



import math

class 	lowerCamelCase_    :






						'''simple docstring'''







						def __init__(	self      :			List[Any]	,	__lowerCamelCase      :			str=0 )      ->				Any:  # a graph with Node 0,1,...,N-1
									A							:					str              =						n
									A							:					Union[str, Any]              =						[
									    [math.inf for j in range(0	,	__lowerCamelCase )] for i in range(0	,	__lowerCamelCase )
									]  # adjacency matrix for weight
									A							:					Any              =						[
									    [math.inf for j in range(0	,	__lowerCamelCase )] for i in range(0	,	__lowerCamelCase )
									]  # dp[i][j] stores minimum distance from i to j







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int]	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			Dict )      ->				List[Any]:
									A							:					Tuple              =						w







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Optional[Any]:
									for k in range(0	,	self.n ):
												for i in range(0	,	self.n ):
															for j in range(0	,	self.n ):
																		A							:					Union[str, Any]              =						min(self.dp[i][j]	,	self.dp[i][k] + self.dp[k][j] )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any]	,	__lowerCamelCase      :			Any	,	__lowerCamelCase      :			Tuple )      ->				Dict:
									return self.dp[u][v]


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE           =  Graph(5)
							graph.add_edge(0, 2, 9)
							graph.add_edge(0, 4, 10)
							graph.add_edge(1, 3, 5)
							graph.add_edge(2, 3, 7)
							graph.add_edge(3, 0, 10)
							graph.add_edge(3, 1, 2)
							graph.add_edge(3, 2, 1)
							graph.add_edge(3, 4, 6)
							graph.add_edge(4, 1, 3)
							graph.add_edge(4, 2, 4)
							graph.add_edge(4, 3, 9)
							graph.floyd_warshall()
							graph.show_min(1, 4)
							graph.show_min(0, 3)




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()
__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Optional[Any]              =						RobertaPreLayerNormConfig.from_pretrained(
			    _lowerCamelCase ,    architectures=["RobertaPreLayerNormForMaskedLM"]    )

			# convert state_dict
			A							:					List[Any]              =						torch.load(hf_hub_download(repo_id=_lowerCamelCase ,    filename="pytorch_model.bin"    )    )
			A							:					Union[str, Any]              =						{}
			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							:					int              =						"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							:					Any              =						tensor_value

			A							:					Optional[int]              =						RobertaPreLayerNormForMaskedLM.from_pretrained(
			    pretrained_model_name_or_path=_lowerCamelCase ,    config=_lowerCamelCase ,    state_dict=_lowerCamelCase    )
			model.save_pretrained(_lowerCamelCase    )

			# convert tokenizer
			A							:					Optional[Any]              =						AutoTokenizer.from_pretrained(_lowerCamelCase    )
			tokenizer.save_pretrained(_lowerCamelCase    )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE           =  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."""
							)
							__SCREAMING_SNAKE_CASE           =  parser.parse_args()
							convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)