






import argparse

import torch

from transformers import MraConfig, MraForMaskedLM







def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			int:







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

     return orig_key







def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase    )				->			List[Any]:







     for key in orig_state_dict.copy().keys():
          UpperCamelCase__    :     Tuple        = orig_state_dict.pop(_A    )

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

     UpperCamelCase__    :     List[str]        = orig_state_dict["cls.predictions.decoder.bias"]
     UpperCamelCase__    :     Optional[int]        = torch.arange(_A    ).expand((1, -1)    ) + 2

     return orig_state_dict







def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase    )				->			str:
     UpperCamelCase__    :     int        = torch.load(_A      ,					map_location="cpu"    )["model_state_dict"]
     UpperCamelCase__    :     Union[str, Any]        = MraConfig.from_json_file(_A    )
     UpperCamelCase__    :     str        = MraForMaskedLM(_A    )

     UpperCamelCase__    :     str        = convert_checkpoint_helper(config.max_position_embeddings      ,					_A    )

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

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


if __name__ == "__main__":
 lowerCamelCase			:							Dict       =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			:							List[str]       =parser.parse_args()
 convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)







from __future__ import annotations







def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			list[int]:
     if not list_of_ints:
          return []

     if min(__lowerCAmelCase    ) < 0:
          raise ValueError("All numbers must be positive"    )

     UpperCamelCase__    :     Dict        =						max(len(bin(__lowerCAmelCase    )[2:]    ) for x in list_of_ints    )
     return _msd_radix_sort(__lowerCAmelCase      ,					__lowerCAmelCase    )







def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase    )				->			list[int]:
     if bit_position == 0 or len(__lowerCAmelCase    ) in [0, 1]:
          return list_of_ints

     UpperCamelCase__    :     Union[str, Any]        =						[]
     UpperCamelCase__    :     Dict        =						[]
     # Split numbers based on bit at bit_position from the right
     for number in list_of_ints:
          if (number >> (bit_position - 1)) & 1:
               # number has a one at bit bit_position
               ones.append(__lowerCAmelCase    )
          else:
               # number has a zero at bit bit_position
               zeros.append(__lowerCAmelCase    )

    # recursively split both lists further
     UpperCamelCase__    :     str        =						_msd_radix_sort(__lowerCAmelCase      ,					bit_position - 1    )
     UpperCamelCase__    :     Optional[Any]        =						_msd_radix_sort(__lowerCAmelCase      ,					bit_position - 1    )

     # recombine lists
     UpperCamelCase__    :     Optional[int]        =						zeros
     res.extend(__lowerCAmelCase    )

     return res







def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			Dict:
     UpperCamelCase__    :     Optional[int]        =						len(__lowerCAmelCase    )
     if not list_of_ints or length == 1:
          return

     if min(__lowerCAmelCase    ) < 0:
          raise ValueError("All numbers must be positive"    )

     UpperCamelCase__    :     int        =						max(len(bin(__lowerCAmelCase    )[2:]    ) for x in list_of_ints    )
     _msd_radix_sort_inplace(__lowerCAmelCase      ,					__lowerCAmelCase      ,					0      ,					__lowerCAmelCase    )







def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase    )				->			List[str]:
     if bit_position == 0 or end_index - begin_index <= 1:
          return

     bit_position -= 1

     UpperCamelCase__    :     Any        =						begin_index
     UpperCamelCase__    :     List[str]        =						end_index - 1
     while i <= j:
          UpperCamelCase__    :     Tuple        =						False
          if not (list_of_ints[i] >> bit_position) & 1:
               # found zero at the beginning
               i += 1
               UpperCamelCase__    :     Any        =						True
          if (list_of_ints[j] >> bit_position) & 1:
               # found one at the end
               j -= 1
               UpperCamelCase__    :     Dict        =						True

          if changed:
               continue

          UpperCamelCase__       ,  UpperCamelCase__    :     Any        =						list_of_ints[j], list_of_ints[i]
          j -= 1
          if j != i:
               i += 1

     _msd_radix_sort_inplace(__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase    )
     _msd_radix_sort_inplace(__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase    )


if __name__ == "__main__":
 import doctest

 doctest.testmod()