'''simple docstring'''


import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


def    _lowerCamelCase       ( lowerCamelCase_   :				Dict  ):



     """simple docstring"""





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

     return orig_key


def    _lowerCamelCase       ( lowerCamelCase_   :				Union[str, Any]      ,	lowerCamelCase_   :				Dict  ):



     """simple docstring"""





     for key in orig_state_dict.copy().keys():
          UpperCAmelCase_				:  Union[str, Any]  = orig_state_dict.pop(lowercase_  )

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

     UpperCAmelCase_				:  Optional[Any]  = orig_state_dict['cls.predictions.decoder.bias']
     UpperCAmelCase_				:  List[Any]  = torch.arange(lowercase_  ).expand((1, -1)  ) + 2

     return orig_state_dict


def    _lowerCamelCase       ( lowerCamelCase_   :				Any      ,	lowerCamelCase_   :				Tuple      ,	lowerCamelCase_   :				str  ):



     """simple docstring"""





     UpperCAmelCase_				:  Dict  = torch.load(lowercase_      ,	map_location='cpu'  )['model_state_dict']
     UpperCAmelCase_				:  List[Any]  = MraConfig.from_json_file(lowercase_  )
     UpperCAmelCase_				:  List[str]  = MraForMaskedLM(lowercase_  )

     UpperCAmelCase_				:  Optional[Any]  = convert_checkpoint_helper(config.max_position_embeddings      ,	lowercase_  )

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

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


if __name__ == "__main__":
 snake_case__	:     int      =    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.'''
 )
 snake_case__	:     Optional[Any]      =    parser.parse_args()
 convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)












class 			_lowerCamelCase    :

      """simple docstring"""

      def __init__(  self       :			Optional[Any]			,							snake_case       :			int					):
          __UpperCamelCase			=    data
          __UpperCamelCase			=    42
          __UpperCamelCase			=    42







def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						None:

    """simple docstring"""


    __UpperCamelCase			=    0
    __UpperCamelCase			=    x




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						None:

    """simple docstring"""


    __UpperCamelCase		,   __UpperCamelCase			=    find_set(lowercase_   ), find_set(lowercase_   )
    if x == y:
        return

    elif x.rank > y.rank:
        __UpperCamelCase			=    x
    else:
        __UpperCamelCase			=    y
        if x.rank == y.rank:
            y.rank += 1




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						Node:

    """simple docstring"""


    if x != x.parent:
        __UpperCamelCase			=    find_set(x.parent   )
    return x.parent




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						set:

    """simple docstring"""


    __UpperCamelCase			=    ({0, 1, 2}, {3, 4, 5})
    for s in sets:
        if node.data in s:
            return s
    __UpperCamelCase			=    F"{node.data} is not in {sets}"
    raise ValueError(lowercase_   )




def        __SCREAMING_SNAKE_CASE (	)							->						None:

    """simple docstring"""


    __UpperCamelCase			=    [Node(lowercase_   ) for i in range(6   )]
    for v in vertex:
        make_set(lowercase_   )

    union_set(vertex[0]       ,  vertex[1]   )
    union_set(vertex[1]       ,  vertex[2]   )
    union_set(vertex[3]       ,  vertex[4]   )
    union_set(vertex[3]       ,  vertex[5]   )

    for nodea in vertex:
        for nodea in vertex:
            if find_python_set(lowercase_   ).isdisjoint(find_python_set(lowercase_   )   ):
                assert find_set(lowercase_   ) != find_set(lowercase_   )
            else:
                assert find_set(lowercase_   ) == find_set(lowercase_   )


if __name__ == "__main__":
 test_disjoint_set()






