





'''simple docstring'''



import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification




def       _lowerCAmelCase  ( lowerCamelCase_	:     Dict ):
			__lowercase   =							[
			    '''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       _lowerCAmelCase  ( lowerCamelCase_	:     Union[str, Any] ):
			__lowercase  ,						__lowercase   =							emb.weight.shape
			__lowercase   =							nn.Linear(lowerCamelCase_						,				lowerCamelCase_						,				bias=lowerCamelCase_ )
			__lowercase   =							emb.weight.data
			return lin_layer




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

			__lowercase   =							PLBartConfig.from_pretrained(lowerCamelCase_						,				vocab_size=lowerCamelCase_ )

			__lowercase   =							state_dict['''decoder.embed_tokens.weight''']
			if not classification:
						__lowercase   =							PLBartForConditionalGeneration(lowerCamelCase_ )
						model.model.load_state_dict(lowerCamelCase_ )
						if finetuned:
									__lowercase   =							make_linear_from_emb(model.model.shared )

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

			return model


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE    =			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'''
	)
	_SCREAMING_SNAKE_CASE    =			parser.parse_args()
	_SCREAMING_SNAKE_CASE    =			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 ..utils import DummyObject, requires_backends
class 	SCREAMING_SNAKE_CASE__			(				metaclass=_UpperCAmelCase      ):
    _A			    =							["torch", "transformers", "invisible_watermark"]




    def __init__(					self				,     *lowercase__				,     **lowercase__			):






          """simple docstring"""






          requires_backends(self				,     ["torch", "transformers", "invisible_watermark"]			)




    @classmethod
    def   __lowerCamelCase      (					cls				,     *lowercase__				,     **lowercase__			):






          """simple docstring"""






          requires_backends(cls				,     ["torch", "transformers", "invisible_watermark"]			)







    @classmethod
    def   __lowerCamelCase      (					cls				,     *lowercase__				,     **lowercase__			):






          """simple docstring"""






          requires_backends(cls				,     ["torch", "transformers", "invisible_watermark"]			)






class 	SCREAMING_SNAKE_CASE__			(				metaclass=_UpperCAmelCase      ):
    _A			    =							["torch", "transformers", "invisible_watermark"]




    def __init__(					self				,     *lowercase__				,     **lowercase__			):






          """simple docstring"""






          requires_backends(self				,     ["torch", "transformers", "invisible_watermark"]			)




    @classmethod
    def   __lowerCamelCase      (					cls				,     *lowercase__				,     **lowercase__			):






          """simple docstring"""






          requires_backends(cls				,     ["torch", "transformers", "invisible_watermark"]			)







    @classmethod
    def   __lowerCamelCase      (					cls				,     *lowercase__				,     **lowercase__			):






          """simple docstring"""






          requires_backends(cls				,     ["torch", "transformers", "invisible_watermark"]			)

