
'''simple docstring'''


import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification
def 					__magic_name__(       UpperCAmelCase__ :       Dict	):
						UpperCamelCase_     :	int					     =     [
						    """encoder.version""",
						    """decoder.version""",
						    """model.encoder.version""",
						    """model.decoder.version""",
						    """_float_tensor""",
						    """decoder.output_projection.weight""",
						]
						for k in ignore_keys:
												state_dict.pop(UpperCAmelCase__							,				UpperCAmelCase__	)
def 					__magic_name__(       UpperCAmelCase__ :       str	):
						UpperCamelCase_     ,UpperCamelCase_     :	Optional[Any]					     =     emb.weight.shape
						UpperCamelCase_     :	List[Any]					     =     nn.Linear(UpperCAmelCase__							,				UpperCAmelCase__							,				bias=UpperCAmelCase__	)
						UpperCamelCase_     :	Optional[int]					     =     emb.weight.data
						return lin_layer
def 					__magic_name__(       UpperCAmelCase__ :       Tuple							,				UpperCAmelCase__ :       Optional[Any]="uclanlp/plbart-base"							,				UpperCAmelCase__ :       str=False							,				UpperCAmelCase__ :       str=False	):
						UpperCamelCase_     :	Tuple					     =     torch.load(UpperCAmelCase__							,				map_location="""cpu"""	)["""model"""]
						remove_ignore_keys_(UpperCAmelCase__	)
						UpperCamelCase_     :	int					     =     state_dict["""encoder.embed_tokens.weight"""].shape[0]

						UpperCamelCase_     :	int					     =     PLBartConfig.from_pretrained(UpperCAmelCase__							,				vocab_size=UpperCAmelCase__	)

						UpperCamelCase_     :	List[str]					     =     state_dict["""decoder.embed_tokens.weight"""]
						if not classification:
												UpperCamelCase_     :	Tuple					     =     PLBartForConditionalGeneration(UpperCAmelCase__	)
												model.model.load_state_dict(UpperCAmelCase__	)
												if finetuned:
																		UpperCamelCase_     :	Any					     =     make_linear_from_emb(model.model.shared	)

						else:
												UpperCamelCase_     :	Optional[Any]					     =     {}
												for key, value in state_dict.copy().items():
																		if key.startswith("""classification_heads.sentence_classification_head"""	):
																								UpperCamelCase_     :	List[Any]					     =     value
																								state_dict.pop(UpperCAmelCase__	)
												UpperCamelCase_     :	List[Any]					     =     PLBartForSequenceClassification(UpperCAmelCase__	)
												model.model.load_state_dict(UpperCAmelCase__	)
												model.classification_head.load_state_dict(UpperCAmelCase__	)

						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'''


__SCREAMING_SNAKE_CASE						=      {hex(i)[2:]: i for i in range(16)}  # Use [:2] to strip off the leading '0x'
def 					__magic_name__(       UpperCAmelCase__ :       str	):
						UpperCamelCase_     :	str					     =     hex_string.strip().lower()
						if not hex_string:
												raise ValueError("""Empty string was passed to the function"""	)
						UpperCamelCase_     :	Dict					     =     hex_string[0] == """-"""
						if is_negative:
												UpperCamelCase_     :	List[str]					     =     hex_string[1:]
						if not all(char in hex_table for char in hex_string	):
												raise ValueError("""Non-hexadecimal value was passed to the function"""	)
						UpperCamelCase_     :	int					     =     0
						for char in hex_string:
												UpperCamelCase_     :	Any					     =     16 * decimal_number + hex_table[char]
						return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
	from doctest import testmod

	testmod()




