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







def 		_snake_case(    SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							)					->       List[str]:
       return [
           int(1_000 * (box[0] / width)							),
           int(1_000 * (box[1] / height)							),
           int(1_000 * (box[2] / width)							),
           int(1_000 * (box[3] / height)							),
       ]







def 		_snake_case(    SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__ = None							)					->       Tuple:
       lowercase				:	Optional[int]			      = tesseract_config if tesseract_config is not None else ''

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

       # filter empty words and corresponding coordinates
       lowercase				:	Optional[Any]			      = [idx for idx, word in enumerate(UpperCAmelCase_							) if not word.strip()]
       lowercase				:	Any			      = [word for idx, word in enumerate(UpperCAmelCase_							) if idx not in irrelevant_indices]
       lowercase				:	Any			      = [coord for idx, coord in enumerate(UpperCAmelCase_							) if idx not in irrelevant_indices]
       lowercase				:	Union[str, Any]			      = [coord for idx, coord in enumerate(UpperCAmelCase_							) if idx not in irrelevant_indices]
       lowercase				:	Union[str, Any]			      = [coord for idx, coord in enumerate(UpperCAmelCase_							) if idx not in irrelevant_indices]
       lowercase				:	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
       lowercase				:	Tuple			      = []
       for x, y, w, h in zip(UpperCAmelCase_   ,      UpperCAmelCase_   ,      UpperCAmelCase_   ,      UpperCAmelCase_							):
              lowercase				:	List[str]			      = [x, y, x + w, y + h]
              actual_boxes.append(UpperCAmelCase_							)

       # finally, normalize the bounding boxes
       lowercase				:	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 __snake_case						( lowerCAmelCase	):
    _a						:		Optional[int]=							["pixel_values"]
    def __init__(    self						,snake_case = True						,snake_case = None						,snake_case = PILImageResampling.BILINEAR						,snake_case = True						,snake_case = None						,snake_case = ""						,**snake_case						,):



           '''simple docstring'''







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

           lowercase				:	Optional[int]			      = do_resize
           lowercase				:	str			      = size
           lowercase				:	Any			      = resample
           lowercase				:	List[str]			      = apply_ocr
           lowercase				:	int			      = ocr_lang
           lowercase				:	Union[str, Any]			      = tesseract_config
    def  _SCREAMING_SNAKE_CASE			(    self						,snake_case						,snake_case						,snake_case = PILImageResampling.BILINEAR						,snake_case = None						,**snake_case						,):



           '''simple docstring'''







           lowercase				:	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()}" )
           lowercase				:	List[str]			      = (size['height'], size['width'])
           return resize(lowerCamelCase__						,size=lowerCamelCase__						,resample=lowerCamelCase__						,data_format=lowerCamelCase__						,**lowerCamelCase__ )
    def  _SCREAMING_SNAKE_CASE			(    self						,snake_case						,snake_case = None						,snake_case = None						,snake_case = None						,snake_case = None						,snake_case = None						,snake_case = None						,snake_case = None						,snake_case = ChannelDimension.FIRST						,**snake_case						,):



           '''simple docstring'''







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

           lowercase				:	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.
           lowercase				:	Union[str, Any]			      = [to_numpy_array(lowerCamelCase__ ) for image in images]

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

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

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

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

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





'''simple docstring'''
from math import ceil






def A__       (	UpperCAmelCase_ = 1_0_0_1				):
						_UpperCamelCase   :						int												=  1

						for i in range(1    ,    int(ceil(n / 2.0				)				)				):
												_UpperCamelCase   :						Dict												=  2 * i + 1
												_UpperCamelCase   :						Tuple												=  2 * i
												_UpperCamelCase   :						Any												=  total + 4 * odd**2 - 6 * even

						return total


if __name__ == "__main__":
					import sys

					if len(sys.argv) == 1:
										print(solution())
					else:
										try:
															snake_case_					:      Tuple    			=    int(sys.argv[1])
															print(solution(n))
										except ValueError:
															print('Invalid entry - please enter a number')


