




'''simple docstring'''







import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification






def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







		_snake_case	       =       [
		    "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 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







		_snake_case       ,    _snake_case	       =       emb.weight.shape
		_snake_case	       =       nn.Linear(lowercase_						,							lowercase_						,							bias=lowercase_  )
		_snake_case	       =       emb.weight.data
		return lin_layer






def 		_lowercase		(	lowercase_						,							lowercase_="uclanlp/plbart-base"						,							lowercase_=False						,							lowercase_=False  ):



		'''simple docstring'''







		_snake_case	       =       torch.load(lowercase_						,							map_location="cpu"  )["model"]
		remove_ignore_keys_(lowercase_  )
		_snake_case	       =       state_dict["encoder.embed_tokens.weight"].shape[0]

		_snake_case	       =       PLBartConfig.from_pretrained(lowercase_						,							vocab_size=lowercase_  )

		_snake_case	       =       state_dict["decoder.embed_tokens.weight"]
		if not classification:
				_snake_case	       =       PLBartForConditionalGeneration(lowercase_  )
				model.model.load_state_dict(lowercase_  )
				if finetuned:
						_snake_case	       =       make_linear_from_emb(model.model.shared  )

		else:
				_snake_case	       =       {}
				for key, value in state_dict.copy().items():
						if key.startswith("classification_heads.sentence_classification_head"  ):
								_snake_case	       =       value
								state_dict.pop(lowercase_  )
				_snake_case	       =       PLBartForSequenceClassification(lowercase_  )
				model.model.load_state_dict(lowercase_  )
				model.classification_head.load_state_dict(lowercase_  )

		return model


if __name__ == "__main__":
						_lowerCamelCase							:						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'
						)
						_lowerCamelCase							:						List[str]								=  parser.parse_args()
						_lowerCamelCase							:						List[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)









import math as m







def   lowerCamelCase(      a__		,a__		,a__=0.001):
				return (f(a + h) - f(a - h)) / (2 * h)







def   lowerCamelCase(      a__		,a__=0		,a__=100		,a__=0.0001		,a__=1e-6		,a__=False):
				_SCREAMING_SNAKE_CASE	     =xa  # set the initial guess
				_SCREAMING_SNAKE_CASE	     =[a]
				_SCREAMING_SNAKE_CASE	     =abs(f(a__))
				_SCREAMING_SNAKE_CASE	     =lambda a__: calc_derivative(a__		,a__		,h=a__)  # noqa: E731  Derivative of f(x)
				for _ in range(a__):
								if fa(a__) == 0:
												raise ValueError('''No converging solution found''')
								_SCREAMING_SNAKE_CASE	     =a - f(a__) / fa(a__)  # Calculate the next estimate
								if logsteps:
												steps.append(a__)
								if error < maxerror:
												break
				else:
								raise ValueError('''Iteration limit reached, no converging solution found''')
				if logsteps:
								# If logstep is true, then log intermediate steps
								return a, error, steps
				return a, error


if __name__ == "__main__":
							from matplotlib import pyplot as plt

							snake_case_  :	Tuple							  =							lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
							snake_case_  , snake_case_  , snake_case_  :	str							  =							newton_raphson(
							    f, xa=10, maxiter=10_00, step=1e-6, logsteps=True
							)
							plt.plot([abs(f(x)) for x in steps])
							plt.xlabel('''step''')
							plt.ylabel('''error''')
							plt.show()
							print(f"""solution = {{{solution:f}}}, error = {{{error:f}}}""")