




import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification


def 				__SCREAMING_SNAKE_CASE    (     snake_case_		):

						'''simple docstring'''


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


def 				__SCREAMING_SNAKE_CASE    (     snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase				,					_UpperCAmelCase						=	emb.weight.shape
						_UpperCAmelCase						=	nn.Linear(snake_case_      ,       snake_case_      ,       bias=snake_case_		)
						_UpperCAmelCase						=	emb.weight.data
						return lin_layer


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_="uclanlp/plbart-base"      ,       snake_case_=False      ,       snake_case_=False		):

						'''simple docstring'''


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

						_UpperCAmelCase						=	PLBartConfig.from_pretrained(snake_case_      ,       vocab_size=snake_case_		)

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

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

						return model


if __name__ == "__main__":
				lowercase_      :							Optional[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'
				)
				lowercase_      :							Any    							=					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)










def 				__SCREAMING_SNAKE_CASE    (     snake_case_		):

						'''simple docstring'''


						return int(snake_case_		) if x - int(snake_case_		) >= 0 else int(snake_case_		) - 1


if __name__ == "__main__":
				import doctest

				doctest.testmod()




