

def 	__a     (      A__			:  int			,  A__			:  int			,  A__			:  list[list[int]]       ):

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

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

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

    SCREAMING_SNAKE_CASE										=   [0]
    update_area_of_max_square(0			,  0       )
    return largest_square_area[0]





def 	__a     (      A__			:  int			,  A__			:  int			,  A__			:  list[list[int]]       ):

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

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

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

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

    return largest_square_area[0]





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

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

    return largest_square_area





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

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


import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
    import torch





@require_torch
@require_optimum
@slow
class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE										=   "hf-internal-testing/tiny-random-t5"
       SCREAMING_SNAKE_CASE										=   AutoTokenizer.from_pretrained(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   tokenizer("This is me"     , return_tensors="pt"			)

       SCREAMING_SNAKE_CASE										=   model.to_bettertransformer()

       self.assertTrue(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

       SCREAMING_SNAKE_CASE										=   model.generate(**__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   model.reverse_bettertransformer()

       self.assertFalse(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

       with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_pretrained(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

           self.assertFalse(
               any("BetterTransformer" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

           SCREAMING_SNAKE_CASE										=   model_reloaded.generate(**__lowerCamelCase			)
           self.assertTrue(torch.allclose(__lowerCamelCase     , __lowerCamelCase			)			)





   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE										=   "hf-internal-testing/tiny-random-t5"
       SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   model.to_bettertransformer()

       with tempfile.TemporaryDirectory() as tmpdirname:
           with self.assertRaises(__lowerCamelCase			):
               model.save_pretrained(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.reverse_bettertransformer()
           model.save_pretrained(__lowerCamelCase			)