

import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Optional[int]    ):





    """simple docstring"""


    if "model" in orig_key:
        SCREAMING_SNAKE_CASE_													=  orig_key.replace('model.'	,				''    )
    if "norm1" in orig_key:
        SCREAMING_SNAKE_CASE_													=  orig_key.replace('norm1'	,				'attention.output.LayerNorm'    )
    if "norm2" in orig_key:
        SCREAMING_SNAKE_CASE_													=  orig_key.replace('norm2'	,				'output.LayerNorm'    )
    if "norm" in orig_key:
        SCREAMING_SNAKE_CASE_													=  orig_key.replace('norm'	,				'LayerNorm'    )
    if "transformer" in orig_key:
        SCREAMING_SNAKE_CASE_													=  orig_key.split('.'    )[0].split('_'    )[-1]
        SCREAMING_SNAKE_CASE_													=  orig_key.replace(f"""transformer_{layer_num}"""	,				f"""encoder.layer.{layer_num}"""    )
    if "mha.attn" in orig_key:
        SCREAMING_SNAKE_CASE_													=  orig_key.replace('mha.attn'	,				'attention.self'    )
    if "mha" in orig_key:
        SCREAMING_SNAKE_CASE_													=  orig_key.replace('mha'	,				'attention'    )
    if "W_q" in orig_key:
        SCREAMING_SNAKE_CASE_													=  orig_key.replace('W_q'	,				'self.query'    )
    if "W_k" in orig_key:
        SCREAMING_SNAKE_CASE_													=  orig_key.replace('W_k'	,				'self.key'    )
    if "W_v" in orig_key:
        SCREAMING_SNAKE_CASE_													=  orig_key.replace('W_v'	,				'self.value'    )
    if "ff.0" in orig_key:
        SCREAMING_SNAKE_CASE_													=  orig_key.replace('ff.0'	,				'intermediate.dense'    )
    if "ff.2" in orig_key:
        SCREAMING_SNAKE_CASE_													=  orig_key.replace('ff.2'	,				'output.dense'    )
    if "ff" in orig_key:
        SCREAMING_SNAKE_CASE_													=  orig_key.replace('ff'	,				'output.dense'    )
    if "mlm_class" in orig_key:
        SCREAMING_SNAKE_CASE_													=  orig_key.replace('mlm.mlm_class'	,				'cls.predictions.decoder'    )
    if "mlm" in orig_key:
        SCREAMING_SNAKE_CASE_													=  orig_key.replace('mlm'	,				'cls.predictions.transform'    )
    if "backbone.backbone.encoders" in orig_key:
        SCREAMING_SNAKE_CASE_													=  orig_key.replace('backbone.backbone.encoders'	,				'encoder.layer'    )
    if "cls" not in orig_key:
        SCREAMING_SNAKE_CASE_													=  'mra.' + orig_key

    return orig_key


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Dict	,				_SCREAMING_SNAKE_CASE   :						Optional[Any]    ):





    """simple docstring"""


    for key in orig_state_dict.copy().keys():
        SCREAMING_SNAKE_CASE_													=  orig_state_dict.pop(_SCREAMING_SNAKE_CASE    )

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            SCREAMING_SNAKE_CASE_													=  val

    SCREAMING_SNAKE_CASE_													=  orig_state_dict['cls.predictions.decoder.bias']
    SCREAMING_SNAKE_CASE_													=  torch.arange(_SCREAMING_SNAKE_CASE    ).expand((1, -1)    ) + 2

    return orig_state_dict


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						List[Any]	,				_SCREAMING_SNAKE_CASE   :						List[Any]	,				_SCREAMING_SNAKE_CASE   :						List[str]    ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  torch.load(_SCREAMING_SNAKE_CASE	,				map_location='cpu'    )['model_state_dict']
    SCREAMING_SNAKE_CASE_													=  MraConfig.from_json_file(_SCREAMING_SNAKE_CASE    )
    SCREAMING_SNAKE_CASE_													=  MraForMaskedLM(_SCREAMING_SNAKE_CASE    )

    SCREAMING_SNAKE_CASE_													=  convert_checkpoint_helper(config.max_position_embeddings	,				_SCREAMING_SNAKE_CASE    )

    print(model.load_state_dict(_SCREAMING_SNAKE_CASE    )    )
    model.eval()
    model.save_pretrained(_SCREAMING_SNAKE_CASE    )

    print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""    )


if __name__ == "__main__":
       UpperCamelCase__					: Tuple								=      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--pytorch_model_path", default=None, type=str, required=True, help="Path to Mra pytorch checkpoint."
       )
       parser.add_argument(
           "--config_file",
           default=None,
           type=str,
           required=True,
           help="The json file for Mra model config.",
       )
       parser.add_argument(
           "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
       )
       UpperCamelCase__					: Optional[int]								=      parser.parse_args()
       convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int    ):





    """simple docstring"""


    if power < 0:
        return -1
    base %= mod
    SCREAMING_SNAKE_CASE_													=  1

    while power > 0:
        if power & 1:
            SCREAMING_SNAKE_CASE_													=  (result * base) % mod
        SCREAMING_SNAKE_CASE_													=  power >> 1
        SCREAMING_SNAKE_CASE_													=  (base * base) % mod

    return result


def    _UpperCAmelCase    (					):





    """simple docstring"""


    print(modular_exponential(3	,				200	,				13    )    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       main()


