





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Union[str, Any]    ):
		SCREAMING_SNAKE_CASE__			=      [
		    """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 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Tuple    ):
		SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      emb.weight.shape
		SCREAMING_SNAKE_CASE__			=      nn.Linear(UpperCamelCase__					,   UpperCamelCase__					,   bias=UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      emb.weight.data
		return lin_layer






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[str]    ):
		SCREAMING_SNAKE_CASE__			=      torch.load(UpperCamelCase__					,   map_location="""cpu"""    )
		SCREAMING_SNAKE_CASE__			=      mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
		SCREAMING_SNAKE_CASE__			=      mam_aaa["""model"""]
		remove_ignore_keys_(UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      state_dict["""encoder.embed_tokens.weight"""].shape[0]

		SCREAMING_SNAKE_CASE__			=      MaMaaaConfig(
		    vocab_size=UpperCamelCase__					,   max_position_embeddings=1_024					,   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"""					,   )

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

		return model


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






import warnings
from functools import wraps
from typing import Callable






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Callable    ):

		@wraps(UpperCamelCase__    )
		def _inner_fn(*UpperCamelCase__:							Dict					,   **UpperCamelCase__:							Any    ):
				warnings.warn(
				    (f'''\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.''')					,   UpperCamelCase__					,   )
				return fn(*UpperCamelCase__					,   **UpperCamelCase__    )

		return _inner_fn