






'''simple docstring'''



from __future__ import annotations

import math


def 							lowercase_				(    __A :       list					,      __A :       list			)       ->      list:



 """simple docstring"""




 if len(__A			) != 2 or len(a[0]			) != 2 or len(__A			) != 2 or len(b[0]			) != 2:
  raise Exception('''Matrices are not 2x2'''			)
 lowercase					:   Optional[Any]				  =[
     [a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
     [a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
 ]
 return new_matrix


def 							lowercase_				(    __A :       list					,      __A :       list			)       ->      Optional[Any]:



 """simple docstring"""




 return [
     [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]			)			)]
     for row in range(len(__A			)			)
 ]


def 							lowercase_				(    __A :       list					,      __A :       list			)       ->      Any:



 """simple docstring"""




 return [
     [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]			)			)]
     for row in range(len(__A			)			)
 ]


def 							lowercase_				(    __A :       list			)       ->      tuple[list, list, list, list]:



 """simple docstring"""




 if len(__A			) % 2 != 0 or len(a[0]			) % 2 != 0:
  raise Exception('''Odd matrices are not supported!'''			)

 lowercase					:   str				  =len(__A			)
 lowercase					:   Optional[Any]				  =matrix_length // 2

 lowercase					:   str				  =[[a[i][j] for j in range(__A					,      __A			)] for i in range(__A			)]
 lowercase					:   Union[str, Any]				  =[
     [a[i][j] for j in range(__A					,      __A			)] for i in range(__A					,      __A			)
 ]

 lowercase					:   str				  =[[a[i][j] for j in range(__A			)] for i in range(__A			)]
 lowercase					:   Dict				  =[[a[i][j] for j in range(__A			)] for i in range(__A					,      __A			)]

 return top_left, top_right, bot_left, bot_right


def 							lowercase_				(    __A :       list			)       ->      tuple[int, int]:



 """simple docstring"""




 return len(__A			), len(matrix[0]			)


def 							lowercase_				(    __A :       list			)       ->      None:



 """simple docstring"""




 print('''\n'''.join(str(__A			) for line in matrix			)			)


def 							lowercase_				(    __A :       list					,      __A :       list			)       ->      list:



 """simple docstring"""




 if matrix_dimensions(__A			) == (2, 2):
  return default_matrix_multiplication(__A					,      __A			)

 lowercase  ,		lowercase  ,		lowercase  ,		lowercase					:   Any				  =split_matrix(__A			)
 lowercase  ,		lowercase  ,		lowercase  ,		lowercase					:   Optional[Any]				  =split_matrix(__A			)

 lowercase					:   Any				  =actual_strassen(__A					,      matrix_subtraction(__A					,      __A			)			)
 lowercase					:   Optional[Any]				  =actual_strassen(matrix_addition(__A					,      __A			)					,      __A			)
 lowercase					:   int				  =actual_strassen(matrix_addition(__A					,      __A			)					,      __A			)
 lowercase					:   Tuple				  =actual_strassen(__A					,      matrix_subtraction(__A					,      __A			)			)
 lowercase					:   List[Any]				  =actual_strassen(matrix_addition(__A					,      __A			)					,      matrix_addition(__A					,      __A			)			)
 lowercase					:   Any				  =actual_strassen(matrix_subtraction(__A					,      __A			)					,      matrix_addition(__A					,      __A			)			)
 lowercase					:   Optional[Any]				  =actual_strassen(matrix_subtraction(__A					,      __A			)					,      matrix_addition(__A					,      __A			)			)

 lowercase					:   Optional[int]				  =matrix_addition(matrix_subtraction(matrix_addition(__A					,      __A			)					,      __A			)					,      __A			)
 lowercase					:   Union[str, Any]				  =matrix_addition(__A					,      __A			)
 lowercase					:   Dict				  =matrix_addition(__A					,      __A			)
 lowercase					:   int				  =matrix_subtraction(matrix_subtraction(matrix_addition(__A					,      __A			)					,      __A			)					,      __A			)

 # construct the new matrix from our 4 quadrants
 lowercase					:   str				  =[]
 for i in range(len(__A			)			):
  new_matrix.append(top_left[i] + top_right[i]			)
 for i in range(len(__A			)			):
  new_matrix.append(bot_left[i] + bot_right[i]			)
 return new_matrix


def 							lowercase_				(    __A :       list					,      __A :       list			)       ->      list:



 """simple docstring"""




 if matrix_dimensions(__A			)[1] != matrix_dimensions(__A			)[0]:
  lowercase					:   Dict				  =(
      '''Unable to multiply these matrices, please check the dimensions.\n'''
      F'Matrix A: {matrixa}\n'
      F'Matrix B: {matrixa}'
  )
  raise Exception(__A			)
 lowercase					:   Dict				  =matrix_dimensions(__A			)
 lowercase					:   Optional[int]				  =matrix_dimensions(__A			)

 if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
  return [matrixa, matrixa]

 lowercase					:   Optional[Any]				  =max(*__A					,      *__A			)
 lowercase					:   int				  =int(math.pow(2					,      math.ceil(math.loga(__A			)			)			)			)
 lowercase					:   List[Any]				  =matrixa
 lowercase					:   Union[str, Any]				  =matrixa

 # Adding zeros to the matrices so that the arrays dimensions are the same and also
 # power of 2
 for i in range(0					,      __A			):
  if i < dimensiona[0]:
   for _ in range(dimensiona[1]					,      __A			):
    new_matrixa[i].append(0			)
  else:
   new_matrixa.append([0] * maxim			)
  if i < dimensiona[0]:
   for _ in range(dimensiona[1]					,      __A			):
    new_matrixa[i].append(0			)
  else:
   new_matrixa.append([0] * maxim			)

 lowercase					:   Optional[int]				  =actual_strassen(__A					,      __A			)

 # Removing the additional zeros
 for i in range(0					,      __A			):
  if i < dimensiona[0]:
   for _ in range(dimensiona[1]					,      __A			):
    final_matrix[i].pop()
  else:
   final_matrix.pop()
 return final_matrix


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE						=	[
          [2, 3, 4, 5],
          [6, 4, 3, 1],
          [2, 3, 6, 7],
          [3, 1, 2, 4],
          [2, 3, 4, 5],
          [6, 4, 3, 1],
          [2, 3, 6, 7],
          [3, 1, 2, 4],
          [2, 3, 4, 5],
          [6, 2, 3, 1],
      ]
      SCREAMING_SNAKE_CASE						=	[[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
      print(strassen(matrixa, matrixa))











'''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

SCREAMING_SNAKE_CASE						=	logging.get_logger(__name__)


def 							lowercase_				(    __A :       List[Any]					,      __A :       int					,      __A :       int			)       ->      Optional[int]:



 """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 							lowercase_				(    __A :       np.ndarray					,      __A :       Optional[str]					,      __A :       Optional[str]			)       ->      Optional[Any]:



 """simple docstring"""




 lowercase					:   int				  =to_pil_image(__A			)
 lowercase  ,		lowercase					:   Tuple				  =pil_image.size
 lowercase					:   Optional[Any]				  =pytesseract.image_to_data(__A					,      lang=__A					,      output_type='''dict'''					,      config=__A			)
 lowercase  ,		lowercase  ,		lowercase  ,		lowercase  ,		lowercase					:   Optional[Any]				  =data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

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

 # finally, normalize the bounding boxes
 lowercase					:   List[str]				  =[]
 for box in actual_boxes:
  normalized_boxes.append(normalize_box(__A					,      __A					,      __A			)			)

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

 return words, normalized_boxes





class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			['''pixel_values''']






      def __init__( self			:		List[Any]     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		Dict[str, int] = None     ,      UpperCAmelCase			:		PILImageResampling = PILImageResampling.BILINEAR     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		float = 1 / 255     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		Union[float, Iterable[float]] = None     ,      UpperCAmelCase			:		Union[float, Iterable[float]] = None     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		Optional[str] = None     ,      UpperCAmelCase			:		Optional[str] = ""     ,      **UpperCAmelCase			:		Tuple     ,      )  ->  None:


       '''simple docstring'''

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

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






      def 				A__      ( self			:		Dict     ,      UpperCAmelCase			:		np.ndarray     ,      UpperCAmelCase			:		Dict[str, int]     ,      UpperCAmelCase			:		PILImageResampling = PILImageResampling.BILINEAR     ,      UpperCAmelCase			:		Optional[Union[str, ChannelDimension]] = None     ,      **UpperCAmelCase			:		Optional[Any]     ,      )  ->  np.ndarray:


       '''simple docstring'''

       lowercase					:   Tuple				  =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()}'		)
       lowercase					:   Optional[Any]				  =(size['''height'''], size['''width'''])
       return resize(UpperCAmelCase     ,      size=UpperCAmelCase     ,      resample=UpperCAmelCase     ,      data_format=UpperCAmelCase     ,      **UpperCAmelCase		)






      def 				A__      ( self			:		Union[str, Any]     ,      UpperCAmelCase			:		np.ndarray     ,      UpperCAmelCase			:		Union[int, float]     ,      UpperCAmelCase			:		Optional[Union[str, ChannelDimension]] = None     ,      **UpperCAmelCase			:		Optional[Any]     ,      )  ->  np.ndarray:


       '''simple docstring'''

       return rescale(UpperCAmelCase     ,      scale=UpperCAmelCase     ,      data_format=UpperCAmelCase     ,      **UpperCAmelCase		)






      def 				A__      ( self			:		Union[str, Any]     ,      UpperCAmelCase			:		np.ndarray     ,      UpperCAmelCase			:		Union[float, Iterable[float]]     ,      UpperCAmelCase			:		Union[float, Iterable[float]]     ,      UpperCAmelCase			:		Optional[Union[str, ChannelDimension]] = None     ,      **UpperCAmelCase			:		Optional[int]     ,      )  ->  np.ndarray:


       '''simple docstring'''

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






      def 				A__      ( self			:		Any     ,      UpperCAmelCase			:		ImageInput     ,      UpperCAmelCase			:		bool = None     ,      UpperCAmelCase			:		Dict[str, int] = None     ,      UpperCAmelCase			:		Union[str, Any]=None     ,      UpperCAmelCase			:		bool = None     ,      UpperCAmelCase			:		float = None     ,      UpperCAmelCase			:		bool = None     ,      UpperCAmelCase			:		Union[float, Iterable[float]] = None     ,      UpperCAmelCase			:		Union[float, Iterable[float]] = None     ,      UpperCAmelCase			:		bool = None     ,      UpperCAmelCase			:		Optional[str] = None     ,      UpperCAmelCase			:		Optional[str] = None     ,      UpperCAmelCase			:		Optional[Union[str, TensorType]] = None     ,      UpperCAmelCase			:		ChannelDimension = ChannelDimension.FIRST     ,      **UpperCAmelCase			:		List[str]     ,      )  ->  PIL.Image.Image:


       '''simple docstring'''

       lowercase					:   Optional[int]				  =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(UpperCAmelCase		)
       lowercase					:   List[str]				  =resample if resample is not None else self.resample
       lowercase					:   List[Any]				  =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					:   List[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					:   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					:   Dict				  =tesseract_config if tesseract_config is not None else self.tesseract_config

       lowercase					:   str				  =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.
       lowercase					:   Tuple				  =[to_numpy_array(UpperCAmelCase		) for image in images]

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

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

       if do_rescale:
        lowercase					:   Tuple				  =[self.rescale(image=UpperCAmelCase     ,      scale=UpperCAmelCase		) for image in images]

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

       lowercase					:   Optional[Any]				  =[to_channel_dimension_format(UpperCAmelCase     ,      UpperCAmelCase		) for image in images]

       lowercase					:   Dict				  =BatchFeature(data={'''pixel_values''': images}     ,      tensor_type=UpperCAmelCase		)

       if apply_ocr:
        lowercase					:   int				  =words_batch
        lowercase					:   List[str]				  =boxes_batch
       return data



