






'''simple docstring'''
from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCamelCase__ =			logging.get_logger(__name__)


class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   Dict		       =    ['pixel_values']




    def __init__(			self  :       Tuple  ,     a  :       bool = True  ,     a  :       Dict[str, int] = None  ,     a  :       PILImageResampling = PILImageResampling.BICUBIC  ,     a  :       bool = True  ,     a  :       Dict[str, int] = None  ,     a  :       bool = True  ,     a  :       Union[int, float] = 1 / 2_5_5  ,     a  :       bool = True  ,     a  :       Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN  ,     a  :       Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD  ,     **a  :       Dict  ,     ):






       '''simple docstring'''






       super().__init__(**a			)
       lowercase_	:    Any           =     size if size is not None else {"shortest_edge": 2_2_4}
       lowercase_	:    Optional[Any]           =     get_size_dict(a  ,     default_to_square=a			)
       lowercase_	:    Union[str, Any]           =     crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
       lowercase_	:    List[str]           =     get_size_dict(a  ,     param_name="crop_size"			)

       lowercase_	:    Union[str, Any]           =     do_resize
       lowercase_	:    Union[str, Any]           =     size
       lowercase_	:    Dict           =     resample
       lowercase_	:    Optional[int]           =     do_center_crop
       lowercase_	:    int           =     crop_size
       lowercase_	:    Any           =     do_rescale
       lowercase_	:    Union[str, Any]           =     rescale_factor
       lowercase_	:    Tuple           =     do_normalize
       lowercase_	:    Any           =     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
       lowercase_	:    Tuple           =     image_std if image_std is not None else IMAGENET_DEFAULT_STD




    def 				lowerCAmelCase__				(			self  :       Optional[int]  ,     a  :       np.ndarray  ,     a  :       Dict[str, int]  ,     a  :       PILImageResampling = PILImageResampling.BICUBIC  ,     a  :       Optional[Union[str, ChannelDimension]] = None  ,     **a  :       List[str]  ,     ):






       '''simple docstring'''






       lowercase_	:    int           =     get_size_dict(a  ,     default_to_square=a			)
       # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
       if "shortest_edge" in size:
          lowercase_	:    Dict           =     int((2_5_6 / 2_2_4) * size["shortest_edge"]			)
          lowercase_	:    Tuple           =     get_resize_output_image_size(a  ,     size=a  ,     default_to_square=a			)
          lowercase_	:    Any           =     {"height": output_size[0], "width": output_size[1]}
       if "height" not in size_dict or "width" not in size_dict:
          raise ValueError(
              f"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""			)
       return resize(
           a  ,     size=(size_dict["height"], size_dict["width"])  ,     resample=a  ,     data_format=a  ,     **a			)




    def 				lowerCAmelCase__				(			self  :       Tuple  ,     a  :       np.ndarray  ,     a  :       Dict[str, int]  ,     a  :       Optional[Union[str, ChannelDimension]] = None  ,     **a  :       Optional[Any]  ,     ):






       '''simple docstring'''






       lowercase_	:    Optional[Any]           =     get_size_dict(a			)
       if "height" not in size or "width" not in size:
          raise ValueError(f"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""			)
       return center_crop(a  ,     size=(size["height"], size["width"])  ,     data_format=a  ,     **a			)




    def 				lowerCAmelCase__				(			self  :       List[str]  ,     a  :       np.ndarray  ,     a  :       Union[int, float]  ,     a  :       Optional[Union[str, ChannelDimension]] = None  ,     **a  :       Union[str, Any]  ,     ):






       '''simple docstring'''






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




    def 				lowerCAmelCase__				(			self  :       Optional[int]  ,     a  :       np.ndarray  ,     a  :       Union[float, List[float]]  ,     a  :       Union[float, List[float]]  ,     a  :       Optional[Union[str, ChannelDimension]] = None  ,     **a  :       Any  ,     ):






       '''simple docstring'''






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





    def 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       ImageInput  ,     a  :       Optional[bool] = None  ,     a  :       Optional[Dict[str, int]] = None  ,     a  :       PILImageResampling = None  ,     a  :       Optional[bool] = None  ,     a  :       Optional[Dict[str, int]] = None  ,     a  :       Optional[bool] = None  ,     a  :       Optional[float] = None  ,     a  :       Optional[bool] = None  ,     a  :       Optional[Union[float, Iterable[float]]] = None  ,     a  :       Optional[Union[float, Iterable[float]]] = None  ,     a  :       Optional[TensorType] = None  ,     a  :       ChannelDimension = ChannelDimension.FIRST  ,     **a  :       Dict  ,     ):






       '''simple docstring'''






       lowercase_	:    Any           =     do_resize if do_resize is not None else self.do_resize
       lowercase_	:    Dict           =     resample if resample is not None else self.resample
       lowercase_	:    Optional[int]           =     do_center_crop if do_center_crop is not None else self.do_center_crop
       lowercase_	:    List[Any]           =     do_rescale if do_rescale is not None else self.do_rescale
       lowercase_	:    str           =     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[str]           =     image_mean if image_mean is not None else self.image_mean
       lowercase_	:    List[Any]           =     image_std if image_std is not None else self.image_std

       lowercase_	:    Union[str, Any]           =     size if size is not None else self.size
       lowercase_	:    Dict           =     get_size_dict(a  ,     default_to_square=a			)
       lowercase_	:    Any           =     crop_size if crop_size is not None else self.crop_size
       lowercase_	:    Any           =     get_size_dict(a  ,     param_name="crop_size"			)

       lowercase_	:    Union[str, Any]           =     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_center_crop and crop_size is None:
          raise ValueError("Crop size must be specified if do_center_crop 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("Image mean and std must be specified if do_normalize is True."			)

       # All transformations expect numpy arrays.
       lowercase_	:    Optional[int]           =     [to_numpy_array(a			) for image in images]

       if do_resize:
          lowercase_	:    Any           =     [self.resize(a  ,     a  ,     a			) for image in images]

       if do_center_crop:
          lowercase_	:    Any           =     [self.center_crop(a  ,     a			) for image in images]

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

       if do_normalize:
          lowercase_	:    Dict           =     [self.normalize(a  ,     a  ,     a			) for image in images]

       lowercase_	:    Tuple           =     [to_channel_dimension_format(a  ,     a			) for image in images]

       lowercase_	:    int           =     {"pixel_values": images}
       return BatchFeature(data=a  ,     tensor_type=a			)







'''simple docstring'''
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   def update_area_of_max_square(_UpperCamelCase						,		_UpperCamelCase							) -> int:
      # BASE CASE
      if row >= rows or col >= cols:
         return 0

      lowercase_	:    List[str]           =     update_area_of_max_square(_UpperCamelCase						,		col + 1							)
      lowercase_	:    List[Any]           =     update_area_of_max_square(row + 1						,		col + 1							)
      lowercase_	:    Tuple           =     update_area_of_max_square(row + 1						,		_UpperCamelCase							)

      if mat[row][col]:
         lowercase_	:    Optional[int]           =     1 + min([right, diagonal, down]							)
         lowercase_	:    Any           =     max(largest_square_area[0]						,		_UpperCamelCase							)
         return sub_problem_sol
      else:
         return 0

   lowercase_	:    int           =     [0]
   update_area_of_max_square(0						,		0							)
   return largest_square_area[0]




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   def update_area_of_max_square_using_dp_array(
       _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							) -> int:
      if row >= rows or col >= cols:
         return 0
      if dp_array[row][col] != -1:
         return dp_array[row][col]

      lowercase_	:    Dict           =     update_area_of_max_square_using_dp_array(_UpperCamelCase						,		col + 1						,		_UpperCamelCase							)
      lowercase_	:    str           =     update_area_of_max_square_using_dp_array(row + 1						,		col + 1						,		_UpperCamelCase							)
      lowercase_	:    Optional[Any]           =     update_area_of_max_square_using_dp_array(row + 1						,		_UpperCamelCase						,		_UpperCamelCase							)

      if mat[row][col]:
         lowercase_	:    Tuple           =     1 + min([right, diagonal, down]							)
         lowercase_	:    int           =     max(largest_square_area[0]						,		_UpperCamelCase							)
         lowercase_	:    Dict           =     sub_problem_sol
         return sub_problem_sol
      else:
         return 0

   lowercase_	:    Any           =     [0]
   lowercase_	:    Optional[int]           =     [[-1] * cols for _ in range(_UpperCamelCase							)]
   update_area_of_max_square_using_dp_array(0						,		0						,		_UpperCamelCase							)

   return largest_square_area[0]




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    str           =     [[0] * (cols + 1) for _ in range(rows + 1							)]
   lowercase_	:    List[Any]           =     0
   for row in range(rows - 1						,		-1						,		-1							):
      for col in range(cols - 1						,		-1						,		-1							):
         lowercase_	:    Tuple           =     dp_array[row][col + 1]
         lowercase_	:    List[str]           =     dp_array[row + 1][col + 1]
         lowercase_	:    List[Any]           =     dp_array[row + 1][col]

         if mat[row][col] == 1:
            lowercase_	:    Any           =     1 + min(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)
            lowercase_	:    Any           =     max(dp_array[row][col]						,		_UpperCamelCase							)
         else:
            lowercase_	:    int           =     0

   return largest_square_area




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Optional[int]           =     [0] * (cols + 1)
   lowercase_	:    Union[str, Any]           =     [0] * (cols + 1)
   lowercase_	:    int           =     0
   for row in range(rows - 1						,		-1						,		-1							):
      for col in range(cols - 1						,		-1						,		-1							):
         lowercase_	:    Dict           =     current_row[col + 1]
         lowercase_	:    List[Any]           =     next_row[col + 1]
         lowercase_	:    Tuple           =     next_row[col]

         if mat[row][col] == 1:
            lowercase_	:    Dict           =     1 + min(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)
            lowercase_	:    int           =     max(current_row[col]						,		_UpperCamelCase							)
         else:
            lowercase_	:    Tuple           =     0
      lowercase_	:    Optional[Any]           =     current_row

   return largest_square_area


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))




