code: 
  
  
  '''simple docstring'''
  
  
  
  import argparse
  
  import torch
  
  from transformers import NystromformerConfig, NystromformerForMaskedLM
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Optional[int]     ):
  					if "model" in orig_key:
  										__a    : Dict      					=						orig_key.replace('model.'		,   ''     )
  					if "norm1" in orig_key:
  										__a    : Any      					=						orig_key.replace('norm1'		,   'attention.output.LayerNorm'     )
  					if "norm2" in orig_key:
  										__a    : List[Any]      					=						orig_key.replace('norm2'		,   'output.LayerNorm'     )
  					if "norm" in orig_key:
  										__a    : Any      					=						orig_key.replace('norm'		,   'LayerNorm'     )
  					if "transformer" in orig_key:
  										__a    : str      					=						orig_key.split('.'     )[0].split('_'     )[-1]
  										__a    : Optional[int]      					=						orig_key.replace(F"""transformer_{layer_num}"""		,   F"""encoder.layer.{layer_num}"""     )
  					if "mha.attn" in orig_key:
  										__a    : Any      					=						orig_key.replace('mha.attn'		,   'attention.self'     )
  					if "mha" in orig_key:
  										__a    : Optional[int]      					=						orig_key.replace('mha'		,   'attention'     )
  					if "W_q" in orig_key:
  										__a    : Optional[Any]      					=						orig_key.replace('W_q'		,   'self.query'     )
  					if "W_k" in orig_key:
  										__a    : Dict      					=						orig_key.replace('W_k'		,   'self.key'     )
  					if "W_v" in orig_key:
  										__a    : Optional[int]      					=						orig_key.replace('W_v'		,   'self.value'     )
  					if "ff1" in orig_key:
  										__a    : Union[str, Any]      					=						orig_key.replace('ff1'		,   'intermediate.dense'     )
  					if "ff2" in orig_key:
  										__a    : Any      					=						orig_key.replace('ff2'		,   'output.dense'     )
  					if "ff" in orig_key:
  										__a    : Any      					=						orig_key.replace('ff'		,   'output.dense'     )
  					if "mlm_class" in orig_key:
  										__a    : int      					=						orig_key.replace('mlm.mlm_class'		,   'cls.predictions.decoder'     )
  					if "mlm" in orig_key:
  										__a    : str      					=						orig_key.replace('mlm'		,   'cls.predictions.transform'     )
  					if "cls" not in orig_key:
  										__a    : List[Any]      					=						'nystromformer.' + orig_key
  
  					return orig_key
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Optional[Any]		,   _SCREAMING_SNAKE_CASE   :     Dict     ):
  					for key in orig_state_dict.copy().keys():
  										__a    : int      					=						orig_state_dict.pop(_SCREAMING_SNAKE_CASE     )
  
  										if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
  															continue
  										else:
  															__a    : Dict      					=						val
  
  					__a    : Dict      					=						orig_state_dict['cls.predictions.decoder.bias']
  					__a    : List[str]      					=						(
  					    torch.arange(config.max_position_embeddings     ).expand((1, -1)     ) + 2
  					)
  
  					return orig_state_dict
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Tuple		,   _SCREAMING_SNAKE_CASE   :     Dict		,   _SCREAMING_SNAKE_CASE   :     int     ):
  					__a    : str      					=						torch.load(_SCREAMING_SNAKE_CASE		,   map_location='cpu'     )['model_state_dict']
  					__a    : List[Any]      					=						NystromformerConfig.from_json_file(_SCREAMING_SNAKE_CASE     )
  					__a    : str      					=						NystromformerForMaskedLM(_SCREAMING_SNAKE_CASE     )
  
  					__a    : List[str]      					=						convert_checkpoint_helper(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
  
  					model.load_state_dict(_SCREAMING_SNAKE_CASE     )
  					model.eval()
  					model.save_pretrained(_SCREAMING_SNAKE_CASE     )
  
  					print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""     )
  
  
  if __name__ == "__main__":
  	__lowercase			:		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.'
  	)
  	__lowercase			:		List[str] =			parser.parse_args()
  	convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  import torch
  
  from diffusers import KDPMaAncestralDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
  	A_			    =					(KDPMaAncestralDiscreteScheduler,)
  	A_			    =					10
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       **__a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Dict      					=						{
  						    'num_train_timesteps': 1100,
  						    'beta_start': 0.0001,
  						    'beta_end': 0.02,
  						    'beta_schedule': 'linear',
  						}
  
  						config.update(**__a							)
  						return config
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						for timesteps in [10, 50, 100, 1000]:
  											self.check_over_configs(num_train_timesteps=__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]		,       [0.0002, 0.002, 0.02]							):
  											self.check_over_configs(beta_start=__a		,       beta_end=__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						for schedule in ["linear", "scaled_linear"]:
  											self.check_over_configs(beta_schedule=__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if torch_device == "mps":
  											return
  						__a    : Dict      					=						self.scheduler_classes[0]
  						__a    : Tuple      					=						self.get_scheduler_config()
  						__a    : Optional[int]      					=						scheduler_class(**__a							)
  
  						scheduler.set_timesteps(self.num_inference_steps							)
  
  						__a    : int      					=						torch.manual_seed(0							)
  
  						__a    : Dict      					=						self.dummy_model()
  						__a    : List[Any]      					=						self.dummy_sample_deter * scheduler.init_noise_sigma
  						__a    : int      					=						sample.to(__a							)
  
  						for i, t in enumerate(scheduler.timesteps							):
  											__a    : Any      					=						scheduler.scale_model_input(__a		,       __a							)
  
  											__a    : int      					=						model(__a		,       __a							)
  
  											__a    : Dict      					=						scheduler.step(__a		,       __a		,       __a		,       generator=__a							)
  											__a    : int      					=						output.prev_sample
  
  						__a    : Tuple      					=						torch.sum(torch.abs(__a							)							)
  						__a    : List[str]      					=						torch.mean(torch.abs(__a							)							)
  
  						assert abs(result_sum.item() - 13849.3877							) < 1E-2
  						assert abs(result_mean.item() - 18.0331							) < 5E-3
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						for prediction_type in ["epsilon", "v_prediction"]:
  											self.check_over_configs(prediction_type=__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if torch_device == "mps":
  											return
  						__a    : Dict      					=						self.scheduler_classes[0]
  						__a    : Dict      					=						self.get_scheduler_config(prediction_type='v_prediction'							)
  						__a    : List[str]      					=						scheduler_class(**__a							)
  
  						scheduler.set_timesteps(self.num_inference_steps							)
  
  						__a    : Dict      					=						self.dummy_model()
  						__a    : List[str]      					=						self.dummy_sample_deter * scheduler.init_noise_sigma
  						__a    : List[str]      					=						sample.to(__a							)
  
  						__a    : Tuple      					=						torch.manual_seed(0							)
  
  						for i, t in enumerate(scheduler.timesteps							):
  											__a    : List[Any]      					=						scheduler.scale_model_input(__a		,       __a							)
  
  											__a    : Optional[int]      					=						model(__a		,       __a							)
  
  											__a    : Union[str, Any]      					=						scheduler.step(__a		,       __a		,       __a		,       generator=__a							)
  											__a    : Union[str, Any]      					=						output.prev_sample
  
  						__a    : Any      					=						torch.sum(torch.abs(__a							)							)
  						__a    : Optional[int]      					=						torch.mean(torch.abs(__a							)							)
  
  						assert abs(result_sum.item() - 328.9970							) < 1E-2
  						assert abs(result_mean.item() - 0.4284							) < 1E-3
  
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if torch_device == "mps":
  											return
  						__a    : List[str]      					=						self.scheduler_classes[0]
  						__a    : Union[str, Any]      					=						self.get_scheduler_config()
  						__a    : List[Any]      					=						scheduler_class(**__a							)
  
  						scheduler.set_timesteps(self.num_inference_steps		,       device=__a							)
  						__a    : Union[str, Any]      					=						torch.manual_seed(0							)
  
  						__a    : List[str]      					=						self.dummy_model()
  						__a    : Any      					=						self.dummy_sample_deter.to(__a							) * scheduler.init_noise_sigma
  
  						for t in scheduler.timesteps:
  											__a    : List[Any]      					=						scheduler.scale_model_input(__a		,       __a							)
  
  											__a    : str      					=						model(__a		,       __a							)
  
  											__a    : str      					=						scheduler.step(__a		,       __a		,       __a		,       generator=__a							)
  											__a    : Any      					=						output.prev_sample
  
  						__a    : Union[str, Any]      					=						torch.sum(torch.abs(__a							)							)
  						__a    : str      					=						torch.mean(torch.abs(__a							)							)
  
  						assert abs(result_sum.item() - 13849.3818							) < 1E-1
  						assert abs(result_mean.item() - 18.0331							) < 1E-3
  
  
  
  