



"""simple docstring"""







import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Optional[Any]:
		lowercase__   :    List[Any]				=			[
		    '''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      __UpperCAmelCase      (    __lowerCamelCase	)							->      List[str]:
		lowercase__		,			lowercase__   :    Dict				=			emb.weight.shape
		lowercase__   :    List[Any]				=			nn.Linear(__lowerCamelCase						,     __lowerCamelCase						,     bias=__lowerCamelCase	)
		lowercase__   :    Optional[int]				=			emb.weight.data
		return lin_layer



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

		lowercase__   :    Optional[int]				=			MBartConfig.from_pretrained(__lowerCamelCase						,     vocab_size=__lowerCamelCase	)
		if mbart_aa and finetuned:
				lowercase__   :    Tuple				=			'''relu'''

		lowercase__   :    List[Any]				=			state_dict['''decoder.embed_tokens.weight''']
		lowercase__   :    Tuple				=			MBartForConditionalGeneration(__lowerCamelCase	)
		model.model.load_state_dict(__lowerCamelCase	)

		if finetuned:
				lowercase__   :    Any				=			make_linear_from_emb(model.model.shared	)

		return model


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





"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor


lowerCAmelCase_   						=   logging.get_logger(__name__)
class        __A       (	A_    ):







		'''simple docstring'''





		def __init__(	self						:  Dict			,*_snake_case						:  Any			,**_snake_case						:  str						)					->		None:






				"""simple docstring"""



				warnings.warn(
				    '''The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
				    ''' Please use MobileViTImageProcessor instead.'''			,_snake_case			,)
				super().__init__(*_snake_case			,**_snake_case						)
