






import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification





def 							lowercase__		(				lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		Tuple							=						[
				    '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 							lowercase__		(				lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						emb.weight.shape
				_SCREAMING_SNAKE_CASE      :		Optional[int]							=						nn.Linear(lowerCamelCase,	lowerCamelCase,	bias=lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		Tuple							=						emb.weight.data
				return lin_layer





def 							lowercase__		(				lowerCamelCase,	lowerCamelCase="uclanlp/plbart-base",	lowerCamelCase=False,	lowerCamelCase=False					):
				_SCREAMING_SNAKE_CASE      :		str							=						torch.load(lowerCamelCase,	map_location='cpu'					)['model']
				remove_ignore_keys_(lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		Optional[int]							=						state_dict['encoder.embed_tokens.weight'].shape[0]

				_SCREAMING_SNAKE_CASE      :		Tuple							=						PLBartConfig.from_pretrained(lowerCamelCase,	vocab_size=lowerCamelCase					)

				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						state_dict['decoder.embed_tokens.weight']
				if not classification:
								_SCREAMING_SNAKE_CASE      :		Tuple							=						PLBartForConditionalGeneration(lowerCamelCase					)
								model.model.load_state_dict(lowerCamelCase					)
								if finetuned:
												_SCREAMING_SNAKE_CASE      :		Dict							=						make_linear_from_emb(model.model.shared					)

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

				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)









"""simple docstring"""
from .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims

