"""simple docstring"""







import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification


def 					a__ (						UpperCAmelCase__    :     Optional[Any]							)->		List[Any]:
						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(lowercase_						,     lowercase_							)


def 					a__ (						UpperCAmelCase__    :     List[str]							)->		List[str]:
						UpperCAmelCase__		, UpperCAmelCase__   		=						emb.weight.shape
						UpperCAmelCase__   		=						nn.Linear(lowercase_						,     lowercase_						,     bias=lowercase_							)
						UpperCAmelCase__   		=						emb.weight.data
						return lin_layer


def 					a__ (						UpperCAmelCase__    :     Optional[Any]						,     UpperCAmelCase__    :     Tuple="uclanlp/plbart-base"						,     UpperCAmelCase__    :     Union[str, Any]=False						,     UpperCAmelCase__    :     Any=False							)->		Union[str, Any]:
						UpperCAmelCase__   		=						torch.load(lowercase_						,     map_location="""cpu"""							)["""model"""]
						remove_ignore_keys_(lowercase_							)
						UpperCAmelCase__   		=						state_dict["""encoder.embed_tokens.weight"""].shape[0]

						UpperCAmelCase__   		=						PLBartConfig.from_pretrained(lowercase_						,     vocab_size=lowercase_							)

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

						return model


if __name__ == "__main__":
						UpperCamelCase_    :    str											=						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[str]											=						parser.parse_args()
						UpperCamelCase_    :    Union[str, Any]											=						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

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

UpperCamelCase_    :    Union[str, Any]											=						{
    """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""",
}

UpperCamelCase_    :    str											=						{
    """mm""": from_to(0.001, 1000),
    """cm""": from_to(0.01, 100),
    """m""": from_to(1, 1),
    """km""": from_to(1000, 0.001),
    """in""": from_to(0.0254, 39.3701),
    """ft""": from_to(0.3048, 3.2_8084),
    """yd""": from_to(0.9144, 1.0_9361),
    """mi""": from_to(1609.34, 0.0_0062_1371),
}


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


if __name__ == "__main__":
						import doctest

						doctest.testmod()

