






import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification

def    lowerCAmelCase__		(			_a				:	Union[str, Any]			):
		snake_case_ :							Optional[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(_a     ,					_a			)

def    lowerCAmelCase__		(			_a				:	Optional[int]			):
		snake_case_       ,				snake_case_ :							str         =				emb.weight.shape
		snake_case_ :							Any         =				nn.Linear(_a     ,					_a     ,					bias=_a			)
		snake_case_ :							Optional[Any]         =				emb.weight.data
		return lin_layer

def    lowerCAmelCase__		(			_a				:	Optional[int]     ,					_a				:	Tuple="uclanlp/plbart-base"     ,					_a				:	int=False     ,					_a				:	List[str]=False			):
		snake_case_ :							Dict         =				torch.load(_a     ,					map_location="cpu"			)["model"]
		remove_ignore_keys_(_a			)
		snake_case_ :							Any         =				state_dict["encoder.embed_tokens.weight"].shape[0]

		snake_case_ :							Optional[int]         =				PLBartConfig.from_pretrained(_a     ,					vocab_size=_a			)

		snake_case_ :							int         =				state_dict["decoder.embed_tokens.weight"]
		if not classification:
				snake_case_ :							Union[str, Any]         =				PLBartForConditionalGeneration(_a			)
				model.model.load_state_dict(_a			)
				if finetuned:
						snake_case_ :							Union[str, Any]         =				make_linear_from_emb(model.model.shared			)

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

		return model


if __name__ == "__main__":
							lowercase     :							Optional[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'''
							)
							lowercase     :							int	     =			parser.parse_args()
							lowercase     :							Tuple	     =			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 collections import namedtuple

_a					: Any      =    namedtuple("""from_to""", """from_ to""")

_a					: Dict      =    {
    """millimeter""": """mm""",
    """centimeter""": """cm""",
    """meter""": """m""",
    """kilometer""": """km""",
    """inch""": """in""",
    """inche""": """in""",  # Trailing 's' has been stripped off
    """feet""": """ft""",
    """foot""": """ft""",
    """yard""": """yd""",
    """mile""": """mi""",
}

_a					: Optional[Any]      =    {
    """mm""": from_to(0.001, 1_0_0_0),
    """cm""": from_to(0.01, 1_0_0),
    """m""": from_to(1, 1),
    """km""": from_to(1_0_0_0, 0.001),
    """in""": from_to(0.0254, 39.3701),
    """ft""": from_to(0.3048, 3.28084),
    """yd""": from_to(0.9144, 1.09361),
    """mi""": from_to(1609.34, 0.000621371),
}





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase	)					->					float:
			__lowerCAmelCase											=				from_type.lower().rstrip("""s"""	)
			__lowerCAmelCase											=				TYPE_CONVERSION.get(lowercase					,	lowercase	)
			__lowerCAmelCase											=				to_type.lower().rstrip("""s"""	)
			__lowerCAmelCase											=				TYPE_CONVERSION.get(lowercase					,	lowercase	)
			if new_from not in METRIC_CONVERSION:
						__lowerCAmelCase											=				(
						    f'Invalid \'from_type\' value: {from_type!r}.\n'
						    f'Conversion abbreviations are: {", ".join(lowercase	)}'
						)
						raise ValueError(lowercase	)
			if new_to not in METRIC_CONVERSION:
						__lowerCAmelCase											=				(
						    f'Invalid \'to_type\' value: {to_type!r}.\n'
						    f'Conversion abbreviations are: {", ".join(lowercase	)}'
						)
						raise ValueError(lowercase	)
			return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
	import doctest

	doctest.testmod()

