



import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 			lowerCAmelCase__	(					lowerCamelCase_	:	int):





				'''simple docstring'''

				lowerCAmelCase__  :       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(lowerCamelCase_ ,lowerCamelCase_)






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__		,	lowerCAmelCase__  :       Dict				=  emb.weight.shape
				lowerCAmelCase__  :       Tuple				=  nn.Linear(lowerCamelCase_ ,lowerCamelCase_ ,bias=lowerCamelCase_)
				lowerCAmelCase__  :       Tuple				=  emb.weight.data
				return lin_layer






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	List[Any]="facebook/mbart-large-en-ro" ,lowerCamelCase_	:	str=False ,lowerCamelCase_	:	int=False):





				'''simple docstring'''

				lowerCAmelCase__  :       int				=  torch.load(lowerCamelCase_ ,map_location='''cpu''')['''model''']
				remove_ignore_keys_(lowerCamelCase_)
				lowerCAmelCase__  :       int				=  state_dict['''encoder.embed_tokens.weight'''].shape[0]

				lowerCAmelCase__  :       str				=  MBartConfig.from_pretrained(lowerCamelCase_ ,vocab_size=lowerCamelCase_)
				if mbart_aa and finetuned:
								lowerCAmelCase__  :       List[Any]				=  '''relu'''

				lowerCAmelCase__  :       Dict				=  state_dict['''decoder.embed_tokens.weight''']
				lowerCAmelCase__  :       Any				=  MBartForConditionalGeneration(lowerCamelCase_)
				model.model.load_state_dict(lowerCamelCase_)

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

				return model


if __name__ == "__main__":
							__snake_case       :	Optional[Any]            =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')
							__snake_case       :	Tuple            =parser.parse_args()
							__snake_case       :	Union[str, Any]            =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 collections.abc import Iterable, Iterator
from dataclasses import dataclass

__snake_case       :	Optional[int]            =(3, 9, -1_1, 0, 7, 5, 1, -1)
__snake_case       :	str            =(4, 6, 2, 0, 8, 1_0, 3, -2)



@dataclass
class  lowerCamelCase__    :




		'''simple docstring'''



		snake_case_			=42
		snake_case_			=42



class  lowerCamelCase__    :




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase				)  ->    None:

						"""simple docstring"""






						lowerCAmelCase__  :       Node | None				=  None
						for i in sorted(__lowerCamelCase	,reverse=__lowerCamelCase				):
										lowerCAmelCase__  :       Dict				=  Node(__lowerCamelCase	,self.head				)



		def __iter__(self				)  ->    Iterator[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  self.head
						while node:
										yield node.data
										lowerCAmelCase__  :       Dict				=  node.next_node



		def __len__(self				)  ->    int:

						"""simple docstring"""






						return sum(1 for _ in self				)



		def __str__(self				)  ->    str:

						"""simple docstring"""






						return " -> ".join([str(__lowerCamelCase				) for node in self]				)






def 			lowerCAmelCase__	(					lowerCamelCase_	:	SortedLinkedList ,lowerCamelCase_	:	SortedLinkedList):





				'''simple docstring'''

				return SortedLinkedList(list(lowerCamelCase_) + list(lowerCamelCase_))


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							__snake_case       :	Any            =SortedLinkedList
							print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))

