
import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def  __magic_name__ (   __lowerCAmelCase	:						int							)			->   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(__lowerCAmelCase		,  __lowerCAmelCase							)




def  __magic_name__ (   __lowerCAmelCase	:						Union[str, Any]							)			->   Any:
				__lowerCamelCase					,    __lowerCamelCase    					=	emb.weight.shape
				__lowerCamelCase    					=	nn.Linear(__lowerCAmelCase		,  __lowerCAmelCase		,  bias=__lowerCAmelCase							)
				__lowerCamelCase    					=	emb.weight.data
				return lin_layer




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

				__lowerCamelCase    					=	MBartConfig.from_pretrained(__lowerCAmelCase		,  vocab_size=__lowerCAmelCase							)
				if mbart_aa and finetuned:
								__lowerCamelCase    					=	'''relu'''

				__lowerCamelCase    					=	state_dict['''decoder.embed_tokens.weight''']
				__lowerCamelCase    					=	MBartForConditionalGeneration(__lowerCAmelCase							)
				model.model.load_state_dict(__lowerCAmelCase							)

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

				return model


if __name__ == "__main__":
					SCREAMING_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")
					SCREAMING_SNAKE_CASE__   :			str												=    parser.parse_args()
					SCREAMING_SNAKE_CASE__   :			int												=    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 typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class    lowerCAmelCase__     (	__lowercase			):






		def __init__(		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							NestedDataStructureLike[PathLike]							,			SCREAMING_SNAKE_CASE__		:							Optional[NamedSplit] = None							,			SCREAMING_SNAKE_CASE__		:							Optional[Features] = None							,			SCREAMING_SNAKE_CASE__		:							str = None							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							Optional[int] = None							,			**SCREAMING_SNAKE_CASE__		:							str							,			)     ->							Union[str, Any]:
						super().__init__(
						    SCREAMING_SNAKE_CASE__							,			split=SCREAMING_SNAKE_CASE__							,			features=SCREAMING_SNAKE_CASE__							,			cache_dir=SCREAMING_SNAKE_CASE__							,			keep_in_memory=SCREAMING_SNAKE_CASE__							,			streaming=SCREAMING_SNAKE_CASE__							,			num_proc=SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__							,			)
						__lowerCamelCase    					=	path_or_paths if isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ) else {self.split: path_or_paths}
						__lowerCamelCase    					=	Text(
						    cache_dir=SCREAMING_SNAKE_CASE__							,			data_files=SCREAMING_SNAKE_CASE__							,			features=SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__							,			)







		def  __A       (		self		:							int  )     ->							Dict:
						# Build iterable dataset
						if self.streaming:
										__lowerCamelCase    					=	self.builder.as_streaming_dataset(split=self.split  )
						# Build regular (map-style) dataset
						else:
										__lowerCamelCase    					=	None
										__lowerCamelCase    					=	None
										__lowerCamelCase    					=	None
										__lowerCamelCase    					=	None

										self.builder.download_and_prepare(
										    download_config=SCREAMING_SNAKE_CASE__							,			download_mode=SCREAMING_SNAKE_CASE__							,			verification_mode=SCREAMING_SNAKE_CASE__							,			base_path=SCREAMING_SNAKE_CASE__							,			num_proc=self.num_proc							,			)
										__lowerCamelCase    					=	self.builder.as_dataset(
										    split=self.split							,			verification_mode=SCREAMING_SNAKE_CASE__							,			in_memory=self.keep_in_memory  )
						return dataset



