



"""simple docstring"""







def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							int:

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

														A         =					update_area_of_max_square(lowerCamelCase__				,      col + 1			)
														A         =					update_area_of_max_square(row + 1				,      col + 1			)
														A         =					update_area_of_max_square(row + 1				,      lowerCamelCase__			)

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

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

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							int:

							def update_area_of_max_square_using_dp_array(
							    lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) -> 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(lowerCamelCase__				,      col + 1				,      lowerCamelCase__			)
														A         =					update_area_of_max_square_using_dp_array(row + 1				,      col + 1				,      lowerCamelCase__			)
														A         =					update_area_of_max_square_using_dp_array(row + 1				,      lowerCamelCase__				,      lowerCamelCase__			)

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

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

							return largest_square_area[0]

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							int:
							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(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			)
																												A         =					max(dp_array[row][col]				,      lowerCamelCase__			)
																					else:
																												A         =					0

							return largest_square_area

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							int:
							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(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			)
																												A         =					max(current_row[col]				,      lowerCamelCase__			)
																					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]]))








"""simple docstring"""







# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A    =			{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'MraForMaskedLM',
							    'MraForMultipleChoice',
							    'MraForQuestionAnswering',
							    'MraForSequenceClassification',
							    'MraForTokenClassification',
							    'MraLayer',
							    'MraModel',
							    'MraPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mra import (
														    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MraForMaskedLM,
														    MraForMultipleChoice,
														    MraForQuestionAnswering,
														    MraForSequenceClassification,
														    MraForTokenClassification,
														    MraLayer,
														    MraModel,
														    MraPreTrainedModel,
														)
else:
							import sys

							A    =			_LazyModule(__name__, globals()['__file__'], _import_structure)



