



"""simple docstring"""





def 	UpperCAmelCase     (		A	:						List[Any]     ,				A	:						Union[str, Any]     ,				A	:						List[Any]       ):

		'''simple docstring'''


		def update_area_of_max_square(A	:						Optional[Any]     ,				A	:						Dict       ) -> int:
				# BASE CASE
				if row >= rows or col >= cols:
						return 0

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

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

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




def 	UpperCAmelCase     (		A	:						List[str]     ,				A	:						List[Any]     ,				A	:						Optional[int]       ):

		'''simple docstring'''


		def update_area_of_max_square_using_dp_array(
		    A	:						Union[str, Any]     ,				A	:						Dict     ,				A	:						Union[str, Any]       ) -> int:
				if row >= rows or col >= cols:
						return 0
				if dp_array[row][col] != -1:
						return dp_array[row][col]

				_UpperCAmelCase							 =		update_area_of_max_square_using_dp_array(lowerCamelCase__     ,				col + 1     ,				lowerCamelCase__       )
				_UpperCAmelCase							 =		update_area_of_max_square_using_dp_array(row + 1     ,				col + 1     ,				lowerCamelCase__       )
				_UpperCAmelCase							 =		update_area_of_max_square_using_dp_array(row + 1     ,				lowerCamelCase__     ,				lowerCamelCase__       )

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

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

		return largest_square_area[0]




def 	UpperCAmelCase     (		A	:						Union[str, Any]     ,				A	:						Optional[Any]     ,				A	:						Optional[int]       ):

		'''simple docstring'''


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

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

		return largest_square_area




def 	UpperCAmelCase     (		A	:						List[Any]     ,				A	:						List[str]     ,				A	:						Dict       ):

		'''simple docstring'''


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

						if mat[row][col] == 1:
								_UpperCAmelCase							 =		1 + min(lowerCamelCase__     ,				lowerCamelCase__     ,				lowerCamelCase__       )
								_UpperCAmelCase							 =		max(current_row[col]     ,				lowerCamelCase__       )
						else:
								_UpperCAmelCase							 =		0
				_UpperCAmelCase							 =		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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase  							=					{
    '''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
    '''tokenization_roberta''': ['''RobertaTokenizer'''],
}

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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''RobertaForCausalLM''',
					    '''RobertaForMaskedLM''',
					    '''RobertaForMultipleChoice''',
					    '''RobertaForQuestionAnswering''',
					    '''RobertaForSequenceClassification''',
					    '''RobertaForTokenClassification''',
					    '''RobertaModel''',
					    '''RobertaPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFRobertaForCausalLM''',
					    '''TFRobertaForMaskedLM''',
					    '''TFRobertaForMultipleChoice''',
					    '''TFRobertaForQuestionAnswering''',
					    '''TFRobertaForSequenceClassification''',
					    '''TFRobertaForTokenClassification''',
					    '''TFRobertaMainLayer''',
					    '''TFRobertaModel''',
					    '''TFRobertaPreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''FlaxRobertaForCausalLM''',
					    '''FlaxRobertaForMaskedLM''',
					    '''FlaxRobertaForMultipleChoice''',
					    '''FlaxRobertaForQuestionAnswering''',
					    '''FlaxRobertaForSequenceClassification''',
					    '''FlaxRobertaForTokenClassification''',
					    '''FlaxRobertaModel''',
					    '''FlaxRobertaPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
					from .tokenization_roberta import RobertaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_roberta_fast import RobertaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roberta import (
										    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RobertaForCausalLM,
										    RobertaForMaskedLM,
										    RobertaForMultipleChoice,
										    RobertaForQuestionAnswering,
										    RobertaForSequenceClassification,
										    RobertaForTokenClassification,
										    RobertaModel,
										    RobertaPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roberta import (
										    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRobertaForCausalLM,
										    TFRobertaForMaskedLM,
										    TFRobertaForMultipleChoice,
										    TFRobertaForQuestionAnswering,
										    TFRobertaForSequenceClassification,
										    TFRobertaForTokenClassification,
										    TFRobertaMainLayer,
										    TFRobertaModel,
										    TFRobertaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roberta import (
										    FlaxRobertaForCausalLM,
										    FlaxRobertaForMaskedLM,
										    FlaxRobertaForMultipleChoice,
										    FlaxRobertaForQuestionAnswering,
										    FlaxRobertaForSequenceClassification,
										    FlaxRobertaForTokenClassification,
										    FlaxRobertaModel,
										    FlaxRobertaPreTrainedModel,
										)

else:
					import sys

					lowercase  							=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


