
import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def 							A__       (    lowerCamelCase    )	->						Optional[int]:
				UpperCamelCase_:				List[str]						=    [
				    """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(__snake_case  ,     __snake_case    )







def 							A__       (    lowerCamelCase    )	->						Dict:
				UpperCamelCase_, UpperCamelCase_:				Dict						=    emb.weight.shape
				UpperCamelCase_:				Tuple						=    nn.Linear(__snake_case  ,     __snake_case  ,     bias=__snake_case    )
				UpperCamelCase_:				Optional[int]						=    emb.weight.data
				return lin_layer







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

				UpperCamelCase_:				Optional[int]						=    MaMaaaConfig(
				    vocab_size=__snake_case  ,     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"""  ,     )

				UpperCamelCase_:				List[str]						=    state_dict["""decoder.embed_tokens.weight"""]
				UpperCamelCase_:				List[str]						=    MaMaaaForConditionalGeneration(__snake_case    )
				model.model.load_state_dict(__snake_case  ,     strict=__snake_case    )
				UpperCamelCase_:				List[Any]						=    make_linear_from_emb(model.model.shared    )

				return model


if __name__ == "__main__":
							lowerCamelCase_    :			Dict    =	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_    :			Optional[Any]    =	parser.parse_args()
							lowerCamelCase_    :			Optional[Any]    =	convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
							model.save_pretrained(args.pytorch_dump_folder_path)














lowerCamelCase      :       List[Any]    =						{
    '''meter''': '''m''',
    '''kilometer''': '''km''',
    '''megametre''': '''Mm''',
    '''gigametre''': '''Gm''',
    '''terametre''': '''Tm''',
    '''petametre''': '''Pm''',
    '''exametre''': '''Em''',
    '''zettametre''': '''Zm''',
    '''yottametre''': '''Ym''',
}
# Exponent of the factor(meter)
lowerCamelCase      :       Union[str, Any]    =						{
    '''m''': 0,
    '''km''': 3,
    '''Mm''': 6,
    '''Gm''': 9,
    '''Tm''': 12,
    '''Pm''': 15,
    '''Em''': 18,
    '''Zm''': 21,
    '''Ym''': 24,
}





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       from_type.lower().strip("s"							)
     __lowerCAmelCase             =       to_type.lower().strip("s"							)

     __lowerCAmelCase             =       UNIT_SYMBOL.get(__snake_case       ,			__snake_case							)
     __lowerCAmelCase             =       UNIT_SYMBOL.get(__snake_case       ,			__snake_case							)

     if from_sanitized not in METRIC_CONVERSION:
          __lowerCAmelCase             =       (
              F"""Invalid 'from_type' value: {from_type!r}.\n"""
              F"""Conversion abbreviations are: {", ".join(__snake_case							)}"""
          )
          raise ValueError(__snake_case							)
     if to_sanitized not in METRIC_CONVERSION:
          __lowerCAmelCase             =       (
              F"""Invalid 'to_type' value: {to_type!r}.\n"""
              F"""Conversion abbreviations are: {", ".join(__snake_case							)}"""
          )
          raise ValueError(__snake_case							)
     __lowerCAmelCase             =       METRIC_CONVERSION[from_sanitized]
     __lowerCAmelCase             =       METRIC_CONVERSION[to_sanitized]
     __lowerCAmelCase             =       1

     if from_exponent > to_exponent:
          __lowerCAmelCase             =       from_exponent - to_exponent
     else:
          __lowerCAmelCase             =       -(to_exponent - from_exponent)

     return value * pow(10       ,			__snake_case							)


if __name__ == "__main__":
 from doctest import testmod

 testmod()






