






import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration

def      __lowercase					( snake_case ):







    """simple docstring"""






    __magic_name__		:List[str]        =      [
        '''encoder.version''',
        '''decoder.version''',
        '''model.encoder.version''',
        '''model.decoder.version''',
        '''_float_tensor''',
        '''decoder.output_projection.weight''',
    ]
    for k in ignore_keys:
        state_dict.pop(snake_case__,  snake_case__ )

def      __lowercase					( snake_case ):







    """simple docstring"""






    __magic_name__     ,				__magic_name__		:Union[str, Any]        =      emb.weight.shape
    __magic_name__		:List[Any]        =      nn.Linear(snake_case__,  snake_case__,  bias=snake_case__ )
    __magic_name__		:Any        =      emb.weight.data
    return lin_layer

def      __lowercase					( snake_case,  snake_case="facebook/mbart-large-en-ro",  snake_case=False,  snake_case=False ):







    """simple docstring"""






    __magic_name__		:List[Any]        =      torch.load(snake_case__,  map_location='''cpu''' )['''model''']
    remove_ignore_keys_(snake_case__ )
    __magic_name__		:List[str]        =      state_dict['''encoder.embed_tokens.weight'''].shape[0]

    __magic_name__		:Any        =      MBartConfig.from_pretrained(snake_case__,  vocab_size=snake_case__ )
    if mbart_aa and finetuned:
        __magic_name__		:str        =      '''relu'''

    __magic_name__		:Optional[int]        =      state_dict['''decoder.embed_tokens.weight''']
    __magic_name__		:Optional[Any]        =      MBartForConditionalGeneration(snake_case__ )
    model.model.load_state_dict(snake_case__ )

    if finetuned:
        __magic_name__		:int        =      make_linear_from_emb(model.model.shared )

    return model


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__      :			Tuple							=		argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """fairseq_path""", type=str, help="""bart.large, bart.large.cnn or a path to a 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="""facebook/mbart-large-cc25""",
         type=str,
         help="""Which huggingface architecture to use: mbart-large""",
     )
     parser.add_argument("""--mbart_50""", action="""store_true""", help="""whether the model is mMART-50 checkpoint""")
     parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
     SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		parser.parse_args()
     SCREAMING_SNAKE_CASE__      :			Dict							=		convert_fairseq_mbart_checkpoint_from_disk(
         args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
     )
     model.save_pretrained(args.pytorch_dump_folder_path)














from __future__ import annotations

from math import pi, sqrt

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				if inductance <= 0:
								raise ValueError('''Inductance cannot be 0 or negative''' )

				elif capacitance <= 0:
								raise ValueError('''Capacitance cannot be 0 or negative''' )

				else:
								return (
								    "Resonant frequency",
								    float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
								)


if __name__ == "__main__":
					import doctest

					doctest.testmod()






