
_A   =			"Alexander Joslin"

import operator as op

from .stack import Stack



def 						lowercase_      (       A__		):
    """simple docstring"""

    snake_case								= {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}

    snake_case								= Stack()
    snake_case								= Stack()

    for i in equation:
        if i.isdigit():
            # RULE 1
            operand_stack.push(int(A__		)		)
        elif i in operators:
            # RULE 2
            operator_stack.push(A__		)
        elif i == ")":
            # RULE 4
            snake_case								= operator_stack.peek()
            operator_stack.pop()
            snake_case								= operand_stack.peek()
            operand_stack.pop()
            snake_case								= operand_stack.peek()
            operand_stack.pop()

            snake_case								= operators[opr](A__			,		A__		)
            operand_stack.push(A__		)

    # RULE 5
    return operand_stack.peek()


if __name__ == "__main__":
   _A   =			"(5 + ((4 * 2) * (2 + 3)))"
   # answer = 45
   print(f"{equation} = {dijkstras_two_stack_algorithm(equation)}")



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()
_A   =			logging.get_logger(__name__)



def 						lowercase_      (       A__			,		A__		)							->							int:
    """simple docstring"""

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

    # convert state_dict
    snake_case								= torch.load(hf_hub_download(repo_id=A__			,		filename="pytorch_model.bin"		)		)
    snake_case								= {}
    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."		):
            snake_case								= "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

        snake_case								= tensor_value

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

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


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

