






"""simple docstring"""







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
 import torch

 from transformers.activations import gelu_new, gelu_python, get_activation





@require_torch
class 	UpperCamelCase_			(							unittest.TestCase	):



  def 		UpperCamelCase_					(   self					)      ->      Optional[int]:



         """simple docstring"""





         UpperCAmelCase						     =      torch.tensor([-1_00, -1, -0.1, 0, 0.1, 1.0, 1_00]					)
         UpperCAmelCase						     =      get_activation("""gelu"""					)
         self.assertTrue(torch.allclose(gelu_python(lowerCamelCase_					)			,		torch_builtin(lowerCamelCase_					)					)					)
         self.assertFalse(torch.allclose(gelu_python(lowerCamelCase_					)			,		gelu_new(lowerCamelCase_					)					)					)



  def 		UpperCamelCase_					(   self					)      ->      Optional[int]:



         """simple docstring"""





         UpperCAmelCase						     =      torch.tensor([-1_00, -1, -0.1, 0, 0.1, 1.0, 1_00]					)
         UpperCAmelCase						     =      get_activation("""gelu"""					)
         UpperCAmelCase						     =      get_activation("""gelu_10"""					)

         UpperCAmelCase						     =      torch_builtin(lowerCamelCase_					)
         UpperCAmelCase						     =      geluaa(lowerCamelCase_					)

         UpperCAmelCase						     =      torch.where(y_gelu_aa < 10.0			,		1			,		0					)

         self.assertTrue(torch.max(lowerCamelCase_					).item() == 10.0					)
         self.assertTrue(torch.allclose(y_gelu * clipped_mask			,		y_gelu_aa * clipped_mask					)					)



  def 		UpperCamelCase_					(   self					)      ->      str:



         """simple docstring"""





         get_activation("""gelu"""					)
         get_activation("""gelu_10"""					)
         get_activation("""gelu_fast"""					)
         get_activation("""gelu_new"""					)
         get_activation("""gelu_python"""					)
         get_activation("""gelu_pytorch_tanh"""					)
         get_activation("""linear"""					)
         get_activation("""mish"""					)
         get_activation("""quick_gelu"""					)
         get_activation("""relu"""					)
         get_activation("""sigmoid"""					)
         get_activation("""silu"""					)
         get_activation("""swish"""					)
         get_activation("""tanh"""					)
         with self.assertRaises(lowerCamelCase_					):
                get_activation("""bogus"""					)
         with self.assertRaises(lowerCamelCase_					):
                get_activation(lowerCamelCase_					)



  def 		UpperCamelCase_					(   self					)      ->      List[Any]:



         """simple docstring"""





         UpperCAmelCase						     =      get_activation("""gelu"""					)
         UpperCAmelCase						     =      1
         UpperCAmelCase						     =      get_activation("""gelu"""					)
         self.assertEqual(acta.a			,		1					)
         with self.assertRaises(lowerCamelCase_					):
                UpperCAmelCase						     =      acta.a




'''simple docstring'''






import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def    _snake_case  (	A					,       A					,       A			)		->       Union[str, Any]:
							lowerCAmelCase__			  =      OmegaConf.load(A			)
							lowerCAmelCase__			  =      torch.load(A					,       map_location='''cpu'''			)['''model''']
							lowerCAmelCase__			  =      list(state_dict.keys()			)

							# extract state_dict for VQVAE
							lowerCAmelCase__			  =      {}
							lowerCAmelCase__			  =      '''first_stage_model.'''
							for key in keys:
														if key.startswith(A			):
																					lowerCAmelCase__			  =      state_dict[key]

    # extract state_dict for UNetLDM
							lowerCAmelCase__			  =      {}
							lowerCAmelCase__			  =      '''model.diffusion_model.'''
							for key in keys:
														if key.startswith(A			):
																					lowerCAmelCase__			  =      state_dict[key]

							lowerCAmelCase__			  =      config.model.params.first_stage_config.params
							lowerCAmelCase__			  =      config.model.params.unet_config.params

							lowerCAmelCase__			  =      VQModel(**A			).eval()
							vqvae.load_state_dict(A			)

							lowerCAmelCase__			  =      UNetLDMModel(**A			).eval()
							unet.load_state_dict(A			)

							lowerCAmelCase__			  =      DDIMScheduler(
							    timesteps=config.model.params.timesteps					,       beta_schedule='''scaled_linear'''					,       beta_start=config.model.params.linear_start					,       beta_end=config.model.params.linear_end					,       clip_sample=A					,       )

							lowerCAmelCase__			  =      LDMPipeline(A					,       A					,       A			)
							pipeline.save_pretrained(A			)


if __name__ == "__main__":
		__UpperCAmelCase       							=				argparse.ArgumentParser()
		parser.add_argument('''--checkpoint_path''', type=str, required=True)
		parser.add_argument('''--config_path''', type=str, required=True)
		parser.add_argument('''--output_path''', type=str, required=True)
		__UpperCAmelCase       							=				parser.parse_args()

		convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)