


'''simple docstring'''






import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration


def    _snake_case  (	A			)		->       Any:
							lowerCAmelCase__			  =      [
							    '''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    _snake_case  (	A			)		->       Optional[int]:
							lowerCAmelCase__  ,   lowerCAmelCase__			  =      emb.weight.shape
							lowerCAmelCase__			  =      nn.Linear(A					,       A					,       bias=A			)
							lowerCAmelCase__			  =      emb.weight.data
							return lin_layer


def    _snake_case  (	A					,       A="facebook/mbart-large-en-ro"					,       A=False					,       A=False			)		->       int:
							lowerCAmelCase__			  =      torch.load(A					,       map_location='''cpu'''			)['''model''']
							remove_ignore_keys_(A			)
							lowerCAmelCase__			  =      state_dict['''encoder.embed_tokens.weight'''].shape[0]

							lowerCAmelCase__			  =      MBartConfig.from_pretrained(A					,       vocab_size=A			)
							if mbart_aa and finetuned:
														lowerCAmelCase__			  =      '''relu'''

							lowerCAmelCase__			  =      state_dict['''decoder.embed_tokens.weight''']
							lowerCAmelCase__			  =      MBartForConditionalGeneration(A			)
							model.model.load_state_dict(A			)

							if finetuned:
														lowerCAmelCase__			  =      make_linear_from_emb(model.model.shared			)

							return model


if __name__ == "__main__":
		__UpperCAmelCase       							=				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''')
		__UpperCAmelCase       							=				parser.parse_args()
		__UpperCAmelCase       							=				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

__a         =   8.988E9  # units = N * m^s * C^-2




def 							a		(				snake_case__:   float		,  snake_case__:   float		,  snake_case__:   float		,  snake_case__:   float   ):


				'''simple docstring'''
				lowercase_	    =   abs(chargea * chargea   )

				if (force, chargea, chargea, distance).count(0   ) != 1:
								raise ValueError('''One and only one argument must be 0'''   )
				if distance < 0:
								raise ValueError('''Distance cannot be negative'''   )
				if force == 0:
								lowercase_	    =   COULOMBS_CONSTANT * charge_product / (distance**2)
								return {"force": force}
				elif chargea == 0:
								lowercase_	    =   abs(snake_case__   ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
								return {"charge1": chargea}
				elif chargea == 0:
								lowercase_	    =   abs(snake_case__   ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
								return {"charge2": chargea}
				elif distance == 0:
								lowercase_	    =   (COULOMBS_CONSTANT * charge_product / abs(snake_case__   )) ** 0.5
								return {"distance": distance}
				raise ValueError('''Exactly one argument must be 0'''   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()




