code: 
  
  
  """simple docstring"""
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import NystromformerConfig, NystromformerForMaskedLM
  
  
  
  
  
  def 			a_   (  lowercase__   :Optional[int]    ):
  
  
  
  
  
  
  
  			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 			a_   (  lowercase__   :int, lowercase__   :str    ):
  
  
  
  
  
  
  
  			for key in orig_state_dict.copy().keys():
  						__lowerCamelCase        =			orig_state_dict.pop(lowercase__    )
  
  						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 			a_   (  lowercase__   :Union[str, Any], lowercase__   :Tuple, lowercase__   :List[Any]    ):
  			__lowerCamelCase        =			torch.load(lowercase__, map_location="""cpu"""    )['model_state_dict']
  			__lowerCamelCase        =			NystromformerConfig.from_json_file(lowercase__    )
  			__lowerCamelCase        =			NystromformerForMaskedLM(lowercase__    )
  
  			__lowerCamelCase        =			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__":
  					__magic_name__	:   List[str]          =							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.'
  					)
  					__magic_name__	:   int          =							parser.parse_args()
  					convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import argparse
  import logging
  import sys
  from unittest.mock import patch
  
  import run_glue_with_pabee
  
  from transformers.testing_utils import TestCasePlus
  
  
  logging.basicConfig(level=logging.DEBUG)
  
  __magic_name__	:   Optional[int]          =							logging.getLogger()
  
  
  
  
  
  def 			a_   (  ):
  			__lowerCamelCase        =			argparse.ArgumentParser()
  			parser.add_argument("""-f"""    )
  			__lowerCamelCase        =			parser.parse_args()
  			return args.f
  
  
  
  
  class  __snake_case      (lowerCamelCase     ):
  
  
  
  
  							def      __a      (    self:						Optional[int]      ):
  										__lowerCamelCase        =			logging.StreamHandler(sys.stdout      )
  										logger.addHandler(A_      )
  
  										__lowerCamelCase        =			self.get_auto_remove_tmp_dir()
  										__lowerCamelCase        =			f'\n            run_glue_with_pabee.py\n            --model_type albert\n            --model_name_or_path albert-base-v2\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --task_name mrpc\n            --do_train\n            --do_eval\n            --per_gpu_train_batch_size=2\n            --per_gpu_eval_batch_size=1\n            --learning_rate=2e-5\n            --max_steps=50\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()
  
  										with patch.object(A_    ,       """argv"""    ,       A_      ):
  													__lowerCamelCase        =			run_glue_with_pabee.main()
  													for value in result.values():
  																self.assertGreaterEqual(A_    ,       0.75      )
  
  
  
  
  