lowercase__  ,	lang=lowercase__  ,	output_type='dict'  ,	config=lowercase__			)
     SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					List[str]      =		data['text'], data['left'], data['top'], data['width'], data['height']

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

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

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

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

     return words, normalized_boxes







class    snake_case       (   UpperCamelCase_    ):
  lowercase_								=  ['pixel_values']




  def __init__(   self  :						List[Any]  ,			a_  :						bool = True  ,			a_  :						Dict[str, int] = None  ,			a_  :						PILImageResampling = PILImageResampling.BILINEAR  ,			a_  :						bool = True  ,			a_  :						float = 1 / 255  ,			a_  :						bool = True  ,			a_  :						Union[float, Iterable[float]] = None  ,			a_  :						Union[float, Iterable[float]] = None  ,			a_  :						bool = True  ,			a_  :						Optional[str] = None  ,			a_  :						Optional[str] = ""  ,			**a_  :						List[Any]  ,			)->							None:




       """simple docstring"""
       super().__init__(**a_					)
       SCREAMING_SNAKE_CASE__					:					Any      =		size if size is not None else {'height': 224, 'width': 224}
       SCREAMING_SNAKE_CASE__					:					Any      =		get_size_dict(a_					)

       SCREAMING_SNAKE_CASE__					:					List[str]      =		do_resize
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		size
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		resample
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		do_rescale
       SCREAMING_SNAKE_CASE__					:					int      =		rescale_value
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		do_normalize
       SCREAMING_SNAKE_CASE__					:					List[str]      =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       SCREAMING_SNAKE_CASE__					:					Any      =		image_std if image_std is not None else IMAGENET_STANDARD_STD
       SCREAMING_SNAKE_CASE__					:					List[str]      =		apply_ocr
       SCREAMING_SNAKE_CASE__					:					Any      =		ocr_lang
       SCREAMING_SNAKE_CASE__					:					int      =		tesseract_config




  def 						__lowercase(   self  :						Optional[Any]  ,			a_  :						np.ndarray  ,			a_  :						Dict[str, int]  ,			a_  :						PILImageResampling = PILImageResampling.BILINEAR  ,			a_  :						Optional[Union[str, ChannelDimension]] = None  ,			**a_  :						Tuple  ,			)->							np.ndarray:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		get_size_dict(a_					)
       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()}'''					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		(size['height'], size['width'])
       return resize(a_  ,			size=a_  ,			resample=a_  ,			data_format=a_  ,			**a_					)




  def 						__lowercase(   self  :						int  ,			a_  :						np.ndarray  ,			a_  :						Union[int, float]  ,			a_  :						Optional[Union[str, ChannelDimension]] = None  ,			**a_  :						List[str]  ,			)->							np.ndarray:




       """simple docstring"""
       return rescale(a_  ,			scale=a_  ,			data_format=a_  ,			**a_					)




  def 						__lowercase(   self  :						Optional[Any]  ,			a_  :						np.ndarray  ,			a_  :						Union[float, Iterable[float]]  ,			a_  :						Union[float, Iterable[float]]  ,			a_  :						Optional[Union[str, ChannelDimension]] = None  ,			**a_  :						List[Any]  ,			)->							np.ndarray:




       """simple docstring"""
       return normalize(a_  ,			mean=a_  ,			std=a_  ,			data_format=a_  ,			**a_					)




  def 						__lowercase(   self  :						Tuple  ,			a_  :						ImageInput  ,			a_  :						bool = None  ,			a_  :						Dict[str, int] = None  ,			a_  :						Union[str, Any]=None  ,			a_  :						bool = None  ,			a_  :						float = None  ,			a_  :						bool = None  ,			a_  :						Union[float, Iterable[float]] = None  ,			a_  :						Union[float, Iterable[float]] = None  ,			a_  :						bool = None  ,			a_  :						Optional[str] = None  ,			a_  :						Optional[str] = None  ,			a_  :						Optional[Union[str, TensorType]] = None  ,			a_  :						ChannelDimension = ChannelDimension.FIRST  ,			**a_  :						str  ,			)->							PIL.Image.Image:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					str      =		do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE__					:					List[str]      =		size if size is not None else self.size
       SCREAMING_SNAKE_CASE__					:					int      =		get_size_dict(a_					)
       SCREAMING_SNAKE_CASE__					:					Any      =		resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE__					:					Any      =		do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE__					:					Any      =		rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE__					:					int      =		image_mean if image_mean is not None else self.image_mean
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		image_std if image_std is not None else self.image_std
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		apply_ocr if apply_ocr is not None else self.apply_ocr
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		ocr_lang if ocr_lang is not None else self.ocr_lang
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		tesseract_config if tesseract_config is not None else self.tesseract_config

       SCREAMING_SNAKE_CASE__					:					Tuple      =		make_list_of_images(a_					)

       if not valid_images(a_					):
            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.'					)

       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('If do_normalize is True, image_mean and image_std must be specified.'					)

       # All transformations expect numpy arrays.
       SCREAMING_SNAKE_CASE__					:					Any      =		[to_numpy_array(a_					) for image in images]

       # Tesseract OCR to get words + normalized bounding boxes
       if apply_ocr:
            requires_backends(self  ,			'pytesseract'					)
            SCREAMING_SNAKE_CASE__					:					str      =		[]
            SCREAMING_SNAKE_CASE__					:					List[str]      =		[]
            for image in images:
                 SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Dict      =		apply_tesseract(a_  ,			a_  ,			a_					)
                 words_batch.append(a_					)
                 boxes_batch.append(a_					)

       if do_resize:
            SCREAMING_SNAKE_CASE__					:					List[str]      =		[self.resize(image=a_  ,			size=a_  ,			resample=a_					) for image in images]

       if do_rescale:
            SCREAMING_SNAKE_CASE__					:					Any      =		[self.rescale(image=a_  ,			scale=a_					) for image in images]

       if do_normalize:
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		[self.normalize(image=a_  ,			mean=a_  ,			std=a_					) for image in images]

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		[to_channel_dimension_format(a_  ,			a_					) for image in images]

       SCREAMING_SNAKE_CASE__					:					List[str]      =		BatchFeature(data={'pixel_values': images}  ,			tensor_type=a_					)

       if apply_ocr:
            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		words_batch
            SCREAMING_SNAKE_CASE__					:					Optional[int]      =		boxes_batch
       return data











import colorsys

from PIL import Image  # type: ignore






def 	UpperCamelCase	(							snake_case__  : float						,snake_case__  : float						,snake_case__  : int	):




     '''simple docstring'''






     __snake_case      :List[Any] 							=    x
     __snake_case      :str 							=    y
     for step in range(snake_case__	):  # noqa: B007
          __snake_case      :Tuple 							=    a * a - b * b + x
          __snake_case      :Dict 							=    2 * a * b + y
          __snake_case      :str 							=    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 	UpperCamelCase	(							snake_case__  : float	):




     '''simple docstring'''






     if distance == 1:
          return (0, 0, 0)
     else:
          return (255, 255, 255)






def 	UpperCamelCase	(							snake_case__  : float	):




     '''simple docstring'''






     if distance == 1:
          return (0, 0, 0)
     else:
          return tuple(round(i * 255	) for i in colorsys.hsv_to_rgb(snake_case__						,1						,1	)	)






def 	UpperCamelCase	(							snake_case__  : int = 800						,snake_case__  : int = 600						,snake_case__  : float = -0.6						,snake_case__  : float = 0						,snake_case__  : float = 3.2						,snake_case__  : int = 50						,snake_case__  : bool = True						,):




     '''simple docstring'''






     __snake_case      :Tuple 							=    Image.new("""RGB"""						,(image_width, image_height)	)
     __snake_case      :Dict 							=    img.load()

     # loop through the image-coordinates
     for image_x in range(snake_case__	):
          for image_y in range(snake_case__	):
               # determine the figure-coordinates based on the image-coordinates
               __snake_case      :Dict 							=    figure_width / image_width * image_height
               __snake_case      :Any 							=    figure_center_x + (image_x / image_width - 0.5) * figure_width
               __snake_case      :List[str] 							=    figure_center_y + (image_y / image_height - 0.5) * figure_height

               __snake_case      :Tuple 							=    get_distance(snake_case__						,snake_case__						,snake_case__	)

               # color the corresponding pixel based on the selected coloring-function
               if use_distance_color_coding:
                    __snake_case      :Optional[int] 							=    get_color_coded_rgb(snake_case__	)
               else:
                    __snake_case      :Optional[int] 							=    get_black_and_white_rgb(snake_case__	)

     return img


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # colored version, full figure
     lowerCamelCase__       		=  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()


