


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def      __lowerCamelCase		(				lowerCamelCase__						:		List[Any]	):





			'''simple docstring'''




			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(SCREAMING_SNAKE_CASE__   ,				SCREAMING_SNAKE_CASE__	)






def      __lowerCamelCase		(				lowerCamelCase__						:		Optional[Any]	):





			'''simple docstring'''




			lowerCamelCase  						=   emb.weight.shape
			lowerCamelCase  						=   nn.Linear(SCREAMING_SNAKE_CASE__   ,				SCREAMING_SNAKE_CASE__   ,				bias=SCREAMING_SNAKE_CASE__	)
			lowerCamelCase  						=   emb.weight.data
			return lin_layer






def      __lowerCamelCase		(				lowerCamelCase__						:		str   ,				lowerCamelCase__						:		Optional[Any]="facebook/mbart-large-en-ro"   ,				lowerCamelCase__						:		Union[str, Any]=False   ,				lowerCamelCase__						:		List[str]=False	):





			'''simple docstring'''




			lowerCamelCase  						=   torch.load(SCREAMING_SNAKE_CASE__   ,				map_location="""cpu"""	)["""model"""]
			remove_ignore_keys_(SCREAMING_SNAKE_CASE__	)
			lowerCamelCase  						=   state_dict["""encoder.embed_tokens.weight"""].shape[0]

			lowerCamelCase  						=   MBartConfig.from_pretrained(SCREAMING_SNAKE_CASE__   ,				vocab_size=SCREAMING_SNAKE_CASE__	)
			if mbart_aa and finetuned:
						lowerCamelCase  						=   """relu"""

			lowerCamelCase  						=   state_dict["""decoder.embed_tokens.weight"""]
			lowerCamelCase  						=   MBartForConditionalGeneration(SCREAMING_SNAKE_CASE__	)
			model.model.load_state_dict(SCREAMING_SNAKE_CASE__	)

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

			return model


if __name__ == "__main__":
		UpperCAmelCase					:  Union[str, 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")
		UpperCAmelCase					:  Optional[int]			=						parser.parse_args()
		UpperCAmelCase					:  Optional[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)





'''simple docstring'''


def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				return int((input_a, input_a).count(0     ) != 0     )







def 			snake_case_   (  ):







				"""simple docstring"""





				assert nand_gate(0							,      0     ) == 1
				assert nand_gate(0							,      1     ) == 1
				assert nand_gate(1							,      0     ) == 1
				assert nand_gate(1							,      1     ) == 0


if __name__ == "__main__":
				print(nand_gate(0, 0))
				print(nand_gate(0, 1))
				print(nand_gate(1, 0))
				print(nand_gate(1, 1))




