"""simple docstring"""







import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Union[str, Any]						)							->    Any:

	'''simple docstring'''





	__snake_case    :							Tuple   	=				[
	    '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 						__UpperCAmelCase       (					UpperCAmelCase_					:       List[str]						)							->    str:

	'''simple docstring'''





	__snake_case	,       __snake_case    :							Tuple   	=				emb.weight.shape
	__snake_case    :							Optional[Any]   	=				nn.Linear(__UpperCamelCase    ,							__UpperCamelCase    ,							bias=__UpperCamelCase						)
	__snake_case    :							Any   	=				emb.weight.data
	return lin_layer
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       str						)							->    str:

	'''simple docstring'''





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

	__snake_case    :							Optional[int]   	=				MaMaaaConfig(
	    vocab_size=__UpperCamelCase    ,							max_position_embeddings=10_24    ,							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'    ,							)

	__snake_case    :							Optional[int]   	=				state_dict['decoder.embed_tokens.weight']
	__snake_case    :							List[Any]   	=				MaMaaaForConditionalGeneration(__UpperCamelCase						)
	model.model.load_state_dict(__UpperCamelCase    ,							strict=__UpperCamelCase						)
	__snake_case    :							int   	=				make_linear_from_emb(model.model.shared						)

	return model


if __name__ == "__main__":
	_a  :  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.")
	_a  :  Dict=				parser.parse_args()
	_a  :  List[str]=				convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
	model.save_pretrained(args.pytorch_dump_folder_path)







"""simple docstring"""







from abc import ABC, abstractmethod
from argparse import ArgumentParser

class 	UpperCamelCase    (					lowercase		):



				@staticmethod
				@abstractmethod
				def       _lowercase  (_A       :   ArgumentParser)					->			Tuple:
					raise NotImplementedError()






				@abstractmethod
				def       _lowercase  (self       :   Any)					->			Optional[Any]:
					raise NotImplementedError()





