




import argparse

import torch

from transformers import MraConfig, MraForMaskedLM




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

    return orig_key




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		int ,  __UpperCamelCase    :		Tuple   )						->							List[Any]:
    for key in orig_state_dict.copy().keys():
        UpperCAmelCase_											=  orig_state_dict.pop(__UpperCamelCase   )

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

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

    return orig_state_dict




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		Any ,  __UpperCamelCase    :		Optional[Any] ,  __UpperCamelCase    :		Union[str, Any]   )						->							Tuple:
    UpperCAmelCase_											=  torch.load(__UpperCamelCase ,  map_location='''cpu'''   )['''model_state_dict''']
    UpperCAmelCase_											=  MraConfig.from_json_file(__UpperCamelCase   )
    UpperCAmelCase_											=  MraForMaskedLM(__UpperCamelCase   )

    UpperCAmelCase_											=  convert_checkpoint_helper(config.max_position_embeddings ,  __UpperCamelCase   )

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

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


if __name__ == "__main__":
 _lowerCamelCase    				=		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.'
 )
 _lowerCamelCase    				=		parser.parse_args()
 convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)











_lowerCamelCase    				=		10_00




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		float   )						->							float:
    return x * x




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		Optional[int] ,  __UpperCamelCase    :		float ,  __UpperCamelCase    :		float ,  __UpperCamelCase    :		int = 4   )						->							float:
    assert callable(
        __UpperCamelCase   ), f'the function(object) passed should be callable your input : {function}'
    assert isinstance(__UpperCamelCase ,  (float, int)   ), f'a should be float or integer your input : {a}'
    assert isinstance(function(__UpperCamelCase   ) ,  (float, int)   ), (
        "the function should return integer or float return type of your function, "
        f'{type(__UpperCamelCase   )}'
    )
    assert isinstance(__UpperCamelCase ,  (float, int)   ), f'b should be float or integer your input : {b}'
    assert (
        isinstance(__UpperCamelCase ,  __UpperCamelCase   ) and precision > 0
    ), f'precision should be positive integer your input : {precision}'

    # just applying the formula of simpson for approximate integration written in
    # mentioned article in first comment of this file and above this function

    UpperCAmelCase_											=  (b - a) / N_STEPS
    UpperCAmelCase_											=  function(__UpperCamelCase   ) + function(__UpperCamelCase   )

    for i in range(1 ,  __UpperCamelCase   ):
        UpperCAmelCase_											=  a + h * i
        result += function(__UpperCamelCase   ) * (4 if i % 2 else 2)

    result *= h / 3
    return round(__UpperCamelCase ,  __UpperCamelCase   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()





