


"""simple docstring"""




import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration





def       a_ (	lowercase__ :str     ):
			__lowerCamelCase          =      [
			    """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       a_ (	lowercase__ :Optional[Any]     ):
			__lowerCamelCase     ,__lowerCamelCase          =      emb.weight.shape
			__lowerCamelCase          =      nn.Linear(lowercase__,			lowercase__,			bias=lowercase__     )
			__lowerCamelCase          =      emb.weight.data
			return lin_layer





def       a_ (	lowercase__ :Tuple     ):
			__lowerCamelCase          =      torch.load(lowercase__,			map_location="""cpu"""     )
			__lowerCamelCase          =      mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
			__lowerCamelCase          =      mam_aaa["""model"""]
			remove_ignore_keys_(lowercase__     )
			__lowerCamelCase          =      state_dict["""encoder.embed_tokens.weight"""].shape[0]

			__lowerCamelCase          =      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""",			)

			__lowerCamelCase          =      state_dict["""decoder.embed_tokens.weight"""]
			__lowerCamelCase          =      MaMaaaForConditionalGeneration(lowercase__     )
			model.model.load_state_dict(lowercase__,			strict=lowercase__     )
			__lowerCamelCase          =      make_linear_from_emb(model.model.shared     )

			return model


if __name__ == "__main__":
					__magic_name__						: Optional[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.')
					__magic_name__						: Union[str, Any]    =				parser.parse_args()
					__magic_name__						: str    =				convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
					model.save_pretrained(args.pytorch_dump_folder_path)








"""simple docstring"""




from __future__ import annotations





def       a_ (	lowercase__ :list[float]     ):
			if len(lowercase__     ) < 2:
						raise ValueError("""Monogons and Digons are not polygons in the Euclidean space"""     )
			if any(i <= 0 for i in nums     ):
						raise ValueError("""All values must be greater than 0"""     )
			__lowerCamelCase          =      nums.copy()
			copy_nums.sort()
			return copy_nums[-1] < sum(copy_nums[:-1]     )


if __name__ == "__main__":
					import doctest

					doctest.testmod()




