
"""simple docstring"""







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()
lowerCamelCase__        =		logging.get_logger(__name__)
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	__lowerCAmelCase      :						List[Any]						      =				RobertaPreLayerNormConfig.from_pretrained(
	    _UpperCamelCase	,  architectures=['RobertaPreLayerNormForMaskedLM']		)

	# convert state_dict
	__lowerCAmelCase      :						int						      =				torch.load(hf_hub_download(repo_id=_UpperCamelCase	,  filename='pytorch_model.bin'		)		)
	__lowerCAmelCase      :						Tuple						      =				{}
	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.'		):
			__lowerCAmelCase      :						Optional[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

		__lowerCAmelCase      :						Tuple						      =				tensor_value

	__lowerCAmelCase      :						Dict						      =				RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=_UpperCamelCase	,  config=_UpperCamelCase	,  state_dict=_UpperCamelCase		)
	model.save_pretrained(_UpperCamelCase		)

	# convert tokenizer
	__lowerCAmelCase      :						Optional[Any]						      =				AutoTokenizer.from_pretrained(_UpperCamelCase		)
	tokenizer.save_pretrained(_UpperCamelCase		)


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

"""simple docstring"""







import os
def    __lowerCAmelCase    (_UpperCamelCase = "input.txt"		):

	with open(os.path.join(os.path.dirname(_UpperCamelCase		)	,  _UpperCamelCase		)		) as input_file:
		__lowerCAmelCase      :						Optional[Any]						      =				[
		    [int(_UpperCamelCase		) for element in line.split(','		)]
		    for line in input_file.readlines()
		]

	__lowerCAmelCase      :						List[str]						      =				len(_UpperCamelCase		)
	__lowerCAmelCase      :						Tuple						      =				len(matrix[0]		)

	__lowerCAmelCase      :						int						      =				[[-1 for _ in range(_UpperCamelCase		)] for _ in range(_UpperCamelCase		)]
	for i in range(_UpperCamelCase		):
		__lowerCAmelCase      :						Any						      =				matrix[i][0]

	for j in range(1	,  _UpperCamelCase		):
		for i in range(_UpperCamelCase		):
			__lowerCAmelCase      :						Optional[Any]						      =				minimal_path_sums[i][j - 1] + matrix[i][j]

		for i in range(1	,  _UpperCamelCase		):
			__lowerCAmelCase      :						Optional[Any]						      =				min(
			    minimal_path_sums[i][j]	,  minimal_path_sums[i - 1][j] + matrix[i][j]		)

		for i in range(rows - 2	,  -1	,  -1		):
			__lowerCAmelCase      :						List[str]						      =				min(
			    minimal_path_sums[i][j]	,  minimal_path_sums[i + 1][j] + matrix[i][j]		)

	return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums		)


if __name__ == "__main__":
							print(f'{solution() = }')