



def  UpperCamelCase						(       _A			:				int			,							_A			:				int			,							_A			:				list[list[int]]    )->       int:





						"""simple docstring"""





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

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

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

						A__							=       [0]
						update_area_of_max_square(0			,							0    )
						return largest_square_area[0]
def  UpperCamelCase						(       _A			:				int			,							_A			:				int			,							_A			:				list[list[int]]    )->       int:





						"""simple docstring"""





						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]

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

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

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

						return largest_square_area[0]
def  UpperCamelCase						(       _A			:				int			,							_A			:				int			,							_A			:				list[list[int]]    )->       int:





						"""simple docstring"""





						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(_A			,							_A			,							_A    )
																								A__							=       max(dp_array[row][col]			,							_A    )
																		else:
																								A__							=       0

						return largest_square_area
def  UpperCamelCase						(       _A			:				int			,							_A			:				int			,							_A			:				list[list[int]]    )->       int:





						"""simple docstring"""





						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(_A			,							_A			,							_A    )
																								A__							=       max(current_row[col]			,							_A    )
																		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]]))










import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_							:      Union[str, Any]      =  logging.get_logger(__name__)
def  UpperCamelCase						(       _A			:				str			,							_A			:				str    )->       Any:





						"""simple docstring"""





						A__							=       RobertaPreLayerNormConfig.from_pretrained(
						    _A			,							architectures=["RobertaPreLayerNormForMaskedLM"]    )

						# convert state_dict
						A__							=       torch.load(hf_hub_download(repo_id=_A			,							filename="pytorch_model.bin"    )    )
						A__							=       {}
						for tensor_key, tensor_value in original_state_dict.items():
												# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
												if tensor_key.startswith("roberta."    ):
																		A__							=       "roberta_prelayernorm." + tensor_key[len("roberta."    ) :]

												# The original implementation contains weights which are not used, remove them from the state_dict
												if tensor_key.endswith(".self.LayerNorm.weight"    ) or tensor_key.endswith(".self.LayerNorm.bias"    ):
																		continue

												A__							=       tensor_value

						A__							=       RobertaPreLayerNormForMaskedLM.from_pretrained(
						    pretrained_model_name_or_path=_A			,							config=_A			,							state_dict=_A    )
						model.save_pretrained(_A    )

						# convert tokenizer
						A__							=       AutoTokenizer.from_pretrained(_A    )
						tokenizer.save_pretrained(_A    )


if __name__ == "__main__":
						UpperCAmelCase_							:      str      =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--checkpoint-repo",
						    default=None,
						    type=str,
						    required=True,
						    help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						UpperCAmelCase_							:      List[Any]      =  parser.parse_args()
						convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)





