

import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class A__     (			A			):



 """simple docstring"""







 _lowercase  :       Optional[Any]  			=    (LMSDiscreteScheduler,)
 _lowercase  :       Optional[int]  			=    10

 def  __magic_name__							(		self			:						Dict  ,    **A_			:						Tuple							):






      '''simple docstring'''




      _lowerCAmelCase :       Any	   =     {
          "num_train_timesteps": 1_1_0_0,
          "beta_start": 0.0001,
          "beta_end": 0.02,
          "beta_schedule": "linear",
      }

      config.update(**A_							)
      return config

 def  __magic_name__							(		self			:						Tuple							):






      '''simple docstring'''




      for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
           self.check_over_configs(num_train_timesteps=A_							)

 def  __magic_name__							(		self			:						Union[str, Any]							):






      '''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  __magic_name__							(		self			:						str							):






      '''simple docstring'''




      for schedule in ["linear", "scaled_linear"]:
           self.check_over_configs(beta_schedule=A_							)

 def  __magic_name__							(		self			:						List[str]							):






      '''simple docstring'''




      for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=A_							)

 def  __magic_name__							(		self			:						str							):






      '''simple docstring'''




      for t in [0, 5_0_0, 8_0_0]:
           self.check_over_forward(time_step=A_							)

 def  __magic_name__							(		self			:						int							):






      '''simple docstring'''




      _lowerCAmelCase :       Optional[Any]	   =     self.scheduler_classes[0]
      _lowerCAmelCase :       List[Any]	   =     self.get_scheduler_config()
      _lowerCAmelCase :       str	   =     scheduler_class(**A_							)

      scheduler.set_timesteps(self.num_inference_steps							)

      _lowerCAmelCase :       Union[str, Any]	   =     self.dummy_model()
      _lowerCAmelCase :       Tuple	   =     self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(scheduler.timesteps							):
           _lowerCAmelCase :       int	   =     scheduler.scale_model_input(A_  ,    A_							)

           _lowerCAmelCase :       Optional[Any]	   =     model(A_  ,    A_							)

           _lowerCAmelCase :       Optional[int]	   =     scheduler.step(A_  ,    A_  ,    A_							)
           _lowerCAmelCase :       Dict	   =     output.prev_sample

      _lowerCAmelCase :       Optional[int]	   =     torch.sum(torch.abs(A_							)							)
      _lowerCAmelCase :       Union[str, Any]	   =     torch.mean(torch.abs(A_							)							)

      assert abs(result_sum.item() - 1006.388							) < 1E-2
      assert abs(result_mean.item() - 1.31							) < 1E-3

 def  __magic_name__							(		self			:						List[str]							):






      '''simple docstring'''




      _lowerCAmelCase :       List[Any]	   =     self.scheduler_classes[0]
      _lowerCAmelCase :       List[str]	   =     self.get_scheduler_config(prediction_type="v_prediction"							)
      _lowerCAmelCase :       Optional[int]	   =     scheduler_class(**A_							)

      scheduler.set_timesteps(self.num_inference_steps							)

      _lowerCAmelCase :       Optional[Any]	   =     self.dummy_model()
      _lowerCAmelCase :       Any	   =     self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(scheduler.timesteps							):
           _lowerCAmelCase :       Union[str, Any]	   =     scheduler.scale_model_input(A_  ,    A_							)

           _lowerCAmelCase :       List[str]	   =     model(A_  ,    A_							)

           _lowerCAmelCase :       Optional[Any]	   =     scheduler.step(A_  ,    A_  ,    A_							)
           _lowerCAmelCase :       Dict	   =     output.prev_sample

      _lowerCAmelCase :       List[Any]	   =     torch.sum(torch.abs(A_							)							)
      _lowerCAmelCase :       Any	   =     torch.mean(torch.abs(A_							)							)

      assert abs(result_sum.item() - 0.0017							) < 1E-2
      assert abs(result_mean.item() - 2.2676E-06							) < 1E-3

 def  __magic_name__							(		self			:						Optional[Any]							):






      '''simple docstring'''




      _lowerCAmelCase :       str	   =     self.scheduler_classes[0]
      _lowerCAmelCase :       Optional[int]	   =     self.get_scheduler_config()
      _lowerCAmelCase :       Tuple	   =     scheduler_class(**A_							)

      scheduler.set_timesteps(self.num_inference_steps  ,    device=A_							)

      _lowerCAmelCase :       Union[str, Any]	   =     self.dummy_model()
      _lowerCAmelCase :       str	   =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      _lowerCAmelCase :       Dict	   =     sample.to(A_							)

      for i, t in enumerate(scheduler.timesteps							):
           _lowerCAmelCase :       List[str]	   =     scheduler.scale_model_input(A_  ,    A_							)

           _lowerCAmelCase :       Any	   =     model(A_  ,    A_							)

           _lowerCAmelCase :       int	   =     scheduler.step(A_  ,    A_  ,    A_							)
           _lowerCAmelCase :       Tuple	   =     output.prev_sample

      _lowerCAmelCase :       Optional[int]	   =     torch.sum(torch.abs(A_							)							)
      _lowerCAmelCase :       Any	   =     torch.mean(torch.abs(A_							)							)

      assert abs(result_sum.item() - 1006.388							) < 1E-2
      assert abs(result_mean.item() - 1.31							) < 1E-3


 def  __magic_name__							(		self			:						Union[str, Any]							):






      '''simple docstring'''




      _lowerCAmelCase :       Dict	   =     self.scheduler_classes[0]
      _lowerCAmelCase :       Dict	   =     self.get_scheduler_config()
      _lowerCAmelCase :       Optional[Any]	   =     scheduler_class(**A_  ,    use_karras_sigmas=A_							)

      scheduler.set_timesteps(self.num_inference_steps  ,    device=A_							)

      _lowerCAmelCase :       Dict	   =     self.dummy_model()
      _lowerCAmelCase :       str	   =     self.dummy_sample_deter.to(A_							) * scheduler.init_noise_sigma
      _lowerCAmelCase :       Optional[Any]	   =     sample.to(A_							)

      for t in scheduler.timesteps:
           _lowerCAmelCase :       Tuple	   =     scheduler.scale_model_input(A_  ,    A_							)

           _lowerCAmelCase :       Optional[Any]	   =     model(A_  ,    A_							)

           _lowerCAmelCase :       Dict	   =     scheduler.step(A_  ,    A_  ,    A_							)
           _lowerCAmelCase :       Dict	   =     output.prev_sample

      _lowerCAmelCase :       List[Any]	   =     torch.sum(torch.abs(A_							)							)
      _lowerCAmelCase :       int	   =     torch.mean(torch.abs(A_							)							)

      assert abs(result_sum.item() - 3812.9927							) < 2E-2
      assert abs(result_mean.item() - 4.9648							) < 1E-3









import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Tuple:

     """simple docstring"""







     if "model" in orig_key:
          _lowerCAmelCase :       Tuple	   =     orig_key.replace("model."		,				""					)
     if "norm1" in orig_key:
          _lowerCAmelCase :       Optional[Any]	   =     orig_key.replace("norm1"		,				"attention.output.LayerNorm"					)
     if "norm2" in orig_key:
          _lowerCAmelCase :       Optional[int]	   =     orig_key.replace("norm2"		,				"output.LayerNorm"					)
     if "norm" in orig_key:
          _lowerCAmelCase :       Any	   =     orig_key.replace("norm"		,				"LayerNorm"					)
     if "transformer" in orig_key:
          _lowerCAmelCase :       str	   =     orig_key.split("."					)[0].split("_"					)[-1]
          _lowerCAmelCase :       Any	   =     orig_key.replace(f'''transformer_{layer_num}'''		,				f'''encoder.layer.{layer_num}'''					)
     if "mha.attn" in orig_key:
          _lowerCAmelCase :       int	   =     orig_key.replace("mha.attn"		,				"attention.self"					)
     if "mha" in orig_key:
          _lowerCAmelCase :       Dict	   =     orig_key.replace("mha"		,				"attention"					)
     if "W_q" in orig_key:
          _lowerCAmelCase :       Union[str, Any]	   =     orig_key.replace("W_q"		,				"self.query"					)
     if "W_k" in orig_key:
          _lowerCAmelCase :       Tuple	   =     orig_key.replace("W_k"		,				"self.key"					)
     if "W_v" in orig_key:
          _lowerCAmelCase :       Optional[int]	   =     orig_key.replace("W_v"		,				"self.value"					)
     if "ff1" in orig_key:
          _lowerCAmelCase :       Tuple	   =     orig_key.replace("ff1"		,				"intermediate.dense"					)
     if "ff2" in orig_key:
          _lowerCAmelCase :       List[Any]	   =     orig_key.replace("ff2"		,				"output.dense"					)
     if "ff" in orig_key:
          _lowerCAmelCase :       str	   =     orig_key.replace("ff"		,				"output.dense"					)
     if "mlm_class" in orig_key:
          _lowerCAmelCase :       int	   =     orig_key.replace("mlm.mlm_class"		,				"cls.predictions.decoder"					)
     if "mlm" in orig_key:
          _lowerCAmelCase :       Union[str, Any]	   =     orig_key.replace("mlm"		,				"cls.predictions.transform"					)
     if "cls" not in orig_key:
          _lowerCAmelCase :       Optional[int]	   =     "nystromformer." + orig_key

     return orig_key







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   Any:

     """simple docstring"""







     for key in orig_state_dict.copy().keys():
          _lowerCAmelCase :       List[str]	   =     orig_state_dict.pop(SCREAMING_SNAKE_CASE					)

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

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

     return orig_state_dict







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   Optional[Any]:

     """simple docstring"""







     _lowerCAmelCase :       Optional[Any]	   =     torch.load(SCREAMING_SNAKE_CASE		,				map_location="cpu"					)["model_state_dict"]
     _lowerCAmelCase :       Optional[Any]	   =     NystromformerConfig.from_json_file(SCREAMING_SNAKE_CASE					)
     _lowerCAmelCase :       List[str]	   =     NystromformerForMaskedLM(SCREAMING_SNAKE_CASE					)

     _lowerCAmelCase :       Union[str, Any]	   =     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__":
  __UpperCAmelCase     					=  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.'
  )
  __UpperCAmelCase     					=  parser.parse_args()
  convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






