






import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM

def 						snake_case      (UpperCAmelCase__						) ->       Optional[int]:
  if "model" in orig_key:
    UpperCamelCase_:      int					   =       orig_key.replace('model.'			,					''						)
  if "norm1" in orig_key:
    UpperCamelCase_:      str					   =       orig_key.replace('norm1'			,					'attention.output.LayerNorm'						)
  if "norm2" in orig_key:
    UpperCamelCase_:      Dict					   =       orig_key.replace('norm2'			,					'output.LayerNorm'						)
  if "norm" in orig_key:
    UpperCamelCase_:      Union[str, Any]					   =       orig_key.replace('norm'			,					'LayerNorm'						)
  if "transformer" in orig_key:
    UpperCamelCase_:      Dict					   =       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_:      int					   =       orig_key.replace('mha.attn'			,					'attention.self'						)
  if "mha" in orig_key:
    UpperCamelCase_:      str					   =       orig_key.replace('mha'			,					'attention'						)
  if "W_q" in orig_key:
    UpperCamelCase_:      Union[str, 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_:      Union[str, Any]					   =       orig_key.replace('W_v'			,					'self.value'						)
  if "ff1" in orig_key:
    UpperCamelCase_:      List[Any]					   =       orig_key.replace('ff1'			,					'intermediate.dense'						)
  if "ff2" in orig_key:
    UpperCamelCase_:      Tuple					   =       orig_key.replace('ff2'			,					'output.dense'						)
  if "ff" in orig_key:
    UpperCamelCase_:      Dict					   =       orig_key.replace('ff'			,					'output.dense'						)
  if "mlm_class" in orig_key:
    UpperCamelCase_:      List[Any]					   =       orig_key.replace('mlm.mlm_class'			,					'cls.predictions.decoder'						)
  if "mlm" in orig_key:
    UpperCamelCase_:      Tuple					   =       orig_key.replace('mlm'			,					'cls.predictions.transform'						)
  if "cls" not in orig_key:
    UpperCamelCase_:      int					   =       'nystromformer.' + orig_key

  return orig_key

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__						) ->       Union[str, Any]:
  for key in orig_state_dict.copy().keys():
    UpperCamelCase_:      List[str]					   =       orig_state_dict.pop(UpperCAmelCase__						)

    if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
      continue
    else:
      UpperCamelCase_:      Tuple					   =       val

  UpperCamelCase_:      List[Any]					   =       orig_state_dict['cls.predictions.decoder.bias']
  UpperCamelCase_:      List[str]					   =       (
      torch.arange(config.max_position_embeddings						).expand((1, -1)						) + 2
  )

  return orig_state_dict

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__						) ->       Optional[int]:
  UpperCamelCase_:      str					   =       torch.load(UpperCAmelCase__			,					map_location='cpu'						)['model_state_dict']
  UpperCamelCase_:      Union[str, Any]					   =       NystromformerConfig.from_json_file(UpperCAmelCase__						)
  UpperCamelCase_:      Union[str, Any]					   =       NystromformerForMaskedLM(UpperCAmelCase__						)

  UpperCamelCase_:      Optional[Any]					   =       convert_checkpoint_helper(UpperCAmelCase__			,					UpperCAmelCase__						)

  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__":
   A_	:	Dict				=				argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--pytorch_model_path', default=None, type=str, required=True, help='Path to Nystromformer pytorch checkpoint.'
   )
   parser.add_argument(
       '--config_file',
       default=None,
       type=str,
       required=True,
       help='The json file for Nystromformer model config.',
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   A_	:	List[Any]				=				parser.parse_args()
   convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)







# Lint as: python3

import hashlib
from typing import Union

def 						snake_case      (UpperCAmelCase__						) ->       bytes:
  if isinstance(UpperCAmelCase__			,					UpperCAmelCase__						):
    # Data already in bytes, returns as it as
    return hash_data
  elif isinstance(UpperCAmelCase__			,					UpperCAmelCase__						):
    # We keep the data as it as for it ot be later encoded to UTF-8
    # However replace `\\` with `/` for Windows compatibility
    UpperCamelCase_:      List[str]					   =       hash_data.replace('\\'			,					'/'						)
  elif isinstance(UpperCAmelCase__			,					UpperCAmelCase__						):
    UpperCamelCase_:      Union[str, Any]					   =       str(UpperCAmelCase__						)
  else:
    # If data is not of the required type, raise error
    raise InvalidKeyError(UpperCAmelCase__						)

  return hash_data.encode('utf-8'						)




class        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       def __init__(      self						,    _lowerCamelCase							):
         UpperCamelCase_:      int					   =       '\nFAILURE TO GENERATE DATASET: Invalid key type detected'
         UpperCamelCase_:      Dict					   =       f'''\nFound Key {hash_data} of type {type(_lowerCamelCase							)}'''
         UpperCamelCase_:      Any					   =       '\nKeys should be either str, int or bytes type'
         super().__init__(f'''{self.prefix}{self.err_msg}{self.suffix}'''							)







class        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       def __init__(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase=""							):
         UpperCamelCase_:      int					   =       key
         UpperCamelCase_:      Dict					   =       duplicate_key_indices
         UpperCamelCase_:      int					   =       fix_msg
         UpperCamelCase_:      List[Any]					   =       'Found multiple examples generated with the same key'
         if len(_lowerCamelCase							) <= 2_0:
           UpperCamelCase_:      Tuple					   =       f'''\nThe examples at index {', '.join(_lowerCamelCase							)} have the key {key}'''
         else:
           UpperCamelCase_:      Optional[int]					   =       f'''\nThe examples at index {', '.join(duplicate_key_indices[:2_0]							)}... ({len(_lowerCamelCase							) - 2_0} more) have the key {key}'''
         UpperCamelCase_:      List[Any]					   =       '\n' + fix_msg if fix_msg else ''
         super().__init__(f'''{self.prefix}{self.err_msg}{self.suffix}'''							)







class        _lowerCAmelCase:





       """simple docstring"""







       def __init__(      self						,    _lowerCamelCase							):
         UpperCamelCase_:      int					   =       hashlib.mda(_as_bytes(_lowerCamelCase							)							)





       def     _a					(      self						,    _lowerCamelCase							):
         UpperCamelCase_:      Optional[int]					   =       self._split_mda.copy()
         UpperCamelCase_:      str					   =       _as_bytes(_lowerCamelCase							)
         mda.update(_lowerCamelCase							)
         # Convert to integer with hexadecimal conversion
         return int(mda.hexdigest()						,    1_6							)