


'''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_ ( __UpperCamelCase					:       int							,    __UpperCamelCase					:       Optional[int]							,    __UpperCamelCase					:       str		)					->	List[str]:







     """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_ ( __UpperCamelCase					:       np.ndarray							,    __UpperCamelCase					:       Optional[str]							,    __UpperCamelCase					:       Optional[str]		)					->	Tuple:







     """simple docstring"""
     _A				=							to_pil_image(__UpperCamelCase		)
     _A		,				_A				=							pil_image.size
     _A				=							pytesseract.image_to_data(__UpperCamelCase							,    lang=__UpperCamelCase							,    output_type='dict'							,    config=__UpperCamelCase		)
     _A		,				_A		,				_A		,				_A		,				_A				=							data['text'], data['left'], data['top'], data['width'], data['height']

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

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

     # finally, normalize the bounding boxes
     _A				=							[]
     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        lowerCAmelCase_  (     UpperCAmelCase	):
       __UpperCAmelCase					=['pixel_values']






       def __init__( self			,     _UpperCamelCase = True			,     _UpperCamelCase = None			,     _UpperCamelCase = PILImageResampling.BILINEAR			,     _UpperCamelCase = True			,     _UpperCamelCase = 1 / 255			,     _UpperCamelCase = True			,     _UpperCamelCase = None			,     _UpperCamelCase = None			,     _UpperCamelCase = True			,     _UpperCamelCase = None			,     _UpperCamelCase = ""			,     **_UpperCamelCase			,     )->      None:
            super().__init__(**_UpperCamelCase   )
            _A				=							size if size is not None else {'height': 224, 'width': 224}
            _A				=							get_size_dict(_UpperCamelCase   )

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






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase = PILImageResampling.BILINEAR			,     _UpperCamelCase = None			,     **_UpperCamelCase			,     )->      np.ndarray:
            _A				=							get_size_dict(_UpperCamelCase   )
            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()}'   )
            _A				=							(size['height'], size['width'])
            return resize(_UpperCamelCase			,     size=_UpperCamelCase			,     resample=_UpperCamelCase			,     data_format=_UpperCamelCase			,     **_UpperCamelCase   )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase = None			,     **_UpperCamelCase			,     )->      np.ndarray:
            return rescale(_UpperCamelCase			,     scale=_UpperCamelCase			,     data_format=_UpperCamelCase			,     **_UpperCamelCase   )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase = None			,     **_UpperCamelCase			,     )->      np.ndarray:
            return normalize(_UpperCamelCase			,     mean=_UpperCamelCase			,     std=_UpperCamelCase			,     data_format=_UpperCamelCase			,     **_UpperCamelCase   )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase = None			,     _UpperCamelCase = None			,     _UpperCamelCase=None			,     _UpperCamelCase = None			,     _UpperCamelCase = None			,     _UpperCamelCase = None			,     _UpperCamelCase = None			,     _UpperCamelCase = None			,     _UpperCamelCase = None			,     _UpperCamelCase = None			,     _UpperCamelCase = None			,     _UpperCamelCase = None			,     _UpperCamelCase = ChannelDimension.FIRST			,     **_UpperCamelCase			,     )->      PIL.Image.Image:
            _A				=							do_resize if do_resize is not None else self.do_resize
            _A				=							size if size is not None else self.size
            _A				=							get_size_dict(_UpperCamelCase   )
            _A				=							resample if resample is not None else self.resample
            _A				=							do_rescale if do_rescale is not None else self.do_rescale
            _A				=							rescale_factor if rescale_factor is not None else self.rescale_factor
            _A				=							do_normalize if do_normalize is not None else self.do_normalize
            _A				=							image_mean if image_mean is not None else self.image_mean
            _A				=							image_std if image_std is not None else self.image_std
            _A				=							apply_ocr if apply_ocr is not None else self.apply_ocr
            _A				=							ocr_lang if ocr_lang is not None else self.ocr_lang
            _A				=							tesseract_config if tesseract_config is not None else self.tesseract_config

            _A				=							make_list_of_images(_UpperCamelCase   )

            if not valid_images(_UpperCamelCase   ):
                 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.
            _A				=							[to_numpy_array(_UpperCamelCase   ) for image in images]

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

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

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

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

            _A				=							[to_channel_dimension_format(_UpperCamelCase			,     _UpperCamelCase   ) for image in images]

            _A				=							BatchFeature(data={'pixel_values': images}			,     tensor_type=_UpperCamelCase   )

            if apply_ocr:
                 _A				=							words_batch
                 _A				=							boxes_batch
            return data









'''simple docstring'''
class        lowerCAmelCase_  :






       def __init__( self			,     _UpperCamelCase   )->      None:
            _A				=							size
            _A				=							[0] * size
            _A				=							[0] * size






       @staticmethod
       def   UpperCamelCase						( _UpperCamelCase   )->      int:
            return index | (index + 1)






       @staticmethod
       def   UpperCamelCase						( _UpperCamelCase   )->      int:
            return (index & (index + 1)) - 1






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase   )->      None:
            _A				=							value
            while index < self.size:
                 _A				=							self.get_prev(_UpperCamelCase   ) + 1
                 if current_left_border == index:
                      _A				=							value
                 else:
                      _A				=							max(_UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )
                 _A				=							self.get_next(_UpperCamelCase   )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase   )->      int:
            right -= 1  # Because of right is exclusive
            _A				=							0
            while left <= right:
                 _A				=							self.get_prev(_UpperCamelCase   )
                 if left <= current_left:
                      _A				=							max(_UpperCamelCase			,     self.tree[right]   )
                      _A				=							current_left
                 else:
                      _A				=							max(_UpperCamelCase			,     self.arr[right]   )
                      right -= 1
            return result


if __name__ == "__main__":
    import doctest

    doctest.testmod()





