


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			: Optional[int]				   =					logging.get_logger(__name__)



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Any:
    return [
        int(1000 * (box[0] / width)					),
        int(1000 * (box[1] / height)					),
        int(1000 * (box[2] / width)					),
        int(1000 * (box[3] / height)					),
    ]



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		List[Any]:
    _lowercase			:				Optional[int] 						=						to_pil_image(lowerCamelCase_					)
    _lowercase						,							_lowercase			:				List[Any] 						=						pil_image.size
    _lowercase			:				Tuple 						=						pytesseract.image_to_data(lowerCamelCase_				,					lang=lowerCamelCase_				,					output_type='dict'				,					config=lowerCamelCase_					)
    _lowercase						,							_lowercase						,							_lowercase						,							_lowercase						,							_lowercase			:				Optional[Any] 						=						data['text'], data['left'], data['top'], data['width'], data['height']

    # filter empty words and corresponding coordinates
    _lowercase			:				List[Any] 						=						[idx for idx, word in enumerate(lowerCamelCase_					) if not word.strip()]
    _lowercase			:				Any 						=						[word for idx, word in enumerate(lowerCamelCase_					) if idx not in irrelevant_indices]
    _lowercase			:				Union[str, Any] 						=						[coord for idx, coord in enumerate(lowerCamelCase_					) if idx not in irrelevant_indices]
    _lowercase			:				Dict 						=						[coord for idx, coord in enumerate(lowerCamelCase_					) if idx not in irrelevant_indices]
    _lowercase			:				List[str] 						=						[coord for idx, coord in enumerate(lowerCamelCase_					) if idx not in irrelevant_indices]
    _lowercase			:				Tuple 						=						[coord for idx, coord in enumerate(lowerCamelCase_					) if idx not in irrelevant_indices]

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

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

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

    return words, normalized_boxes






class _lowerCamelCase(		_a  ):
       lowercase_					:      Tuple       					=     ["""pixel_values"""]
       def __init__(   self, lowerCamelCase = True, lowerCamelCase = None, lowerCamelCase = PILImageResampling.BILINEAR, lowerCamelCase = True, lowerCamelCase = 1 / 2_55, lowerCamelCase = True, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = True, lowerCamelCase = None, lowerCamelCase = "", **lowerCamelCase, )			->  None:




           """simple docstring"""

           super().__init__(**lowerCamelCase)
           _lowercase			:				Union[str, Any] 						=						size if size is not None else {'height': 2_24, 'width': 2_24}
           _lowercase			:				Dict 						=						get_size_dict(lowerCamelCase)

           _lowercase			:				int 						=						do_resize
           _lowercase			:				Any 						=						size
           _lowercase			:				List[Any] 						=						resample
           _lowercase			:				Optional[Any] 						=						do_rescale
           _lowercase			:				Optional[Any] 						=						rescale_value
           _lowercase			:				Dict 						=						do_normalize
           _lowercase			:				Tuple 						=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           _lowercase			:				List[Any] 						=						image_std if image_std is not None else IMAGENET_STANDARD_STD
           _lowercase			:				List[str] 						=						apply_ocr
           _lowercase			:				Any 						=						ocr_lang
           _lowercase			:				Optional[int] 						=						tesseract_config
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase = PILImageResampling.BILINEAR, lowerCamelCase = None, **lowerCamelCase, )			->  np.ndarray:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						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			:				Any 						=						(size['height'], size['width'])
           return resize(lowerCamelCase, size=lowerCamelCase, resample=lowerCamelCase, data_format=lowerCamelCase, **lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase = None, **lowerCamelCase, )			->  np.ndarray:




           """simple docstring"""

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




           """simple docstring"""

           return normalize(lowerCamelCase, mean=lowerCamelCase, std=lowerCamelCase, data_format=lowerCamelCase, **lowerCamelCase)




       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase=None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = ChannelDimension.FIRST, **lowerCamelCase, )			->  PIL.Image.Image:




           """simple docstring"""

           _lowercase			:				int 						=						do_resize if do_resize is not None else self.do_resize
           _lowercase			:				int 						=						size if size is not None else self.size
           _lowercase			:				int 						=						get_size_dict(lowerCamelCase)
           _lowercase			:				Tuple 						=						resample if resample is not None else self.resample
           _lowercase			:				int 						=						do_rescale if do_rescale is not None else self.do_rescale
           _lowercase			:				List[Any] 						=						rescale_factor if rescale_factor is not None else self.rescale_factor
           _lowercase			:				Optional[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[int] 						=						image_std if image_std is not None else self.image_std
           _lowercase			:				int 						=						apply_ocr if apply_ocr is not None else self.apply_ocr
           _lowercase			:				Optional[int] 						=						ocr_lang if ocr_lang is not None else self.ocr_lang
           _lowercase			:				Any 						=						tesseract_config if tesseract_config is not None else self.tesseract_config

           _lowercase			:				str 						=						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.')

           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			:				List[Any] 						=						[to_numpy_array(lowerCamelCase) for image in images]

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

           if do_resize:
               _lowercase			:				Tuple 						=						[self.resize(image=lowerCamelCase, size=lowerCamelCase, resample=lowerCamelCase) for image in images]

           if do_rescale:
               _lowercase			:				str 						=						[self.rescale(image=lowerCamelCase, scale=lowerCamelCase) for image in images]

           if do_normalize:
               _lowercase			:				str 						=						[self.normalize(image=lowerCamelCase, mean=lowerCamelCase, std=lowerCamelCase) for image in images]

           _lowercase			:				List[str] 						=						[to_channel_dimension_format(lowerCamelCase, lowerCamelCase) for image in images]

           _lowercase			:				List[Any] 						=						BatchFeature(data={'pixel_values': images}, tensor_type=lowerCamelCase)

           if apply_ocr:
               _lowercase			:				List[str] 						=						words_batch
               _lowercase			:				Tuple 						=						boxes_batch
           return data







from __future__ import annotations


def A    (							__UpperCamelCase = 4       )    ->   list[list[int]]:
			A__	  =     abs(__UpperCamelCase       ) or 4
			return [[1 + x + y * row_size for x in range(__UpperCamelCase       )] for y in range(__UpperCamelCase       )]


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			return reverse_row(transpose(__UpperCamelCase       )       )
			# OR.. transpose(reverse_column(matrix))


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			return reverse_row(reverse_column(__UpperCamelCase       )       )
			# OR.. reverse_column(reverse_row(matrix))


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			return reverse_column(transpose(__UpperCamelCase       )       )
			# OR.. transpose(reverse_row(matrix))


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			A__	  =     [list(__UpperCamelCase       ) for x in zip(*__UpperCamelCase       )]
			return matrix


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			A__	  =     matrix[::-1]
			return matrix


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			A__	  =     [x[::-1] for x in matrix]
			return matrix


def A    (							__UpperCamelCase       )    ->   None:
			for i in matrix:
						print(*__UpperCamelCase       )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   make_matrix()
							print('''\norigin:\n''')
							print_matrix(matrix)
							print('''\nrotate 90 counterclockwise:\n''')
							print_matrix(rotate_aa(matrix))

							SCREAMING_SNAKE_CASE__   			=   make_matrix()
							print('''\norigin:\n''')
							print_matrix(matrix)
							print('''\nrotate 180:\n''')
							print_matrix(rotate_aaa(matrix))

							SCREAMING_SNAKE_CASE__   			=   make_matrix()
							print('''\norigin:\n''')
							print_matrix(matrix)
							print('''\nrotate 270 counterclockwise:\n''')
							print_matrix(rotate_aaa(matrix))




