





"""simple docstring"""




import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       int   )					->Tuple:






	'''simple docstring'''






	a :						Union[str, Any]      =							[
	    "encoder.version",
	    "decoder.version",
	    "model.encoder.version",
	    "model.decoder.version",
	    "decoder.output_projection.weight",
	    "_float_tensor",
	    "encoder.embed_positions._float_tensor",
	    "decoder.embed_positions._float_tensor",
	]
	for k in ignore_keys:
		state_dict.pop(_lowercase       ,			_lowercase   )






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Any   )					->Union[str, Any]:






	'''simple docstring'''






	a,			a :						List[str]      =							emb.weight.shape
	a :						List[Any]      =							nn.Linear(_lowercase       ,			_lowercase       ,			bias=_lowercase   )
	a :						int      =							emb.weight.data
	return lin_layer






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Union[str, Any]   )					->Tuple:






	'''simple docstring'''






	a :						Tuple      =							torch.load(_lowercase       ,			map_location="cpu"   )
	a :						Optional[int]      =							mam_aaa["args"] or mam_aaa["cfg"]["model"]
	a :						Any      =							mam_aaa["model"]
	remove_ignore_keys_(_lowercase   )
	a :						Optional[int]      =							state_dict["encoder.embed_tokens.weight"].shape[0]

	a :						Optional[int]      =							MaMaaaConfig(
	    vocab_size=_lowercase       ,			max_position_embeddings=1024       ,			encoder_layers=args.encoder_layers       ,			decoder_layers=args.decoder_layers       ,			encoder_attention_heads=args.encoder_attention_heads       ,			decoder_attention_heads=args.decoder_attention_heads       ,			encoder_ffn_dim=args.encoder_ffn_embed_dim       ,			decoder_ffn_dim=args.decoder_ffn_embed_dim       ,			d_model=args.encoder_embed_dim       ,			encoder_layerdrop=args.encoder_layerdrop       ,			decoder_layerdrop=args.decoder_layerdrop       ,			dropout=args.dropout       ,			attention_dropout=args.attention_dropout       ,			activation_dropout=args.activation_dropout       ,			activation_function="relu"       ,			)

	a :						Dict      =							state_dict["decoder.embed_tokens.weight"]
	a :						List[str]      =							MaMaaaForConditionalGeneration(_lowercase   )
	model.model.load_state_dict(_lowercase       ,			strict=_lowercase   )
	a :						Tuple      =							make_linear_from_emb(model.model.shared   )

	return model


if __name__ == "__main__":
		a		:					int	      =     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('''fairseq_path''', type=str, help='''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.''')
		a		:					List[str]	      =     parser.parse_args()
		a		:					Union[str, Any]	      =     convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
		model.save_pretrained(args.pytorch_dump_folder_path)









"""simple docstring"""




def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[int]   )					->float:






	'''simple docstring'''






	if not nums:  # Makes sure that the list is not empty
		raise ValueError("List is empty"   )

	a :						Dict      =							sum(_lowercase   ) / len(_lowercase   )  # Calculate the average
	return sum(abs(x - average   ) for x in nums   ) / len(_lowercase   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()


