




import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification




def      __magic_name__							(					A						:   Union[str, Any]  ):






			'''simple docstring'''







			a       =     [
			    "encoder.version",
			    "decoder.version",
			    "model.encoder.version",
			    "model.decoder.version",
			    "_float_tensor",
			    "decoder.output_projection.weight",
			]
			for k in ignore_keys:
						state_dict.pop(lowerCamelCase__,    lowerCamelCase__  )




def      __magic_name__							(					A						:   List[str]  ):






			'''simple docstring'''







			a       ,						a       =     emb.weight.shape
			a       =     nn.Linear(lowerCamelCase__,    lowerCamelCase__,    bias=lowerCamelCase__  )
			a       =     emb.weight.data
			return lin_layer




def      __magic_name__							(					A						:   Union[str, Any],    A						:   Union[str, Any]="uclanlp/plbart-base",    A						:   int=False,    A						:   int=False  ):






			'''simple docstring'''







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

			a       =     PLBartConfig.from_pretrained(lowerCamelCase__,    vocab_size=lowerCamelCase__  )

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

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

			return model


if __name__ == "__main__":
		__lowerCAmelCase						:							int			     =						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						:							str			     =						parser.parse_args()
		__lowerCAmelCase						:							Optional[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"""







def 	lowerCAmelCase__      (  lowerCamelCase__      )			->				List[str]:
							A    =       [701, 301, 132, 57, 23, 10, 4, 1]
							for gap in gaps:
														for i in range(lowerCamelCase__					,   len(lowerCamelCase__      )      ):
																					A    =       collection[i]
																					A    =       i
																					while j >= gap and collection[j - gap] > insert_value:
																												A    =       collection[j - gap]
																												j -= gap
																					if j != i:
																												A    =       insert_value
							return collection


if __name__ == "__main__":
							from doctest import testmod

							testmod()
							A     =      input('Enter numbers separated by a comma:\n').strip()
							A     =      [int(item) for item in user_input.split(',')]
							print(shell_sort(unsorted))



