
from typing import TYPE_CHECKING

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


__snake_case								=  {
    """configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
    """tokenization_roberta""": ["""RobertaTokenizer"""],
}

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

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__snake_case								=  [
				    """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:
				__snake_case								=  [
				    """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:
				__snake_case								=  [
				    """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

				__snake_case								=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		int:
					'''simple docstring'''


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

										SCREAMING_SNAKE_CASE__													= update_area_of_max_square(UpperCamelCase_	,	col + 1		)
										SCREAMING_SNAKE_CASE__													= update_area_of_max_square(row + 1	,	col + 1		)
										SCREAMING_SNAKE_CASE__													= update_area_of_max_square(row + 1	,	UpperCamelCase_		)

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

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






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		int:
					'''simple docstring'''


					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]

										SCREAMING_SNAKE_CASE__													= update_area_of_max_square_using_dp_array(UpperCamelCase_	,	col + 1	,	UpperCamelCase_		)
										SCREAMING_SNAKE_CASE__													= update_area_of_max_square_using_dp_array(row + 1	,	col + 1	,	UpperCamelCase_		)
										SCREAMING_SNAKE_CASE__													= update_area_of_max_square_using_dp_array(row + 1	,	UpperCamelCase_	,	UpperCamelCase_		)

										if mat[row][col]:
															SCREAMING_SNAKE_CASE__													= 1 + min([right, diagonal, down]		)
															SCREAMING_SNAKE_CASE__													= max(largest_square_area[0]	,	UpperCamelCase_		)
															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(UpperCamelCase_		)]
					update_area_of_max_square_using_dp_array(0	,	0	,	UpperCamelCase_		)

					return largest_square_area[0]






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		int:
					'''simple docstring'''


					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(UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)
																				SCREAMING_SNAKE_CASE__													= max(dp_array[row][col]	,	UpperCamelCase_		)
															else:
																				SCREAMING_SNAKE_CASE__													= 0

					return largest_square_area






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		int:
					'''simple docstring'''


					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(UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)
																				SCREAMING_SNAKE_CASE__													= max(current_row[col]	,	UpperCamelCase_		)
															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]]))





