

import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()





class A__     (			unittest.TestCase			):



 """simple docstring"""







 _lowercase  :       Optional[int]  			=    '''hf-internal-testing/tiny-stable-diffusion-pipe'''
 _lowercase  :       Tuple  			=    1
 _lowercase  :       List[str]  			=    77
 _lowercase  :       Any  			=    32
 _lowercase  :       List[str]  			=    4
 _lowercase  :       Dict  			=    64
 _lowercase  :       Optional[Any]  			=    torch.manual_seed(0			)

 def  __magic_name__							(		self			:						Optional[int]  ,    A_			:						Tuple=0.9999							):






      '''simple docstring'''




      _lowerCAmelCase :       Optional[int]	   =     UNetaDConditionModel.from_pretrained(self.model_id  ,    subfolder="unet"							)
      _lowerCAmelCase :       int	   =     unet.to(A_							)
      _lowerCAmelCase :       Union[str, Any]	   =     EMAModel(unet.parameters()  ,    decay=A_  ,    model_cls=A_  ,    model_config=unet.config							)
      return unet, ema_unet

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






      '''simple docstring'''




      _lowerCAmelCase :       Optional[int]	   =     torch.randn(
          self.batch_size  ,    self.num_in_channels  ,    self.latent_height  ,    self.latent_width  ,    generator=self.generator							).to(A_							)
      _lowerCAmelCase :       Optional[int]	   =     torch.randint(0  ,    1_0_0_0  ,    size=(self.batch_size,)  ,    generator=self.generator							).to(A_							)
      _lowerCAmelCase :       Any	   =     torch.randn(
          self.batch_size  ,    self.prompt_length  ,    self.text_encoder_hidden_dim  ,    generator=self.generator							).to(A_							)
      return noisy_latents, timesteps, encoder_hidden_states

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






      '''simple docstring'''




      _lowerCAmelCase :       Dict	   =     {}
      for k, param in unet.state_dict().items():
           _lowerCAmelCase :       List[Any]	   =     torch.randn_like(A_							) + (param * torch.randn_like(A_							))
           updated_state_dict.update({k: updated_param}							)
      unet.load_state_dict(A_							)
      return unet

 def  __magic_name__							(		self			:						Tuple							):






      '''simple docstring'''




      _lowerCAmelCase   ,      _lowerCAmelCase :       Tuple	   =     self.get_models()
      # Take the first (hypothetical) EMA step.
      ema_unet.step(unet.parameters()							)
      assert ema_unet.optimization_step == 1

      # Take two more.
      for _ in range(2							):
           ema_unet.step(unet.parameters()							)
      assert ema_unet.optimization_step == 3

 def  __magic_name__							(		self			:						Tuple							):






      '''simple docstring'''




      _lowerCAmelCase   ,      _lowerCAmelCase :       Dict	   =     self.get_models()
      # Since the `unet` is not being updated (i.e., backprop'd)
      # there won't be any difference between the `params` of `unet`
      # and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
      ema_unet.step(unet.parameters()							)
      _lowerCAmelCase :       List[Any]	   =     list(unet.parameters()							)
      for s_param, param in zip(ema_unet.shadow_params  ,    A_							):
           assert torch.allclose(A_  ,    A_							)

      # The above holds true even if we call `ema.step()` multiple times since
      # `unet` params are still not being updated.
      for _ in range(4							):
           ema_unet.step(unet.parameters()							)
      for s_param, param in zip(ema_unet.shadow_params  ,    A_							):
           assert torch.allclose(A_  ,    A_							)

 def  __magic_name__							(		self			:						List[str]							):






      '''simple docstring'''




      _lowerCAmelCase   ,      _lowerCAmelCase :       List[Any]	   =     self.get_models()
      # Here we simulate the parameter updates for `unet`. Since there might
      # be some parameters which are initialized to zero we take extra care to
      # initialize their values to something non-zero before the multiplication.
      _lowerCAmelCase :       str	   =     self.simulate_backprop(A_							)

      # Take the EMA step.
      ema_unet.step(unet_pseudo_updated_step_one.parameters()							)

      # Now the EMA'd parameters won't be equal to the original model parameters.
      _lowerCAmelCase :       Dict	   =     list(unet_pseudo_updated_step_one.parameters()							)
      for s_param, param in zip(ema_unet.shadow_params  ,    A_							):
           assert ~torch.allclose(A_  ,    A_							)

      # Ensure this is the case when we take multiple EMA steps.
      for _ in range(4							):
           ema_unet.step(unet.parameters()							)
      for s_param, param in zip(ema_unet.shadow_params  ,    A_							):
           assert ~torch.allclose(A_  ,    A_							)

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






      '''simple docstring'''




      _lowerCAmelCase   ,      _lowerCAmelCase :       int	   =     self.get_models()

      # First backprop + EMA
      _lowerCAmelCase :       Dict	   =     self.simulate_backprop(A_							)
      ema_unet.step(unet_step_one.parameters()							)
      _lowerCAmelCase :       str	   =     ema_unet.shadow_params

      # Second backprop + EMA
      _lowerCAmelCase :       Any	   =     self.simulate_backprop(A_							)
      ema_unet.step(unet_step_two.parameters()							)
      _lowerCAmelCase :       Union[str, Any]	   =     ema_unet.shadow_params

      for step_one, step_two in zip(A_  ,    A_							):
           assert ~torch.allclose(A_  ,    A_							)

 def  __magic_name__							(		self			:						List[Any]							):






      '''simple docstring'''




      _lowerCAmelCase   ,      _lowerCAmelCase :       int	   =     self.get_models(decay=0.0							)
      _lowerCAmelCase :       int	   =     self.simulate_backprop(A_							)
      ema_unet.step(unet_step_one.parameters()							)
      _lowerCAmelCase :       str	   =     ema_unet.shadow_params

      _lowerCAmelCase :       Optional[int]	   =     self.simulate_backprop(A_							)
      ema_unet.step(unet_step_two.parameters()							)
      _lowerCAmelCase :       Union[str, Any]	   =     ema_unet.shadow_params

      for step_one, step_two in zip(A_  ,    A_							):
           assert torch.allclose(A_  ,    A_							)


 @skip_mps
 def  __magic_name__							(		self			:						str							):






      '''simple docstring'''




      _lowerCAmelCase   ,      _lowerCAmelCase :       str	   =     self.get_models()
      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase :       Optional[int]	   =     self.get_dummy_inputs()

      with tempfile.TemporaryDirectory() as tmpdir:
           ema_unet.save_pretrained(A_							)
           _lowerCAmelCase :       Union[str, Any]	   =     UNetaDConditionModel.from_pretrained(A_  ,    model_cls=A_							)
           _lowerCAmelCase :       str	   =     loaded_unet.to(unet.device							)

      # Since no EMA step has been performed the outputs should match.
      _lowerCAmelCase :       Optional[Any]	   =     unet(A_  ,    A_  ,    A_							).sample
      _lowerCAmelCase :       Optional[int]	   =     loaded_unet(A_  ,    A_  ,    A_							).sample

      assert torch.allclose(A_  ,    A_  ,    atol=1E-4							)









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)






