code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import NystromformerConfig, NystromformerForMaskedLM
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if "model" in orig_key:
  				_snake_case			 = orig_key.replace("model."		,							""						)
  		if "norm1" in orig_key:
  				_snake_case			 = orig_key.replace("norm1"		,							"attention.output.LayerNorm"						)
  		if "norm2" in orig_key:
  				_snake_case			 = orig_key.replace("norm2"		,							"output.LayerNorm"						)
  		if "norm" in orig_key:
  				_snake_case			 = orig_key.replace("norm"		,							"LayerNorm"						)
  		if "transformer" in orig_key:
  				_snake_case			 = orig_key.split("."						)[0].split("_"						)[-1]
  				_snake_case			 = orig_key.replace(f'''transformer_{layer_num}'''		,							f'''encoder.layer.{layer_num}'''						)
  		if "mha.attn" in orig_key:
  				_snake_case			 = orig_key.replace("mha.attn"		,							"attention.self"						)
  		if "mha" in orig_key:
  				_snake_case			 = orig_key.replace("mha"		,							"attention"						)
  		if "W_q" in orig_key:
  				_snake_case			 = orig_key.replace("W_q"		,							"self.query"						)
  		if "W_k" in orig_key:
  				_snake_case			 = orig_key.replace("W_k"		,							"self.key"						)
  		if "W_v" in orig_key:
  				_snake_case			 = orig_key.replace("W_v"		,							"self.value"						)
  		if "ff1" in orig_key:
  				_snake_case			 = orig_key.replace("ff1"		,							"intermediate.dense"						)
  		if "ff2" in orig_key:
  				_snake_case			 = orig_key.replace("ff2"		,							"output.dense"						)
  		if "ff" in orig_key:
  				_snake_case			 = orig_key.replace("ff"		,							"output.dense"						)
  		if "mlm_class" in orig_key:
  				_snake_case			 = orig_key.replace("mlm.mlm_class"		,							"cls.predictions.decoder"						)
  		if "mlm" in orig_key:
  				_snake_case			 = orig_key.replace("mlm"		,							"cls.predictions.transform"						)
  		if "cls" not in orig_key:
  				_snake_case			 = "nystromformer." + orig_key
  
  		return orig_key
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		for key in orig_state_dict.copy().keys():
  				_snake_case			 = orig_state_dict.pop(lowercase_						)
  
  				if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
  						continue
  				else:
  						_snake_case			 = val
  
  		_snake_case			 = orig_state_dict["cls.predictions.decoder.bias"]
  		_snake_case			 = (
  		    torch.arange(config.max_position_embeddings						).expand((1, -1)						) + 2
  		)
  
  		return orig_state_dict
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = torch.load(lowercase_		,							map_location="cpu"						)["model_state_dict"]
  		_snake_case			 = NystromformerConfig.from_json_file(lowercase_						)
  		_snake_case			 = NystromformerForMaskedLM(lowercase_						)
  
  		_snake_case			 = convert_checkpoint_helper(lowercase_		,							lowercase_						)
  
  		model.load_state_dict(lowercase_						)
  		model.eval()
  		model.save_pretrained(lowercase_						)
  
  		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 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							:		str										=						parser.parse_args()
  						convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
  
  
  
  
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import warnings
  
  from ...utils import logging
  from .image_processing_vilt import ViltImageProcessor
  
  
  _lowerCamelCase							:		Optional[Any]										=						logging.get_logger(__name__)
  
  
  
  
  
  class   lowerCamelCase_							( UpperCAmelCase_							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def __init__(					self,						*__UpperCamelCase,						**__UpperCamelCase				):
  							warnings.warn(
  							    "The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
  							    " use ViltImageProcessor instead.",						__UpperCamelCase,						)
  							super().__init__(*__UpperCamelCase,						**__UpperCamelCase				)
  
  
  
  
  
  