



import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification



def        __a     (    lowerCAmelCase_   :					List[Any]				)      -> Optional[int]:






						'''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(lowerCAmelCase_			,lowerCAmelCase_				)



def        __a     (    lowerCAmelCase_   :					int				)      -> Optional[int]:






						'''simple docstring'''



						UpperCAmelCase_,   UpperCAmelCase_=   emb.weight.shape
						UpperCAmelCase_=   nn.Linear(lowerCAmelCase_			,lowerCAmelCase_			,bias=lowerCAmelCase_				)
						UpperCAmelCase_=   emb.weight.data
						return lin_layer



def        __a     (    lowerCAmelCase_   :					List[Any]			,lowerCAmelCase_   :					Dict="uclanlp/plbart-base"			,lowerCAmelCase_   :					List[str]=False			,lowerCAmelCase_   :					Tuple=False				)      -> Any:






						'''simple docstring'''



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

						UpperCAmelCase_=   PLBartConfig.from_pretrained(lowerCAmelCase_			,vocab_size=lowerCAmelCase_				)

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

						return model


if __name__ == "__main__":
	__A				    =						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'''
	)
	__A				    =						parser.parse_args()
	__A				    =						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        __a     (    lowerCAmelCase_   :					list				)      -> bool:






						'''simple docstring'''



						if not isinstance(lowerCAmelCase_			,lowerCAmelCase_				):
												raise ValueError("""Input series is not valid, valid series - [1, 2/3, 2]"""				)
						if len(lowerCAmelCase_				) == 0:
												raise ValueError("""Input list must be a non empty list"""				)
						if len(lowerCAmelCase_				) == 1 and series[0] != 0:
												return True
						UpperCAmelCase_=   []
						UpperCAmelCase_=   len(lowerCAmelCase_				)
						for i in range(0			,lowerCAmelCase_				):
												if series[i] == 0:
																		raise ValueError("""Input series cannot have 0 as an element"""				)
												rec_series.append(1 / series[i]				)
						UpperCAmelCase_=   rec_series[1] - rec_series[0]
						for index in range(2			,lowerCAmelCase_				):
												if rec_series[index] - rec_series[index - 1] != common_diff:
																		return False
						return True



def        __a     (    lowerCAmelCase_   :					list				)      -> float:






						'''simple docstring'''



						if not isinstance(lowerCAmelCase_			,lowerCAmelCase_				):
												raise ValueError("""Input series is not valid, valid series - [2, 4, 6]"""				)
						if len(lowerCAmelCase_				) == 0:
												raise ValueError("""Input list must be a non empty list"""				)
						UpperCAmelCase_=   0
						for val in series:
												answer += 1 / val
						return len(lowerCAmelCase_				) / answer


if __name__ == "__main__":
	import doctest

	doctest.testmod()




