

import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM
def   _lowerCAmelCase(						__A      ):
  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 "ff1" in orig_key:
    UpperCAmelCase          =	orig_key.replace("ff1" ,							"intermediate.dense"      )
  if "ff2" in orig_key:
    UpperCAmelCase          =	orig_key.replace("ff2" ,							"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 "cls" not in orig_key:
    UpperCAmelCase          =	"nystromformer." + orig_key

  return orig_key
def   _lowerCAmelCase(						__A ,							__A      ):
  for key in orig_state_dict.copy().keys():
    UpperCAmelCase          =	orig_state_dict.pop(__A      )

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

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

  return orig_state_dict
def   _lowerCAmelCase(						__A ,							__A ,							__A      ):
  UpperCAmelCase          =	torch.load(__A ,							map_location="cpu"      )["model_state_dict"]
  UpperCAmelCase          =	NystromformerConfig.from_json_file(__A      )
  UpperCAmelCase          =	NystromformerForMaskedLM(__A      )

  UpperCAmelCase          =	convert_checkpoint_helper(__A ,							__A      )

  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__							=			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."
      )
      lowerCAmelCase__							=			parser.parse_args()
      convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter






class     __magic_name__     (							TensorFormatter[Mapping, np.ndarray, Mapping]     ):


 def __init__(    self		:			Optional[int]      ,    lowerCAmelCase__		:			str=None      ,    **lowerCAmelCase__		:			int     )     ->   Optional[Any]:
   super().__init__(features=lowerCAmelCase__     )
   UpperCAmelCase          =	np_array_kwargs


 def _UpperCamelCase						(    self		:			Any      ,    lowerCAmelCase__		:			Dict     )     ->   Union[str, Any]:
   if isinstance(lowerCAmelCase__      ,    lowerCAmelCase__     ):
     if column and all(
         isinstance(lowerCAmelCase__      ,    np.ndarray     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
       return np.stack(lowerCAmelCase__     )
     else:
       # don't use np.array(column, dtype=object)
       # since it fails in certain cases
       # see https://stackoverflow.com/q/51005699
       UpperCAmelCase          =	np.empty(len(lowerCAmelCase__     )      ,    dtype=lowerCAmelCase__     )
       UpperCAmelCase          =	column
       return out
   return column


 def _UpperCamelCase						(    self		:			Optional[Any]      ,    lowerCAmelCase__		:			Union[str, Any]     )     ->   List[Any]:
   if isinstance(lowerCAmelCase__      ,    (str, bytes, type(lowerCAmelCase__     ))     ):
     return value
   elif isinstance(lowerCAmelCase__      ,    (np.character, np.ndarray)     ) and np.issubdtype(value.dtype      ,    np.character     ):
     return value
   elif isinstance(lowerCAmelCase__      ,    np.number     ):
     return value

   UpperCAmelCase          =	{}

   if isinstance(lowerCAmelCase__      ,    np.ndarray     ) and np.issubdtype(value.dtype      ,    np.integer     ):
     UpperCAmelCase          =	{"dtype": np.intaa}
   elif isinstance(lowerCAmelCase__      ,    np.ndarray     ) and np.issubdtype(value.dtype      ,    np.floating     ):
     UpperCAmelCase          =	{"dtype": np.floataa}
   elif config.PIL_AVAILABLE and "PIL" in sys.modules:
     import PIL.Image

     if isinstance(lowerCAmelCase__      ,    PIL.Image.Image     ):
       return np.asarray(lowerCAmelCase__      ,    **self.np_array_kwargs     )

   return np.asarray(lowerCAmelCase__      ,    **{**default_dtype, **self.np_array_kwargs}     )


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			str     )     ->   Optional[int]:
   # support for torch, tf, jax etc.
   if config.TORCH_AVAILABLE and "torch" in sys.modules:
     import torch

     if isinstance(lowerCAmelCase__      ,    torch.Tensor     ):
       return self._tensorize(data_struct.detach().cpu().numpy()[()]     )
   if hasattr(lowerCAmelCase__      ,    "__array__"     ) and not isinstance(lowerCAmelCase__      ,    (np.ndarray, np.character, np.number)     ):
     UpperCAmelCase          =	data_struct.__array__()
   # support for nested types like struct of list of struct
   if isinstance(lowerCAmelCase__      ,    np.ndarray     ):
     if data_struct.dtype == object:
       return self._consolidate([self.recursive_tensorize(lowerCAmelCase__     ) for substruct in data_struct]     )
   if isinstance(lowerCAmelCase__      ,    (list, tuple)     ):
     return self._consolidate([self.recursive_tensorize(lowerCAmelCase__     ) for substruct in data_struct]     )
   return self._tensorize(lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			Union[str, Any]      ,    lowerCAmelCase__		:			dict     )     ->   Union[str, Any]:
   return map_nested(self._recursive_tensorize      ,    lowerCAmelCase__      ,    map_list=lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			int      ,    lowerCAmelCase__		:			pa.Table     )     ->   Mapping:
   UpperCAmelCase          =	self.numpy_arrow_extractor().extract_row(lowerCAmelCase__     )
   UpperCAmelCase          =	self.python_features_decoder.decode_row(lowerCAmelCase__     )
   return self.recursive_tensorize(lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			List[str]      ,    lowerCAmelCase__		:			pa.Table     )     ->   np.ndarray:
   UpperCAmelCase          =	self.numpy_arrow_extractor().extract_column(lowerCAmelCase__     )
   UpperCAmelCase          =	self.python_features_decoder.decode_column(lowerCAmelCase__      ,    pa_table.column_names[0]     )
   UpperCAmelCase          =	self.recursive_tensorize(lowerCAmelCase__     )
   UpperCAmelCase          =	self._consolidate(lowerCAmelCase__     )
   return column


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			pa.Table     )     ->   Mapping:
   UpperCAmelCase          =	self.numpy_arrow_extractor().extract_batch(lowerCAmelCase__     )
   UpperCAmelCase          =	self.python_features_decoder.decode_batch(lowerCAmelCase__     )
   UpperCAmelCase          =	self.recursive_tensorize(lowerCAmelCase__     )
   for column_name in batch:
     UpperCAmelCase          =	self._consolidate(batch[column_name]     )
   return batch



