



'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification







def     __UpperCamelCase					(				_lowercase       )			->      List[Any]:
	_lowercase       :							str  	=		[
	    '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     __UpperCamelCase					(				_lowercase       )			->      Optional[int]:
	_lowercase	,     _lowercase       :							List[Any]  	=		emb.weight.shape
	_lowercase       :							int  	=		nn.Linear(_lowercase,					_lowercase,					bias=_lowercase       )
	_lowercase       :							Optional[int]  	=		emb.weight.data
	return lin_layer







def     __UpperCamelCase					(				_lowercase,					_lowercase="uclanlp/plbart-base",					_lowercase=False,					_lowercase=False       )			->      str:
	_lowercase       :							int  	=		torch.load(_lowercase,					map_location='cpu'       )['model']
	remove_ignore_keys_(_lowercase       )
	_lowercase       :							Any  	=		state_dict['encoder.embed_tokens.weight'].shape[0]

	_lowercase       :							Optional[int]  	=		PLBartConfig.from_pretrained(_lowercase,					vocab_size=_lowercase       )

	_lowercase       :							str  	=		state_dict['decoder.embed_tokens.weight']
	if not classification:
		_lowercase       :							Optional[int]  	=		PLBartForConditionalGeneration(_lowercase       )
		model.model.load_state_dict(_lowercase       )
		if finetuned:
			_lowercase       :							Dict  	=		make_linear_from_emb(model.model.shared       )

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

	return model


if __name__ == "__main__":
		_A					:     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'''
		)
		_A					:     str      =parser.parse_args()
		_A					:     int      =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)





'''simple docstring'''

from __future__ import annotations

_A					:     Union[str, Any]      ='''Alexander Joslin'''







def     __UpperCamelCase					(				_lowercase       )			->      int:

	if number <= 0:
		_lowercase       :							Optional[Any]  	=		f'''n must be greater than 0. Got n = {number}'''
		raise ValueError(_lowercase       )

	_lowercase       :							List[str]  	=		[number + 1] * (number + 1)

	# starting position
	_lowercase       :							str  	=		0
	for i in range(1,					_lowercase       ):
		_lowercase       :							Union[str, Any]  	=		min(table[i + 1],					table[i] + 1       )
		# check if out of bounds
		if i * 2 <= number:
			_lowercase       :							Optional[Any]  	=		min(table[i * 2],					table[i] + 1       )
		# check if out of bounds
		if i * 3 <= number:
			_lowercase       :							Any  	=		min(table[i * 3],					table[i] + 1       )
	return table[number]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
