



import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification

def 			lowerCAmelCase_  (						_lowercase       :     Optional[int])							-> Any:







			"""simple docstring"""
			a__     :		Optional[Any]		 =						[
			    """encoder.version""",
			    """decoder.version""",
			    """model.encoder.version""",
			    """model.decoder.version""",
			    """_float_tensor""",
			    """decoder.output_projection.weight""",
			]
			for k in ignore_keys:
						state_dict.pop(_lowercase		,     _lowercase)

def 			lowerCAmelCase_  (						_lowercase       :     Optional[Any])							-> str:







			"""simple docstring"""
			a__ ,		a__     :		Optional[int]		 =						emb.weight.shape
			a__     :		Union[str, Any]		 =						nn.Linear(_lowercase		,     _lowercase		,     bias=_lowercase)
			a__     :		Any		 =						emb.weight.data
			return lin_layer

def 			lowerCAmelCase_  (						_lowercase       :     str		,     _lowercase       :     str="uclanlp/plbart-base"		,     _lowercase       :     Any=False		,     _lowercase       :     str=False)							-> Dict:







			"""simple docstring"""
			a__     :		Tuple		 =						torch.load(_lowercase		,     map_location="""cpu""")["""model"""]
			remove_ignore_keys_(_lowercase)
			a__     :		Union[str, Any]		 =						state_dict["""encoder.embed_tokens.weight"""].shape[0]

			a__     :		Union[str, Any]		 =						PLBartConfig.from_pretrained(_lowercase		,     vocab_size=_lowercase)

			a__     :		List[Any]		 =						state_dict["""decoder.embed_tokens.weight"""]
			if not classification:
						a__     :		int		 =						PLBartForConditionalGeneration(_lowercase)
						model.model.load_state_dict(_lowercase)
						if finetuned:
									a__     :		Dict		 =						make_linear_from_emb(model.model.shared)

			else:
						a__     :		List[Any]		 =						{}
						for key, value in state_dict.copy().items():
									if key.startswith("""classification_heads.sentence_classification_head"""):
												a__     :		List[str]		 =						value
												state_dict.pop(_lowercase)
						a__     :		Optional[Any]		 =						PLBartForSequenceClassification(_lowercase)
						model.model.load_state_dict(_lowercase)
						model.classification_head.load_state_dict(_lowercase)

			return model


if __name__ == "__main__":
		_lowercase	:   Any    	=argparse.ArgumentParser()
		# Required parameters
		parser.add_argument("fairseq_path", type=str, help="model.pt on local filesystem.")
		parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
		parser.add_argument(
		    "--hf_config",
		    default="uclanlp/plbart-base",
		    type=str,
		    help="Which huggingface architecture to use: plbart-base",
		)
		parser.add_argument("--finetuned", action="store_true", help="whether the model is a fine-tuned checkpoint")
		parser.add_argument(
		    "--classification", action="store_true", help="whether the model is a classification checkpoint"
		)
		_lowercase	:   str    	=parser.parse_args()
		_lowercase	:   Any    	=convert_fairseq_plbart_checkpoint_from_disk(
		    args.fairseq_path,
		    hf_config_path=args.hf_config,
		    finetuned=args.finetuned,
		    classification=args.classification,
		)
		model.save_pretrained(args.pytorch_dump_folder_path)









from collections.abc import Callable

_lowercase	:   int    	=Callable[[float], float]  # type alias for a real -> real function

def 			lowerCAmelCase_  (						_lowercase       :     RealFunc		,     _lowercase       :     RealFunc		,     _lowercase       :     int		,     )							-> float:







			"""simple docstring"""
			a__     :		Optional[int]		 =						float(_lowercase)
			while True:
						try:
									a__     :		Union[str, Any]		 =						prev_guess - function(_lowercase) / derivative(_lowercase)
						except ZeroDivisionError:
									raise ZeroDivisionError("""Could not find root""") from None
						if abs(prev_guess - next_guess) < 10**-5:
									return next_guess
						a__     :		Optional[Any]		 =						next_guess

def 			lowerCAmelCase_  (						_lowercase       :     float)							-> float:







			"""simple docstring"""
			return (x**3) - (2 * x) - 5

def 			lowerCAmelCase_  (						_lowercase       :     float)							-> float:







			"""simple docstring"""
			return 3 * (x**2) - 2


if __name__ == "__main__":
		print(newton(f, fa, 3))




