




import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM

def      lowerCAmelCase_     (					lowerCamelCase:    Union[str, Any]  )   ->     List[str]:


      '''simple docstring'''




      if "model" in orig_key:
            lowerCamelCase__ 					=			orig_key.replace('model.'       ,			''  )
      if "norm1" in orig_key:
            lowerCamelCase__ 					=			orig_key.replace('norm1'       ,			'attention.output.LayerNorm'  )
      if "norm2" in orig_key:
            lowerCamelCase__ 					=			orig_key.replace('norm2'       ,			'output.LayerNorm'  )
      if "norm" in orig_key:
            lowerCamelCase__ 					=			orig_key.replace('norm'       ,			'LayerNorm'  )
      if "transformer" in orig_key:
            lowerCamelCase__ 					=			orig_key.split('.'  )[0].split('_'  )[-1]
            lowerCamelCase__ 					=			orig_key.replace(f"""transformer_{layer_num}"""       ,			f"""encoder.layer.{layer_num}"""  )
      if "mha.attn" in orig_key:
            lowerCamelCase__ 					=			orig_key.replace('mha.attn'       ,			'attention.self'  )
      if "mha" in orig_key:
            lowerCamelCase__ 					=			orig_key.replace('mha'       ,			'attention'  )
      if "W_q" in orig_key:
            lowerCamelCase__ 					=			orig_key.replace('W_q'       ,			'self.query'  )
      if "W_k" in orig_key:
            lowerCamelCase__ 					=			orig_key.replace('W_k'       ,			'self.key'  )
      if "W_v" in orig_key:
            lowerCamelCase__ 					=			orig_key.replace('W_v'       ,			'self.value'  )
      if "ff1" in orig_key:
            lowerCamelCase__ 					=			orig_key.replace('ff1'       ,			'intermediate.dense'  )
      if "ff2" in orig_key:
            lowerCamelCase__ 					=			orig_key.replace('ff2'       ,			'output.dense'  )
      if "ff" in orig_key:
            lowerCamelCase__ 					=			orig_key.replace('ff'       ,			'output.dense'  )
      if "mlm_class" in orig_key:
            lowerCamelCase__ 					=			orig_key.replace('mlm.mlm_class'       ,			'cls.predictions.decoder'  )
      if "mlm" in orig_key:
            lowerCamelCase__ 					=			orig_key.replace('mlm'       ,			'cls.predictions.transform'  )
      if "cls" not in orig_key:
            lowerCamelCase__ 					=			'nystromformer.' + orig_key

      return orig_key

def      lowerCAmelCase_     (					lowerCamelCase:    Optional[int]       ,			lowerCamelCase:    Optional[Any]  )   ->     Any:


      '''simple docstring'''




      for key in orig_state_dict.copy().keys():
            lowerCamelCase__ 					=			orig_state_dict.pop(lowerCamelCase  )

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

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

      return orig_state_dict

def      lowerCAmelCase_     (					lowerCamelCase:    Tuple       ,			lowerCamelCase:    int       ,			lowerCamelCase:    List[Any]  )   ->     str:


      '''simple docstring'''




      lowerCamelCase__ 					=			torch.load(lowerCamelCase       ,			map_location='cpu'  )['model_state_dict']
      lowerCamelCase__ 					=			NystromformerConfig.from_json_file(lowerCamelCase  )
      lowerCamelCase__ 					=			NystromformerForMaskedLM(lowerCamelCase  )

      lowerCamelCase__ 					=			convert_checkpoint_helper(lowerCamelCase       ,			lowerCamelCase  )

      model.load_state_dict(lowerCamelCase  )
      model.eval()
      model.save_pretrained(lowerCamelCase  )

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


if __name__ == "__main__":
      __SCREAMING_SNAKE_CASE      :				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."""
      )
      __SCREAMING_SNAKE_CASE      :				List[Any]				      = parser.parse_args()
      convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





def      lowerCAmelCase_     (					lowerCamelCase:    list       ,			lowerCamelCase:    int       ,			lowerCamelCase:    int  )   ->     None:


      '''simple docstring'''




      if right <= left:
            return
      lowerCamelCase__ 					=			lowerCamelCase__ 					=			left
      lowerCamelCase__ 					=			right
      lowerCamelCase__ 					=			sorting[left]
      while i <= b:
            if sorting[i] < pivot:
                  lowerCamelCase__					,						lowerCamelCase__ 					=			sorting[i], sorting[a]
                  a += 1
                  i += 1
            elif sorting[i] > pivot:
                  lowerCamelCase__					,						lowerCamelCase__ 					=			sorting[i], sorting[b]
                  b -= 1
            else:
                  i += 1
      quick_sort_apartition(lowerCamelCase       ,			lowerCamelCase       ,			a - 1  )
      quick_sort_apartition(lowerCamelCase       ,			b + 1       ,			lowerCamelCase  )

def      lowerCAmelCase_     (					lowerCamelCase:    list       ,			lowerCamelCase:    int       ,			lowerCamelCase:    int  )   ->     None:


      '''simple docstring'''




      if left < right:
            lowerCamelCase__ 					=			lomuto_partition(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase  )
            quick_sort_lomuto_partition(lowerCamelCase       ,			lowerCamelCase       ,			pivot_index - 1  )
            quick_sort_lomuto_partition(lowerCamelCase       ,			pivot_index + 1       ,			lowerCamelCase  )

def      lowerCAmelCase_     (					lowerCamelCase:    list       ,			lowerCamelCase:    int       ,			lowerCamelCase:    int  )   ->     int:


      '''simple docstring'''




      lowerCamelCase__ 					=			sorting[right]
      lowerCamelCase__ 					=			left
      for i in range(lowerCamelCase       ,			lowerCamelCase  ):
            if sorting[i] < pivot:
                  lowerCamelCase__					,						lowerCamelCase__ 					=			sorting[i], sorting[store_index]
                  store_index += 1
      lowerCamelCase__					,						lowerCamelCase__ 					=			sorting[store_index], sorting[right]
      return store_index

def      lowerCAmelCase_     (					lowerCamelCase:    list  )   ->     list:


      '''simple docstring'''




      if len(lowerCamelCase  ) <= 1:
            return sorting
      return (
          three_way_radix_quicksort([i for i in sorting if i < sorting[0]]  )
          + [i for i in sorting if i == sorting[0]]
          + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]  )
      )


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)

      __SCREAMING_SNAKE_CASE      :				Optional[int]				      =input("""Enter numbers separated by a comma:\n""").strip()
      __SCREAMING_SNAKE_CASE      :				str				      =[int(item) for item in user_input.split(""",""")]
      quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
      print(unsorted)