



import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase			,       _lowercase	)   ->    List[str]:
							# Initialise PyTorch model
							UpperCAmelCase  :	Dict							  =	FunnelConfig.from_json_file(_lowercase	)
							print(F'''Building PyTorch model from configuration: {config}'''	)
							UpperCAmelCase  :	Tuple							  =	FunnelBaseModel(_lowercase	) if base_model else FunnelModel(_lowercase	)

							# Load weights from tf checkpoint
							load_tf_weights_in_funnel(_lowercase			,       _lowercase			,       _lowercase	)

							# Save pytorch-model
							print(F'''Save PyTorch model to {pytorch_dump_path}'''	)
							torch.save(model.state_dict()			,       _lowercase	)


if __name__ == "__main__":
							a     :    Tuple      						=					argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
							)
							parser.add_argument(
							    """--config_file""",
							    default=None,
							    type=str,
							    required=True,
							    help="""The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.""",
							)
							parser.add_argument(
							    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							parser.add_argument(
							    """--base_model""", action="""store_true""", help="""Whether you want just the base model (no decoder) or not."""
							)
							a     :    Any      						=					parser.parse_args()
							convert_tf_checkpoint_to_pytorch(
							    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
							)






'''simple docstring'''




a     :    List[Any]      						=					"""Alexander Joslin"""

import operator as op

from .stack import Stack



def    __lowerCamelCase						(      _lowercase	)   ->    int:
							UpperCAmelCase  :	Dict							  =	{"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}

							UpperCAmelCase  :	Stack[int]							  =	Stack()
							UpperCAmelCase  :	Stack[str]							  =	Stack()

							for i in equation:
														if i.isdigit():
																					# RULE 1
																					operand_stack.push(int(_lowercase	)	)
														elif i in operators:
																					# RULE 2
																					operator_stack.push(_lowercase	)
														elif i == ")":
																					# RULE 4
																					UpperCAmelCase  :	List[Any]							  =	operator_stack.peek()
																					operator_stack.pop()
																					UpperCAmelCase  :	str							  =	operand_stack.peek()
																					operand_stack.pop()
																					UpperCAmelCase  :	str							  =	operand_stack.peek()
																					operand_stack.pop()

																					UpperCAmelCase  :	List[Any]							  =	operators[opr](_lowercase			,       _lowercase	)
																					operand_stack.push(_lowercase	)

    # RULE 5
							return operand_stack.peek()


if __name__ == "__main__":
							a     :    Tuple      						=					"""(5 + ((4 * 2) * (2 + 3)))"""
							# answer = 45
							print(F'''{equation} = {dijkstras_two_stack_algorithm(equation)}''')

