





'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase_   :   str       =					{"configuration_vit_msn": ["VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMSNConfig"]}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase_   :   Optional[int]       =					[
		    "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

		lowerCAmelCase_   :   str       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)










'''simple docstring'''


import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def    _lowerCamelCase  (__lowerCamelCase  :     Any     )   ->    Optional[int]:
			a__     = [
			    "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(__lowerCamelCase					,  __lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     int     )   ->    List[Any]:
			a__   ,				a__     = emb.weight.shape
			a__     = nn.Linear(__lowerCamelCase					,  __lowerCamelCase					,  bias=__lowerCamelCase     )
			a__     = emb.weight.data
			return lin_layer







def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]     )   ->    Any:
			a__     = torch.load(__lowerCamelCase					,  map_location="cpu"     )
			a__     = mam_aaa["args"] or mam_aaa["cfg"]["model"]
			a__     = mam_aaa["model"]
			remove_ignore_keys_(__lowerCamelCase     )
			a__     = state_dict["encoder.embed_tokens.weight"].shape[0]

			a__     = MaMaaaConfig(
			    vocab_size=__lowerCamelCase					,  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__     = state_dict["decoder.embed_tokens.weight"]
			a__     = MaMaaaForConditionalGeneration(__lowerCamelCase     )
			model.model.load_state_dict(__lowerCamelCase					,  strict=__lowerCamelCase     )
			a__     = make_linear_from_emb(model.model.shared     )

			return model


if __name__ == "__main__":
		lowerCAmelCase_   :   Tuple       =					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.")
		lowerCAmelCase_   :   Tuple       =					parser.parse_args()
		lowerCAmelCase_   :   int       =					convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
		model.save_pretrained(args.pytorch_dump_folder_path)



