


"""simple docstring"""


from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
	import PIL

A     :   Union[str, Any]			    =			logging.get_logger(__name__)


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			if isinstance(_UpperCamelCase    , (list, tuple)      ) and isinstance(videos[0]    , (list, tuple)      ) and is_valid_image(videos[0][0]      ):
						return videos

			elif isinstance(_UpperCamelCase    , (list, tuple)      ) and is_valid_image(videos[0]      ):
						return [videos]

			elif is_valid_image(_UpperCamelCase      ):
						return [[videos]]

			raise ValueError(f"Could not make batched video from {videos}"      )


class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    List[str]				      =["""pixel_values"""]

							def __init__(     self    ,   __a = True    ,   __a = None    ,   __a = PILImageResampling.BILINEAR    ,   __a = True    ,   __a = None    ,   __a = True    ,   __a = 1 / 2_55    ,   __a = True    ,   __a = True    ,   __a = None    ,   __a = None    ,   **__a    ,   ):
										super().__init__(**__a	)
										__lowerCAmelCase				    =			size if size is not None else {"shortest_edge": 2_56}
										__lowerCAmelCase				    =			get_size_dict(__a    ,   default_to_square=__a	)
										__lowerCAmelCase				    =			crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
										__lowerCAmelCase				    =			get_size_dict(__a    ,   param_name="crop_size"	)

										__lowerCAmelCase				    =			do_resize
										__lowerCAmelCase				    =			size
										__lowerCAmelCase				    =			do_center_crop
										__lowerCAmelCase				    =			crop_size
										__lowerCAmelCase				    =			resample
										__lowerCAmelCase				    =			do_rescale
										__lowerCAmelCase				    =			rescale_factor
										__lowerCAmelCase				    =			offset
										__lowerCAmelCase				    =			do_normalize
										__lowerCAmelCase				    =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										__lowerCAmelCase				    =			image_std if image_std is not None else IMAGENET_STANDARD_STD

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a = PILImageResampling.BILINEAR    ,   __a = None    ,   **__a    ,   ):
										__lowerCAmelCase				    =			get_size_dict(__a    ,   default_to_square=__a	)
										if "shortest_edge" in size:
													__lowerCAmelCase				    =			get_resize_output_image_size(__a    ,   size["shortest_edge"]    ,   default_to_square=__a	)
										elif "height" in size and "width" in size:
													__lowerCAmelCase				    =			(size["height"], size["width"])
										else:
													raise ValueError(f"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"	)
										return resize(__a    ,   size=__a    ,   resample=__a    ,   data_format=__a    ,   **__a	)

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a = None    ,   **__a    ,   ):
										__lowerCAmelCase				    =			get_size_dict(__a	)
										if "height" not in size or "width" not in size:
													raise ValueError(f"Size must have 'height' and 'width' as keys. Got {size.keys()}"	)
										return center_crop(__a    ,   size=(size["height"], size["width"])    ,   data_format=__a    ,   **__a	)

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a = True    ,   __a = None    ,   **__a    ,   ):
										__lowerCAmelCase				    =			image.astype(np.floataa	)
										if offset:
													__lowerCAmelCase				    =			image - (scale / 2)
										return rescale(__a    ,   scale=__a    ,   data_format=__a    ,   **__a	)

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a    ,   __a = None    ,   **__a    ,   ):
										return normalize(__a    ,   mean=__a    ,   std=__a    ,   data_format=__a    ,   **__a	)

							def 			snake_case				(     self    ,   __a    ,   __a = None    ,   __a = None    ,   __a = None    ,   __a = None    ,   __a = None    ,   __a = None    ,   __a = None    ,   __a = None    ,   __a = None    ,   __a = None    ,   __a = None    ,   __a = ChannelDimension.FIRST    ,   ):
										if do_resize and size is None or resample is None:
													raise ValueError("Size and resample 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."	)

										if offset and not do_rescale:
													raise ValueError("For offset, do_rescale must also be set to True."	)

										# All transformations expect numpy arrays.
										__lowerCAmelCase				    =			to_numpy_array(__a	)

										if do_resize:
													__lowerCAmelCase				    =			self.resize(image=__a    ,   size=__a    ,   resample=__a	)

										if do_center_crop:
													__lowerCAmelCase				    =			self.center_crop(__a    ,   size=__a	)

										if do_rescale:
													__lowerCAmelCase				    =			self.rescale(image=__a    ,   scale=__a    ,   offset=__a	)

										if do_normalize:
													__lowerCAmelCase				    =			self.normalize(image=__a    ,   mean=__a    ,   std=__a	)

										__lowerCAmelCase				    =			to_channel_dimension_format(__a    ,   __a	)
										return image



							def 			snake_case				(     self    ,   __a    ,   __a = None    ,   __a = None    ,   __a = None    ,   __a = None    ,   __a = None    ,   __a = None    ,   __a = None    ,   __a = None    ,   __a = None    ,   __a = None    ,   __a = None    ,   __a = None    ,   __a = ChannelDimension.FIRST    ,   **__a    ,   ):
										__lowerCAmelCase				    =			do_resize if do_resize is not None else self.do_resize
										__lowerCAmelCase				    =			resample if resample is not None else self.resample
										__lowerCAmelCase				    =			do_center_crop if do_center_crop is not None else self.do_center_crop
										__lowerCAmelCase				    =			do_rescale if do_rescale is not None else self.do_rescale
										__lowerCAmelCase				    =			rescale_factor if rescale_factor is not None else self.rescale_factor
										__lowerCAmelCase				    =			offset if offset is not None else self.offset
										__lowerCAmelCase				    =			do_normalize if do_normalize is not None else self.do_normalize
										__lowerCAmelCase				    =			image_mean if image_mean is not None else self.image_mean
										__lowerCAmelCase				    =			image_std if image_std is not None else self.image_std

										__lowerCAmelCase				    =			size if size is not None else self.size
										__lowerCAmelCase				    =			get_size_dict(__a    ,   default_to_square=__a	)
										__lowerCAmelCase				    =			crop_size if crop_size is not None else self.crop_size
										__lowerCAmelCase				    =			get_size_dict(__a    ,   param_name="crop_size"	)

										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."	)

										__lowerCAmelCase				    =			make_batched(__a	)

										__lowerCAmelCase				    =			[
										    [
										        self._preprocess_image(
										            image=__a    ,   do_resize=__a    ,   size=__a    ,   resample=__a    ,   do_center_crop=__a    ,   crop_size=__a    ,   do_rescale=__a    ,   rescale_factor=__a    ,   offset=__a    ,   do_normalize=__a    ,   image_mean=__a    ,   image_std=__a    ,   data_format=__a    ,   )
										        for img in video
										    ]
										    for video in videos
										]

										__lowerCAmelCase				    =			{"pixel_values": videos}
										return BatchFeature(data=__a    ,   tensor_type=__a	)





'''simple docstring'''

def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):

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

  lowercase__				:    int  =					update_area_of_max_square(UpperCAmelCase		,     col + 1     )
  lowercase__				:    Any  =					update_area_of_max_square(row + 1		,     col + 1     )
  lowercase__				:    str  =					update_area_of_max_square(row + 1		,     UpperCAmelCase     )

  if mat[row][col]:
   lowercase__				:    List[Any]  =					1 + min([right, diagonal, down]     )
   lowercase__				:    List[Any]  =					max(largest_square_area[0]		,     UpperCAmelCase     )
   return sub_problem_sol
  else:
   return 0

 lowercase__				:    Dict  =					[0]
 update_area_of_max_square(0		,     0     )
 return largest_square_area[0]
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):

 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__				:    int  =					update_area_of_max_square_using_dp_array(UpperCAmelCase		,     col + 1		,     UpperCAmelCase     )
  lowercase__				:    Optional[int]  =					update_area_of_max_square_using_dp_array(row + 1		,     col + 1		,     UpperCAmelCase     )
  lowercase__				:    Any  =					update_area_of_max_square_using_dp_array(row + 1		,     UpperCAmelCase		,     UpperCAmelCase     )

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

 lowercase__				:    Any  =					[0]
 lowercase__				:    List[Any]  =					[[-1] * cols for _ in range(UpperCAmelCase     )]
 update_area_of_max_square_using_dp_array(0		,     0		,     UpperCAmelCase     )

 return largest_square_area[0]
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Optional[int]  =					[[0] * (cols + 1) for _ in range(rows + 1     )]
 lowercase__				:    str  =					0
 for row in range(rows - 1		,     -1		,     -1     ):
  for col in range(cols - 1		,     -1		,     -1     ):
   lowercase__				:    str  =					dp_array[row][col + 1]
   lowercase__				:    Optional[Any]  =					dp_array[row + 1][col + 1]
   lowercase__				:    str  =					dp_array[row + 1][col]

   if mat[row][col] == 1:
    lowercase__				:    Dict  =					1 + min(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )
    lowercase__				:    str  =					max(dp_array[row][col]		,     UpperCAmelCase     )
   else:
    lowercase__				:    Any  =					0

 return largest_square_area
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    List[str]  =					[0] * (cols + 1)
 lowercase__				:    str  =					[0] * (cols + 1)
 lowercase__				:    Tuple  =					0
 for row in range(rows - 1		,     -1		,     -1     ):
  for col in range(cols - 1		,     -1		,     -1     ):
   lowercase__				:    List[Any]  =					current_row[col + 1]
   lowercase__				:    Any  =					next_row[col + 1]
   lowercase__				:    Optional[Any]  =					next_row[col]

   if mat[row][col] == 1:
    lowercase__				:    List[str]  =					1 + min(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )
    lowercase__				:    List[str]  =					max(current_row[col]		,     UpperCAmelCase     )
   else:
    lowercase__				:    int  =					0
  lowercase__				:    int  =					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]]))

