




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)





# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

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


if TYPE_CHECKING:
      import tensorflow as tf






class      A     (						TensorFormatter[Mapping, "tf.Tensor", Mapping]						):
     """simple docstring"""

     def __init__(      self					:				int		,					A__					:				Tuple=None		,					**A__					:				Tuple     )			->    Dict:
           super().__init__(features=A__     )
           lowerCamelCase__ 					=			tf_tensor_kwargs
           import tensorflow as tf  # noqa: import tf at initialization




     def 	__snake_case				(      self					:				List[Any]		,					A__					:				List[str]     )			->    Dict:
           import tensorflow as tf

           if isinstance(A__		,					A__     ) and column:
                 if all(
                     isinstance(A__		,					tf.Tensor     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
                       return tf.stack(A__     )
                 elif all(
                     isinstance(A__		,					(tf.Tensor, tf.RaggedTensor)     ) and x.ndim == 1 and x.dtype == column[0].dtype
                     for x in column     ):
                       # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
                       return tf.ragged.stack(A__     )

           return column




     def 	__snake_case				(      self					:				Tuple		,					A__					:				Optional[int]     )			->    int:
           import tensorflow as tf

           if value is None:
                 return value

           lowerCamelCase__ 					=			{}

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

                 if isinstance(A__		,					PIL.Image.Image     ):
                       lowerCamelCase__ 					=			np.asarray(A__     )

           return tf.convert_to_tensor(A__		,					**{**default_dtype, **self.tf_tensor_kwargs}     )




     def 	__snake_case				(      self					:				Optional[int]		,					A__					:				Dict     )			->    List[str]:
           import tensorflow as tf

           # support for torch, tf, jax etc.
           if config.TORCH_AVAILABLE and "torch" in sys.modules:
                 import torch

                 if isinstance(A__		,					torch.Tensor     ):
                       return self._tensorize(data_struct.detach().cpu().numpy()[()]     )
           if hasattr(A__		,					'__array__'     ) and not isinstance(A__		,					tf.Tensor     ):
                 lowerCamelCase__ 					=			data_struct.__array__()
           # support for nested types like struct of list of struct
           if isinstance(A__		,					np.ndarray     ):
                 if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
                       return self._consolidate([self.recursive_tensorize(A__     ) for substruct in data_struct]     )
           elif isinstance(A__		,					(list, tuple)     ):
                 return self._consolidate([self.recursive_tensorize(A__     ) for substruct in data_struct]     )
           return self._tensorize(A__     )




     def 	__snake_case				(      self					:				List[Any]		,					A__					:				dict     )			->    List[str]:
           return map_nested(self._recursive_tensorize		,					A__		,					map_list=A__     )




     def 	__snake_case				(      self					:				Union[str, Any]		,					A__					:				pa.Table     )			->    Mapping:
           lowerCamelCase__ 					=			self.numpy_arrow_extractor().extract_row(A__     )
           lowerCamelCase__ 					=			self.python_features_decoder.decode_row(A__     )
           return self.recursive_tensorize(A__     )




     def 	__snake_case				(      self					:				Optional[Any]		,					A__					:				pa.Table     )			->    "tf.Tensor":
           lowerCamelCase__ 					=			self.numpy_arrow_extractor().extract_column(A__     )
           lowerCamelCase__ 					=			self.python_features_decoder.decode_column(A__		,					pa_table.column_names[0]     )
           lowerCamelCase__ 					=			self.recursive_tensorize(A__     )
           lowerCamelCase__ 					=			self._consolidate(A__     )
           return column




     def 	__snake_case				(      self					:				int		,					A__					:				pa.Table     )			->    Mapping:
           lowerCamelCase__ 					=			self.numpy_arrow_extractor().extract_batch(A__     )
           lowerCamelCase__ 					=			self.python_features_decoder.decode_batch(A__     )
           lowerCamelCase__ 					=			self.recursive_tensorize(A__     )
           for column_name in batch:
                 lowerCamelCase__ 					=			self._consolidate(batch[column_name]     )
           return batch