

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


lowercase_			=  logging.get_logger(__name__)


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							['pixel_values']


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





        '''simple docstring'''







        super().__init__(**lowercase_		)
        A__   				=       size if size is not None else {'shortest_edge': 2_2_4}
        A__   				=       get_size_dict(lowercase_,default_to_square=lowercase_		)
        A__   				=       crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
        A__   				=       get_size_dict(lowercase_,param_name='crop_size'		)

        A__   				=       do_resize
        A__   				=       size
        A__   				=       resample
        A__   				=       do_center_crop
        A__   				=       crop_size
        A__   				=       do_rescale
        A__   				=       rescale_factor
        A__   				=       do_normalize
        A__   				=       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        A__   				=       image_std if image_std is not None else IMAGENET_DEFAULT_STD


    def 						snake_case__					(				self			:	List[str],lowercase_			:	np.ndarray,lowercase_			:	Dict[str, int],lowercase_			:	PILImageResampling = PILImageResampling.BICUBIC,lowercase_			:	Optional[Union[str, ChannelDimension]] = None,**lowercase_			:	str,)->   np.ndarray:





        '''simple docstring'''







        A__   				=       get_size_dict(lowercase_,default_to_square=lowercase_		)
        # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
        if "shortest_edge" in size:
            A__   				=       int((2_5_6 / 2_2_4) * size['shortest_edge']		)
            A__   				=       get_resize_output_image_size(lowercase_,size=lowercase_,default_to_square=lowercase_		)
            A__   				=       {'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(
            lowercase_,size=(size_dict['height'], size_dict['width']),resample=lowercase_,data_format=lowercase_,**lowercase_		)


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	np.ndarray,lowercase_			:	Dict[str, int],lowercase_			:	Optional[Union[str, ChannelDimension]] = None,**lowercase_			:	List[Any],)->   np.ndarray:





        '''simple docstring'''







        A__   				=       get_size_dict(lowercase_		)
        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(lowercase_,size=(size['height'], size['width']),data_format=lowercase_,**lowercase_		)


    def 						snake_case__					(				self			:	Dict,lowercase_			:	np.ndarray,lowercase_			:	Union[int, float],lowercase_			:	Optional[Union[str, ChannelDimension]] = None,**lowercase_			:	Tuple,)->   np.ndarray:





        '''simple docstring'''







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


    def 						snake_case__					(				self			:	Tuple,lowercase_			:	np.ndarray,lowercase_			:	Union[float, List[float]],lowercase_			:	Union[float, List[float]],lowercase_			:	Optional[Union[str, ChannelDimension]] = None,**lowercase_			:	Optional[Any],)->   np.ndarray:





        '''simple docstring'''







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


    def 						snake_case__					(				self			:	int,lowercase_			:	ImageInput,lowercase_			:	Optional[bool] = None,lowercase_			:	Optional[Dict[str, int]] = None,lowercase_			:	PILImageResampling = None,lowercase_			:	Optional[bool] = None,lowercase_			:	Optional[Dict[str, int]] = None,lowercase_			:	Optional[bool] = None,lowercase_			:	Optional[float] = None,lowercase_			:	Optional[bool] = None,lowercase_			:	Optional[Union[float, Iterable[float]]] = None,lowercase_			:	Optional[Union[float, Iterable[float]]] = None,lowercase_			:	Optional[TensorType] = None,lowercase_			:	ChannelDimension = ChannelDimension.FIRST,**lowercase_			:	Optional[Any],)->   BatchFeature:





        '''simple docstring'''







        A__   				=       do_resize if do_resize is not None else self.do_resize
        A__   				=       resample if resample is not None else self.resample
        A__   				=       do_center_crop if do_center_crop is not None else self.do_center_crop
        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__   				=       size if size is not None else self.size
        A__   				=       get_size_dict(lowercase_,default_to_square=lowercase_		)
        A__   				=       crop_size if crop_size is not None else self.crop_size
        A__   				=       get_size_dict(lowercase_,param_name='crop_size'		)

        A__   				=       make_list_of_images(lowercase_		)

        if not valid_images(lowercase_		):
            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.
        A__   				=       [to_numpy_array(lowercase_		) for image in images]

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

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

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

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

        A__   				=       [to_channel_dimension_format(lowercase_,lowercase_		) for image in images]

        A__   				=       {'pixel_values': images}
        return BatchFeature(data=lowercase_,tensor_type=lowercase_		)




def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				)							->				int:





    '''simple docstring'''






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

        A__   				=       update_area_of_max_square(SCREAMING_SNAKE_CASE__   ,      col + 1				)
        A__   				=       update_area_of_max_square(row + 1   ,      col + 1				)
        A__   				=       update_area_of_max_square(row + 1   ,      SCREAMING_SNAKE_CASE__				)

        if mat[row][col]:
            A__   				=       1 + min([right, diagonal, down]				)
            A__   				=       max(largest_square_area[0]   ,      SCREAMING_SNAKE_CASE__				)
            return sub_problem_sol
        else:
            return 0

    A__   				=       [0]
    update_area_of_max_square(0   ,      0				)
    return largest_square_area[0]
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				)							->				int:





    '''simple docstring'''






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

        A__   				=       update_area_of_max_square_using_dp_array(SCREAMING_SNAKE_CASE__   ,      col + 1   ,      SCREAMING_SNAKE_CASE__				)
        A__   				=       update_area_of_max_square_using_dp_array(row + 1   ,      col + 1   ,      SCREAMING_SNAKE_CASE__				)
        A__   				=       update_area_of_max_square_using_dp_array(row + 1   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)

        if mat[row][col]:
            A__   				=       1 + min([right, diagonal, down]				)
            A__   				=       max(largest_square_area[0]   ,      SCREAMING_SNAKE_CASE__				)
            A__   				=       sub_problem_sol
            return sub_problem_sol
        else:
            return 0

    A__   				=       [0]
    A__   				=       [[-1] * cols for _ in range(SCREAMING_SNAKE_CASE__				)]
    update_area_of_max_square_using_dp_array(0   ,      0   ,      SCREAMING_SNAKE_CASE__				)

    return largest_square_area[0]
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				)							->				int:





    '''simple docstring'''






    A__   				=       [[0] * (cols + 1) for _ in range(rows + 1				)]
    A__   				=       0
    for row in range(rows - 1   ,      -1   ,      -1				):
        for col in range(cols - 1   ,      -1   ,      -1				):
            A__   				=       dp_array[row][col + 1]
            A__   				=       dp_array[row + 1][col + 1]
            A__   				=       dp_array[row + 1][col]

            if mat[row][col] == 1:
                A__   				=       1 + min(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
                A__   				=       max(dp_array[row][col]   ,      SCREAMING_SNAKE_CASE__				)
            else:
                A__   				=       0

    return largest_square_area
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				)							->				int:





    '''simple docstring'''






    A__   				=       [0] * (cols + 1)
    A__   				=       [0] * (cols + 1)
    A__   				=       0
    for row in range(rows - 1   ,      -1   ,      -1				):
        for col in range(cols - 1   ,      -1   ,      -1				):
            A__   				=       current_row[col + 1]
            A__   				=       next_row[col + 1]
            A__   				=       next_row[col]

            if mat[row][col] == 1:
                A__   				=       1 + min(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
                A__   				=       max(current_row[col]   ,      SCREAMING_SNAKE_CASE__				)
            else:
                A__   				=       0
        A__   				=       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]]))

