



import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification



def  _lowerCAmelCase     (_lowerCAmelCase):
					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  _lowerCAmelCase     (_lowerCAmelCase):
					UpperCamelCase_	,     UpperCamelCase_			= emb.weight.shape
					UpperCamelCase_			= nn.Linear(_lowerCAmelCase ,						_lowerCAmelCase ,						bias=_lowerCAmelCase)
					UpperCamelCase_			= emb.weight.data
					return lin_layer



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase="uclanlp/plbart-base" ,						_lowerCAmelCase=False ,						_lowerCAmelCase=False):
					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__":
	UpperCAmelCase :				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"""
	)
	UpperCAmelCase :				List[Any]							=parser.parse_args()
	UpperCAmelCase :				Optional[int]							=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 itertools import combinations, product






def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase							):
 if xa == ya == 0 or xa == ya == 0:
  return False
 lowerCamelCase__	:     Tuple								=		xa * xa + ya * ya
 lowerCamelCase__	:     Union[str, Any]								=		xa * xa + ya * ya
 lowerCamelCase__	:     Optional[Any]								=		(xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
 return (
     a_square + b_square == c_square
     or a_square + c_square == b_square
     or b_square + c_square == a_square
 )






def 			lowerCamelCase_    (		_lowerCamelCase = 50							):
 return sum(
     1
     for pta, pta in combinations(product(range(limit + 1							)     ,  repeat=2							)     ,  2							)
     if is_right(*_lowerCamelCase     ,  *_lowerCamelCase							)							)


if __name__ == "__main__":
      print(f"{solution() = }")

