




'''simple docstring'''
def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		int:
			if len(__lowerCamelCase     ) != len(__lowerCamelCase     ):
						raise ValueError('The length of profit and weight must be same.'     )
			if max_weight <= 0:
						raise ValueError('max_weight must greater than zero.'     )
			if any(p < 0 for p in profit     ):
						raise ValueError('Profit can not be negative.'     )
			if any(w < 0 for w in weight     ):
						raise ValueError('Weight can not be negative.'     )

			# List created to store profit gained for the 1kg in case of each weight
			# respectively.  Calculate and append profit/weight for each element.
			A_									= [p / w for p, w in zip(__lowerCamelCase       ,__lowerCamelCase     )]

			# Creating a copy of the list and sorting profit/weight in ascending order
			A_									= sorted(__lowerCamelCase     )

			# declaring useful variables
			A_									= len(__lowerCamelCase     )
			A_									= 0
			A_									= 0
			A_									= 0

			# loop till the total weight do not reach max limit e.g. 15 kg and till i<length
			while limit <= max_weight and i < length:
						# flag value for encountered greatest element in sorted_profit_by_weight
						A_									= sorted_profit_by_weight[length - i - 1]
						A_									= profit_by_weight.index(__lowerCamelCase     )
						A_									= -1

						# check if the weight encountered is less than the total weight
						# encountered before.
						if max_weight - limit >= weight[index]:
									limit += weight[index]
									# Adding profit gained for the given weight 1 ===
									# weight[index]/weight[index]
									gain += 1 * profit[index]
						else:
									# Since the weight encountered is greater than limit, therefore take the
									# required number of remaining kgs and calculate profit for it.
									# weight remaining / weight[index]
									gain += (max_weight - limit) / weight[index] * profit[index]
									break
						i += 1
			return gain


if __name__ == "__main__":
							print(
							    "Input profits, weights, and then max_weight (all positive ints) separated by "
							    "spaces."
							)

							_SCREAMING_SNAKE_CASE			=						[int(x) for x in input("Input profits separated by spaces: ").split()]
							_SCREAMING_SNAKE_CASE			=						[int(x) for x in input("Input weights separated by spaces: ").split()]
							_SCREAMING_SNAKE_CASE			=						int(input("Max weight allowed: "))

							# Function Call
							calc_profit(profit, weight, max_weight)










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   :			Optional[Any]          = logging.get_logger(__name__)





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase       ) ->			Dict:
	a									=				RobertaPreLayerNormConfig.from_pretrained(
	    __lowerCamelCase				,     architectures=["""RobertaPreLayerNormForMaskedLM"""]       )

	# convert state_dict
	a									=				torch.load(hf_hub_download(repo_id=__lowerCamelCase				,     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=__lowerCamelCase				,     config=__lowerCamelCase				,     state_dict=__lowerCamelCase       )
	model.save_pretrained(__lowerCamelCase       )

	# convert tokenizer
	a									=				AutoTokenizer.from_pretrained(__lowerCamelCase       )
	tokenizer.save_pretrained(__lowerCamelCase       )


if __name__ == "__main__":
		__UpperCamelCase   :			Union[str, Any]          = 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   :			Any          = parser.parse_args()
		convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)

