

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

snake_case_			  = logging.get_logger(__name__)

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Tuple	,     SCREAMING_SNAKE_CASE_		:    int	,     SCREAMING_SNAKE_CASE_		:    int			):



     '''simple docstring'''





     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_		:    np.ndarray	,     SCREAMING_SNAKE_CASE_		:    Optional[str]	,     SCREAMING_SNAKE_CASE_		:    Optional[str]			):



     '''simple docstring'''





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

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

     # finally, normalize the bounding boxes
     lowercase__    :   Dict      =       []
     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       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       List[str]														=     ["""pixel_values"""]





    def __init__(      self						,   a = True						,   a = None						,   a = PILImageResampling.BILINEAR						,   a = True						,   a = 1 / 255						,   a = True						,   a = None						,   a = None						,   a = True						,   a = None						,   a = ""						,   **a						,   ):
         super().__init__(**a)
         lowercase__    :   Tuple      =       size if size is not None else {'height': 224, 'width': 224}
         lowercase__    :   Optional[int]      =       get_size_dict(a)

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





    def       snake_case_				(      self						,   a						,   a						,   a = PILImageResampling.BILINEAR						,   a = None						,   **a						,   ):
         lowercase__    :   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()}""")
         lowercase__    :   Optional[Any]      =       (size['height'], size['width'])
         return resize(a						,   size=a						,   resample=a						,   data_format=a						,   **a)





    def       snake_case_				(      self						,   a						,   a						,   a = None						,   **a						,   ):
         return rescale(a						,   scale=a						,   data_format=a						,   **a)





    def       snake_case_				(      self						,   a						,   a						,   a						,   a = None						,   **a						,   ):
         return normalize(a						,   mean=a						,   std=a						,   data_format=a						,   **a)





    def       snake_case_				(      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						,   ):
         lowercase__    :   Any      =       do_resize if do_resize is not None else self.do_resize
         lowercase__    :   List[str]      =       size if size is not None else self.size
         lowercase__    :   List[Any]      =       get_size_dict(a)
         lowercase__    :   str      =       resample if resample is not None else self.resample
         lowercase__    :   Optional[Any]      =       do_rescale if do_rescale is not None else self.do_rescale
         lowercase__    :   int      =       rescale_factor if rescale_factor is not None else self.rescale_factor
         lowercase__    :   int      =       do_normalize if do_normalize is not None else self.do_normalize
         lowercase__    :   Any      =       image_mean if image_mean is not None else self.image_mean
         lowercase__    :   Optional[Any]      =       image_std if image_std is not None else self.image_std
         lowercase__    :   Union[str, Any]      =       apply_ocr if apply_ocr is not None else self.apply_ocr
         lowercase__    :   Any      =       ocr_lang if ocr_lang is not None else self.ocr_lang
         lowercase__    :   Optional[int]      =       tesseract_config if tesseract_config is not None else self.tesseract_config

         lowercase__    :   Dict      =       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.
         lowercase__    :   int      =       [to_numpy_array(a) for image in images]

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

         if do_resize:
              lowercase__    :   Optional[Any]      =       [self.resize(image=a						,   size=a						,   resample=a) for image in images]

         if do_rescale:
              lowercase__    :   int      =       [self.rescale(image=a						,   scale=a) for image in images]

         if do_normalize:
              lowercase__    :   Tuple      =       [self.normalize(image=a						,   mean=a						,   std=a) for image in images]

         lowercase__    :   Optional[int]      =       [to_channel_dimension_format(a						,   a) for image in images]

         lowercase__    :   Optional[Any]      =       BatchFeature(data={'pixel_values': images}						,   tensor_type=a)

         if apply_ocr:
              lowercase__    :   List[Any]      =       words_batch
              lowercase__    :   int      =       boxes_batch
         return data




def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





     if not all(char in '01' for char in bin_string			):
          raise ValueError('Non-binary value was passed to the function'			)
     if not bin_string:
          raise ValueError('Empty string was passed to the function'			)
     lowercase__    :   Union[str, Any]      =       ''
     while len(SCREAMING_SNAKE_CASE_			) % 3 != 0:
          lowercase__    :   List[str]      =       '0' + bin_string
     lowercase__    :   Any      =       [
         bin_string[index : index + 3]
         for index in range(len(SCREAMING_SNAKE_CASE_			)			)
         if index % 3 == 0
     ]
     for bin_group in bin_string_in_3_list:
          lowercase__    :   str      =       0
          for index, val in enumerate(SCREAMING_SNAKE_CASE_			):
               oct_val += int(2 ** (2 - index) * int(SCREAMING_SNAKE_CASE_			)			)
          oct_string += str(SCREAMING_SNAKE_CASE_			)
     return oct_string


if __name__ == "__main__":
       from doctest import testmod

       testmod()

