




import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification




def       __magic_name__    (			A		: Tuple       ):






			'''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(A,  A       )




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






			'''simple docstring'''







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




def       __magic_name__    (			A		: Dict,  A		: List[Any]="uclanlp/plbart-base",  A		: Optional[Any]=False,  A		: int=False       ):






			'''simple docstring'''







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

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

			a   =      state_dict["decoder.embed_tokens.weight"]
			if not classification:
						a   =      PLBartForConditionalGeneration(A       )
						model.model.load_state_dict(A       )
						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(A       )
						a   =      PLBartForSequenceClassification(A       )
						model.model.load_state_dict(A       )
						model.classification_head.load_state_dict(A       )

			return model


if __name__ == "__main__":
		__lowerCAmelCase		:    Union[str, 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'
		)
		__lowerCAmelCase		:    Tuple	      =		parser.parse_args()
		__lowerCAmelCase		:    Union[str, 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)












from ..utils import DummyObject, requires_backends




class    snake_case__							(metaclass=_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							List[str]										= ["""torch""", """transformers""", """k_diffusion"""]





				def __init__(				self    :					Optional[int] ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					List[Any]							) ->					Optional[Any]:
							requires_backends(self ,				["torch", "transformers", "k_diffusion"]							)





				@classmethod
				def 							__UpperCAmelCase							(				cls    :					str ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					Tuple							) ->					List[Any]:
							requires_backends(cls ,				["torch", "transformers", "k_diffusion"]							)





				@classmethod
				def 							__UpperCAmelCase							(				cls    :					Optional[Any] ,				*__lowerCamelCase    :					List[str] ,				**__lowerCamelCase    :					int							) ->					Dict:
							requires_backends(cls ,				["torch", "transformers", "k_diffusion"]							)






