'''simple docstring'''




import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration




def 							lowerCamelCase			(   UpperCAmelCase__ :      Tuple				)    ->   str:
							lowercase_      :    int       		= [
							    """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(UpperCAmelCase__   ,   UpperCAmelCase__				)




def 							lowerCamelCase			(   UpperCAmelCase__ :      Dict				)    ->   Tuple:
							lowercase_		,		lowercase_      :    int       		= emb.weight.shape
							lowercase_      :    Any       		= nn.Linear(UpperCAmelCase__   ,   UpperCAmelCase__   ,   bias=UpperCAmelCase__				)
							lowercase_      :    Dict       		= emb.weight.data
							return lin_layer




def 							lowerCamelCase			(   UpperCAmelCase__ :      List[Any]				)    ->   Dict:
							lowercase_      :    int       		= torch.load(UpperCAmelCase__   ,   map_location="""cpu"""				)
							lowercase_      :    str       		= mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
							lowercase_      :    List[Any]       		= mam_aaa["""model"""]
							remove_ignore_keys_(UpperCAmelCase__				)
							lowercase_      :    Optional[Any]       		= state_dict["""encoder.embed_tokens.weight"""].shape[0]

							lowercase_      :    int       		= MaMaaaConfig(
							    vocab_size=UpperCAmelCase__   ,   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"""   ,   )

							lowercase_      :    List[str]       		= state_dict["""decoder.embed_tokens.weight"""]
							lowercase_      :    Optional[Any]       		= MaMaaaForConditionalGeneration(UpperCAmelCase__				)
							model.model.load_state_dict(UpperCAmelCase__   ,   strict=UpperCAmelCase__				)
							lowercase_      :    int       		= make_linear_from_emb(model.model.shared				)

							return model


if __name__ == "__main__":
						_lowercase		:				Any	       =		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.")
						_lowercase		:				Any	       =		parser.parse_args()
						_lowercase		:				Dict	       =		convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
						model.save_pretrained(args.pytorch_dump_folder_path)






'''simple docstring'''




from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase		:				List[Any]	       =		{"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				int	       =		[
						    "FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "FocalNetForImageClassification",
						    "FocalNetForMaskedImageModeling",
						    "FocalNetBackbone",
						    "FocalNetModel",
						    "FocalNetPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_focalnet import (
												    FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FocalNetBackbone,
												    FocalNetForImageClassification,
												    FocalNetForMaskedImageModeling,
												    FocalNetModel,
												    FocalNetPreTrainedModel,
												)

else:
						import sys

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





