


import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification







def  _lowerCamelCase(			lowercase__			)		->			Union[str, Any]:

		'''simple docstring'''







		__lowercase=    [
		    '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__			)		->			List[Any]:

		'''simple docstring'''







		__lowercase,							__lowercase=    emb.weight.shape
		__lowercase=    nn.Linear(lowercase__     ,				lowercase__     ,				bias=lowercase__			)
		__lowercase=    emb.weight.data
		return lin_layer







def  _lowerCamelCase(			lowercase__     ,				lowercase__="uclanlp/plbart-base"     ,				lowercase__=False     ,				lowercase__=False			)		->			Optional[Any]:

		'''simple docstring'''







		__lowercase=    torch.load(lowercase__     ,				map_location='cpu'			)['model']
		remove_ignore_keys_(lowercase__			)
		__lowercase=    state_dict['encoder.embed_tokens.weight'].shape[0]

		__lowercase=    PLBartConfig.from_pretrained(lowercase__     ,				vocab_size=lowercase__			)

		__lowercase=    state_dict['decoder.embed_tokens.weight']
		if not classification:
				__lowercase=    PLBartForConditionalGeneration(lowercase__			)
				model.model.load_state_dict(lowercase__			)
				if finetuned:
						__lowercase=    make_linear_from_emb(model.model.shared			)

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

		return model


if __name__ == "__main__":
	lowerCAmelCase					 =	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					 =	parser.parse_args()
	lowerCAmelCase					 =	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 __future__ import annotations

from enum import Enum





class    A					(     A_				):
							UpperCamelCase_     :							List[Any] =24
							UpperCamelCase_     :							List[Any] =21
							UpperCamelCase_     :							int =18
							UpperCamelCase_     :							Optional[int] =15
							UpperCamelCase_     :							Tuple =12
							UpperCamelCase_     :							List[str] =9
							UpperCamelCase_     :							Any =6
							UpperCamelCase_     :							List[str] =3
							UpperCamelCase_     :							str =2
							UpperCamelCase_     :							str =1
							UpperCamelCase_     :							Optional[int] =-1
							UpperCamelCase_     :							List[Any] =-2
							UpperCamelCase_     :							str =-3
							UpperCamelCase_     :							Tuple =-6
							UpperCamelCase_     :							Tuple =-9
							UpperCamelCase_     :							Optional[int] =-12
							UpperCamelCase_     :							str =-15
							UpperCamelCase_     :							Optional[Any] =-18
							UpperCamelCase_     :							Any =-21
							UpperCamelCase_     :							List[str] =-24





class    A					(     A_				):
							UpperCamelCase_     :							str =8
							UpperCamelCase_     :							Optional[Any] =7
							UpperCamelCase_     :							Tuple =6
							UpperCamelCase_     :							List[Any] =5
							UpperCamelCase_     :							Optional[Any] =4
							UpperCamelCase_     :							Union[str, Any] =3
							UpperCamelCase_     :							Union[str, Any] =2
							UpperCamelCase_     :							int =1







def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__     ,				)		->			float:

		'''simple docstring'''







		if isinstance(lowercase__     ,				lowercase__			):
				__lowercase=    SIUnit[known_prefix.lower()]
		if isinstance(lowercase__     ,				lowercase__			):
				__lowercase=    SIUnit[unknown_prefix.lower()]
		__lowercase=    known_amount * (
		    1_0 ** (known_prefix.value - unknown_prefix.value)
		)
		return unknown_amount







def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__     ,				)		->			float:

		'''simple docstring'''







		if isinstance(lowercase__     ,				lowercase__			):
				__lowercase=    BinaryUnit[known_prefix.lower()]
		if isinstance(lowercase__     ,				lowercase__			):
				__lowercase=    BinaryUnit[unknown_prefix.lower()]
		__lowercase=    known_amount * (
		    2 ** ((known_prefix.value - unknown_prefix.value) * 1_0)
		)
		return unknown_amount


if __name__ == "__main__":
	import doctest

	doctest.testmod()






