






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration





def 						lowerCAmelCase_		(lowercase__    :							str							)		->     str:


				'''simple docstring'''




				lowerCAmelCase__         =    [
				    '''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_		(lowercase__    :							str							)		->     Tuple:


				'''simple docstring'''




				lowerCAmelCase__      ,		lowerCAmelCase__         =    emb.weight.shape
				lowerCAmelCase__         =    nn.Linear(__lowerCAmelCase  ,       __lowerCAmelCase  ,       bias=__lowerCAmelCase							)
				lowerCAmelCase__         =    emb.weight.data
				return lin_layer





def 						lowerCAmelCase_		(lowercase__    :							List[Any]							)		->     Any:


				'''simple docstring'''




				lowerCAmelCase__         =    torch.load(__lowerCAmelCase  ,       map_location='''cpu'''							)
				lowerCAmelCase__         =    mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
				lowerCAmelCase__         =    mam_aaa['''model''']
				remove_ignore_keys_(__lowerCAmelCase							)
				lowerCAmelCase__         =    state_dict['''encoder.embed_tokens.weight'''].shape[0]

				lowerCAmelCase__         =    MaMaaaConfig(
				    vocab_size=__lowerCAmelCase  ,       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'''  ,       )

				lowerCAmelCase__         =    state_dict['''decoder.embed_tokens.weight''']
				lowerCAmelCase__         =    MaMaaaForConditionalGeneration(__lowerCAmelCase							)
				model.model.load_state_dict(__lowerCAmelCase  ,       strict=__lowerCAmelCase							)
				lowerCAmelCase__         =    make_linear_from_emb(model.model.shared							)

				return model


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







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__magic_name__  							=	logging.get_logger(__name__)

__magic_name__  							=	{
    '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}




class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		Dict     						=  'biogpt'
      def __init__(      self		,						lowerCamelCase=4_23_84		,						lowerCamelCase=10_24		,						lowerCamelCase=24		,						lowerCamelCase=16		,						lowerCamelCase=40_96		,						lowerCamelCase="gelu"		,						lowerCamelCase=0.1		,						lowerCamelCase=0.1		,						lowerCamelCase=10_24		,						lowerCamelCase=0.0_2		,						lowerCamelCase=1e-12		,						lowerCamelCase=True		,						lowerCamelCase=True		,						lowerCamelCase=0.0		,						lowerCamelCase=0.0		,						lowerCamelCase=1		,						lowerCamelCase=0		,						lowerCamelCase=2		,						**lowerCamelCase		,						):
          snake_case__    =					vocab_size
          snake_case__    =					max_position_embeddings
          snake_case__    =					hidden_size
          snake_case__    =					num_hidden_layers
          snake_case__    =					num_attention_heads
          snake_case__    =					intermediate_size
          snake_case__    =					hidden_act
          snake_case__    =					hidden_dropout_prob
          snake_case__    =					attention_probs_dropout_prob
          snake_case__    =					initializer_range
          snake_case__    =					layer_norm_eps
          snake_case__    =					scale_embedding
          snake_case__    =					use_cache
          snake_case__    =					layerdrop
          snake_case__    =					activation_dropout
          super().__init__(pad_token_id=lowerCamelCase		,						bos_token_id=lowerCamelCase		,						eos_token_id=lowerCamelCase		,						**lowerCamelCase			)



