


'''simple docstring'''
import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def       a				(      __a       )  ->     Tuple:



					'''simple docstring'''






					UpperCamelCase__       :str							 =			[
					    '''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(__a			, __a       )


def       a				(      __a       )  ->     List[str]:



					'''simple docstring'''






					UpperCamelCase__ ,    UpperCamelCase__       :Any							 =			emb.weight.shape
					UpperCamelCase__       :Tuple							 =			nn.Linear(__a			, __a			, bias=__a       )
					UpperCamelCase__       :Tuple							 =			emb.weight.data
					return lin_layer


def       a				(      __a       )  ->     str:



					'''simple docstring'''






					UpperCamelCase__       :str							 =			torch.load(__a			, map_location='''cpu'''       )
					UpperCamelCase__       :Union[str, Any]							 =			mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
					UpperCamelCase__       :int							 =			mam_aaa['''model''']
					remove_ignore_keys_(__a       )
					UpperCamelCase__       :Optional[Any]							 =			state_dict['''encoder.embed_tokens.weight'''].shape[0]

					UpperCamelCase__       :Optional[Any]							 =			MaMaaaConfig(
					    vocab_size=__a			, 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'''			, )

					UpperCamelCase__       :List[str]							 =			state_dict['''decoder.embed_tokens.weight''']
					UpperCamelCase__       :List[str]							 =			MaMaaaForConditionalGeneration(__a       )
					model.model.load_state_dict(__a			, strict=__a       )
					UpperCamelCase__       :List[Any]							 =			make_linear_from_emb(model.model.shared       )

					return model


if __name__ == "__main__":
		__snake_case											=					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.''')
		__snake_case											=					parser.parse_args()
		__snake_case											=					convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
		model.save_pretrained(args.pytorch_dump_folder_path)



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case											=					{'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case											=					[
		    '''VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ViTMSNModel''',
		    '''ViTMSNForImageClassification''',
		    '''ViTMSNPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vit_msn import (
				    VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViTMSNForImageClassification,
				    ViTMSNModel,
				    ViTMSNPreTrainedModel,
				)

else:
		import sys

		__snake_case											=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)