





def    _lowerCAmelCase						(				lowerCamelCase_			:  int		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  list[list[int]]				):

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

						__lowercase										=							update_area_of_max_square(lowerCamelCase_		,       col + 1				)
						__lowercase										=							update_area_of_max_square(row + 1		,       col + 1				)
						__lowercase										=							update_area_of_max_square(row + 1		,       lowerCamelCase_				)

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

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




def    _lowerCAmelCase						(				lowerCamelCase_			:  int		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  list[list[int]]				):

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

						__lowercase										=							update_area_of_max_square_using_dp_array(lowerCamelCase_		,       col + 1		,       lowerCamelCase_				)
						__lowercase										=							update_area_of_max_square_using_dp_array(row + 1		,       col + 1		,       lowerCamelCase_				)
						__lowercase										=							update_area_of_max_square_using_dp_array(row + 1		,       lowerCamelCase_		,       lowerCamelCase_				)

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

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

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

			return largest_square_area




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

									if mat[row][col] == 1:
												__lowercase										=							1 + min(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
												__lowercase										=							max(current_row[col]		,       lowerCamelCase_				)
									else:
												__lowercase										=							0
						__lowercase										=							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'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE					=    {'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    ['''XGLMTokenizer''']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    ['''XGLMTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    [
	    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''XGLMForCausalLM''',
	    '''XGLMModel''',
	    '''XGLMPreTrainedModel''',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    [
	    '''FlaxXGLMForCausalLM''',
	    '''FlaxXGLMModel''',
	    '''FlaxXGLMPreTrainedModel''',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    [
	    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFXGLMForCausalLM''',
	    '''TFXGLMModel''',
	    '''TFXGLMPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xglm import XGLMTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xglm_fast import XGLMTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xglm import (
		    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXGLMForCausalLM,
		    TFXGLMModel,
		    TFXGLMPreTrainedModel,
		)


else:
	import sys

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





