
"""simple docstring"""






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

lowerCAmelCase_			=							logging.get_logger(__name__)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Tuple:






       """simple docstring"""




       return [
           int(1000 * (box[0] / width)							),
           int(1000 * (box[1] / height)							),
           int(1000 * (box[2] / width)							),
           int(1000 * (box[3] / height)							),
       ]

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Union[str, Any]:






       """simple docstring"""




       _UpperCAmelCase     =     to_pil_image(SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase    ,							_UpperCAmelCase     =     pil_image.size
       _UpperCAmelCase     =     pytesseract.image_to_data(SCREAMING_SNAKE_CASE,lang=SCREAMING_SNAKE_CASE,output_type='dict',config=SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase     =     data['text'], data['left'], data['top'], data['width'], data['height']

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

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

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

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

       return words, normalized_boxes



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  ["""pixel_values"""]






       def __init__(   self    ,  a__ = True    ,  a__ = None    ,  a__ = PILImageResampling.BILINEAR    ,  a__ = True    ,  a__ = 1 / 2_55    ,  a__ = True    ,  a__ = None    ,  a__ = None    ,  a__ = True    ,  a__ = None    ,  a__ = ""    ,  **a__    ,  ):
              super().__init__(**a__     )
              _UpperCAmelCase     =     size if size is not None else {'height': 2_24, 'width': 2_24}
              _UpperCAmelCase     =     get_size_dict(a__     )

              _UpperCAmelCase     =     do_resize
              _UpperCAmelCase     =     size
              _UpperCAmelCase     =     resample
              _UpperCAmelCase     =     do_rescale
              _UpperCAmelCase     =     rescale_value
              _UpperCAmelCase     =     do_normalize
              _UpperCAmelCase     =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
              _UpperCAmelCase     =     image_std if image_std is not None else IMAGENET_STANDARD_STD
              _UpperCAmelCase     =     apply_ocr
              _UpperCAmelCase     =     ocr_lang
              _UpperCAmelCase     =     tesseract_config






       def 							__A (   self    ,  a__    ,  a__    ,  a__ = PILImageResampling.BILINEAR    ,  a__ = None    ,  **a__    ,  ):
              _UpperCAmelCase     =     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()}"""     )
              _UpperCAmelCase     =     (size['height'], size['width'])
              return resize(a__    ,  size=a__    ,  resample=a__    ,  data_format=a__    ,  **a__     )






       def 							__A (   self    ,  a__    ,  a__    ,  a__ = None    ,  **a__    ,  ):
              return rescale(a__    ,  scale=a__    ,  data_format=a__    ,  **a__     )






       def 							__A (   self    ,  a__    ,  a__    ,  a__    ,  a__ = None    ,  **a__    ,  ):
              return normalize(a__    ,  mean=a__    ,  std=a__    ,  data_format=a__    ,  **a__     )






       def 							__A (   self    ,  a__    ,  a__ = None    ,  a__ = None    ,  a__=None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = ChannelDimension.FIRST    ,  **a__    ,  ):
              _UpperCAmelCase     =     do_resize if do_resize is not None else self.do_resize
              _UpperCAmelCase     =     size if size is not None else self.size
              _UpperCAmelCase     =     get_size_dict(a__     )
              _UpperCAmelCase     =     resample if resample is not None else self.resample
              _UpperCAmelCase     =     do_rescale if do_rescale is not None else self.do_rescale
              _UpperCAmelCase     =     rescale_factor if rescale_factor is not None else self.rescale_factor
              _UpperCAmelCase     =     do_normalize if do_normalize is not None else self.do_normalize
              _UpperCAmelCase     =     image_mean if image_mean is not None else self.image_mean
              _UpperCAmelCase     =     image_std if image_std is not None else self.image_std
              _UpperCAmelCase     =     apply_ocr if apply_ocr is not None else self.apply_ocr
              _UpperCAmelCase     =     ocr_lang if ocr_lang is not None else self.ocr_lang
              _UpperCAmelCase     =     tesseract_config if tesseract_config is not None else self.tesseract_config

              _UpperCAmelCase     =     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.
              _UpperCAmelCase     =     [to_numpy_array(a__     ) for image in images]

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

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

              if do_rescale:
                     _UpperCAmelCase     =     [self.rescale(image=a__    ,  scale=a__     ) for image in images]

              if do_normalize:
                     _UpperCAmelCase     =     [self.normalize(image=a__    ,  mean=a__    ,  std=a__     ) for image in images]

              _UpperCAmelCase     =     [to_channel_dimension_format(a__    ,  a__     ) for image in images]

              _UpperCAmelCase     =     BatchFeature(data={'pixel_values': images}    ,  tensor_type=a__     )

              if apply_ocr:
                     _UpperCAmelCase     =     words_batch
                     _UpperCAmelCase     =     boxes_batch
              return data













"""simple docstring"""






import sys

lowerCAmelCase_			=							(
    '''73167176531330624919225119674426574742355349194934'''
    '''96983520312774506326239578318016984801869478851843'''
    '''85861560789112949495459501737958331952853208805511'''
    '''12540698747158523863050715693290963295227443043557'''
    '''66896648950445244523161731856403098711121722383113'''
    '''62229893423380308135336276614282806444486645238749'''
    '''30358907296290491560440772390713810515859307960866'''
    '''70172427121883998797908792274921901699720888093776'''
    '''65727333001053367881220235421809751254540594752243'''
    '''52584907711670556013604839586446706324415722155397'''
    '''53697817977846174064955149290862569321978468622482'''
    '''83972241375657056057490261407972968652414535100474'''
    '''82166370484403199890008895243450658541227588666881'''
    '''16427171479924442928230863465674813919123162824586'''
    '''17866458359124566529476545682848912883142607690042'''
    '''24219022671055626321111109370544217506941658960408'''
    '''07198403850962455444362981230987879927244284909188'''
    '''84580156166097919133875499200524063689912560717606'''
    '''05886116467109405077541002256983155200055935729725'''
    '''71636269561882670428252483600823257530420752963450'''
)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       _UpperCAmelCase     =     1
       for digit in s:
              product *= int(SCREAMING_SNAKE_CASE							)
       return product

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE = N							)		->       int:






       """simple docstring"""




       _UpperCAmelCase     =     -sys.maxsize - 1
       _UpperCAmelCase     =     n[:13]
       _UpperCAmelCase     =     13
       while cur_index < len(SCREAMING_SNAKE_CASE							) - 13:
              if int(n[cur_index]							) >= int(substr[0]							):
                     _UpperCAmelCase     =     substr[1:] + n[cur_index]
                     cur_index += 1
              else:
                     _UpperCAmelCase     =     max(SCREAMING_SNAKE_CASE,str_eval(SCREAMING_SNAKE_CASE							)							)
                     _UpperCAmelCase     =     n[cur_index : cur_index + 13]
                     cur_index += 13
       return largest_product


if __name__ == "__main__":
    print(F'''{solution() = }''')






