





'''simple docstring'''

import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def 						UpperCamelCase    (							_lowerCamelCase : Union[str, Any]						,	_lowerCamelCase : Tuple						,	_lowerCamelCase : str	):
    A__    =      OmegaConf.load(_lowerCamelCase	)
    A__    =      torch.load(_lowerCamelCase						,	map_location="cpu"	)["model"]
    A__    =      list(state_dict.keys()	)

    # extract state_dict for VQVAE
    A__    =      {}
    A__    =      "first_stage_model."
    for key in keys:
        if key.startswith(_lowerCamelCase	):
            A__    =      state_dict[key]

    # extract state_dict for UNetLDM
    A__    =      {}
    A__    =      "model.diffusion_model."
    for key in keys:
        if key.startswith(_lowerCamelCase	):
            A__    =      state_dict[key]

    A__    =      config.model.params.first_stage_config.params
    A__    =      config.model.params.unet_config.params

    A__    =      VQModel(**_lowerCamelCase	).eval()
    vqvae.load_state_dict(_lowerCamelCase	)

    A__    =      UNetLDMModel(**_lowerCamelCase	).eval()
    unet.load_state_dict(_lowerCamelCase	)

    A__    =      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=_lowerCamelCase						,	)

    A__    =      LDMPipeline(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
    pipeline.save_pretrained(_lowerCamelCase	)


if __name__ == "__main__":
  __lowerCAmelCase							:     Optional[int]		     =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)
  __lowerCAmelCase							:     Union[str, Any]		     =parser.parse_args()

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










'''simple docstring'''

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest






class        UpperCAmelCase     (	UpperCamelCase__    ):
 __lowercase       		=    (PNDMScheduler,)
 __lowercase       		=    (("""num_inference_steps""", 50),)
 def 					UpperCAmelCase_							(    self				:List[str]						,	**lowercase_				:Optional[int]							)->							List[str]:
     A__    =      {
         "num_train_timesteps": 10_00,
         "beta_start": 0.0_0_0_1,
         "beta_end": 0.0_2,
         "beta_schedule": "linear",
     }

     config.update(**lowercase_							)
     return config
 def 					UpperCAmelCase_							(    self				:int						,	lowercase_				:Optional[int]=0						,	**lowercase_				:Any							)->							int:
     A__    =      dict(self.forward_default_kwargs							)
     A__    =      kwargs.pop("num_inference_steps"						,	lowercase_							)
     A__    =      self.dummy_sample
     A__    =      0.1 * sample
     A__    =      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

     for scheduler_class in self.scheduler_classes:
         A__    =      self.get_scheduler_config(**lowercase_							)
         A__    =      scheduler_class(**lowercase_							)
         scheduler.set_timesteps(lowercase_							)
         # copy over dummy past residuals
         A__    =      dummy_past_residuals[:]

         with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(lowercase_							)
             A__    =      scheduler_class.from_pretrained(lowercase_							)
             new_scheduler.set_timesteps(lowercase_							)
             # copy over dummy past residuals
             A__    =      dummy_past_residuals[:]

         A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

         assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

         A__    =      scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

         assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Tuple:
     pass
 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:Optional[Any]=0						,	**lowercase_				:List[str]							)->							str:
     A__    =      dict(self.forward_default_kwargs							)
     A__    =      kwargs.pop("num_inference_steps"						,	lowercase_							)
     A__    =      self.dummy_sample
     A__    =      0.1 * sample
     A__    =      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

     for scheduler_class in self.scheduler_classes:
         A__    =      self.get_scheduler_config()
         A__    =      scheduler_class(**lowercase_							)
         scheduler.set_timesteps(lowercase_							)

         # copy over dummy past residuals (must be after setting timesteps)
         A__    =      dummy_past_residuals[:]

         with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(lowercase_							)
             A__    =      scheduler_class.from_pretrained(lowercase_							)
             # copy over dummy past residuals
             new_scheduler.set_timesteps(lowercase_							)

             # copy over dummy past residual (must be after setting timesteps)
             A__    =      dummy_past_residuals[:]

         A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

         assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

         A__    =      scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

         assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"
 def 					UpperCAmelCase_							(    self				:Optional[int]						,	**lowercase_				:Dict							)->							Optional[int]:
     A__    =      self.scheduler_classes[0]
     A__    =      self.get_scheduler_config(**lowercase_							)
     A__    =      scheduler_class(**lowercase_							)

     A__    =      10
     A__    =      self.dummy_model()
     A__    =      self.dummy_sample_deter
     scheduler.set_timesteps(lowercase_							)

     for i, t in enumerate(scheduler.prk_timesteps							):
         A__    =      model(lowercase_						,	lowercase_							)
         A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_							).prev_sample

     for i, t in enumerate(scheduler.plms_timesteps							):
         A__    =      model(lowercase_						,	lowercase_							)
         A__    =      scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_							).prev_sample

     return sample
 def 					UpperCAmelCase_							(    self				:int							)->							Union[str, Any]:
     A__    =      dict(self.forward_default_kwargs							)

     A__    =      kwargs.pop("num_inference_steps"						,	lowercase_							)

     for scheduler_class in self.scheduler_classes:
         A__    =      self.get_scheduler_config()
         A__    =      scheduler_class(**lowercase_							)

         A__    =      self.dummy_sample
         A__    =      0.1 * sample

         if num_inference_steps is not None and hasattr(lowercase_						,	"set_timesteps"							):
             scheduler.set_timesteps(lowercase_							)
         elif num_inference_steps is not None and not hasattr(lowercase_						,	"set_timesteps"							):
             A__    =      num_inference_steps

         # copy over dummy past residuals (must be done after set_timesteps)
         A__    =      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
         A__    =      dummy_past_residuals[:]

         A__    =      scheduler.step_prk(lowercase_						,	0						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      scheduler.step_prk(lowercase_						,	1						,	lowercase_						,	**lowercase_							).prev_sample

         self.assertEqual(output_a.shape						,	sample.shape							)
         self.assertEqual(output_a.shape						,	output_a.shape							)

         A__    =      scheduler.step_plms(lowercase_						,	0						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      scheduler.step_plms(lowercase_						,	1						,	lowercase_						,	**lowercase_							).prev_sample

         self.assertEqual(output_a.shape						,	sample.shape							)
         self.assertEqual(output_a.shape						,	output_a.shape							)
 def 					UpperCAmelCase_							(    self				:Any							)->							Dict:
     for timesteps in [1_00, 10_00]:
         self.check_over_configs(num_train_timesteps=lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							int:
     for steps_offset in [0, 1]:
         self.check_over_configs(steps_offset=lowercase_							)

     A__    =      self.scheduler_classes[0]
     A__    =      self.get_scheduler_config(steps_offset=1							)
     A__    =      scheduler_class(**lowercase_							)
     scheduler.set_timesteps(10							)
     assert torch.equal(
         scheduler.timesteps						,	torch.LongTensor(
             [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]							)						,	)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Any:
     for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]						,	[0.0_0_2, 0.0_2]							):
         self.check_over_configs(beta_start=lowercase_						,	beta_end=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Tuple:
     for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Tuple:
     for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							Tuple:
     for t in [1, 5, 10]:
         self.check_over_forward(time_step=lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Optional[int]:
     for t, num_inference_steps in zip([1, 5, 10]						,	[10, 50, 1_00]							):
         self.check_over_forward(num_inference_steps=lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							str:
     # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
     A__    =      27

     for scheduler_class in self.scheduler_classes:
         A__    =      self.dummy_sample
         A__    =      0.1 * sample

         A__    =      self.get_scheduler_config()
         A__    =      scheduler_class(**lowercase_							)

         scheduler.set_timesteps(lowercase_							)

         # before power of 3 fix, would error on first step, so we only need to do two
         for i, t in enumerate(scheduler.prk_timesteps[:2]							):
             A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_							).prev_sample
 def 					UpperCAmelCase_							(    self				:List[str]							)->							str:
     with self.assertRaises(lowercase_							):
         A__    =      self.scheduler_classes[0]
         A__    =      self.get_scheduler_config()
         A__    =      scheduler_class(**lowercase_							)

         scheduler.step_plms(self.dummy_sample						,	1						,	self.dummy_sample							).prev_sample
 def 					UpperCAmelCase_							(    self				:int							)->							str:
     A__    =      self.full_loop()
     A__    =      torch.sum(torch.abs(lowercase_							)							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_sum.item() - 1_9_8.1_3_1_8							) < 1E-2
     assert abs(result_mean.item() - 0.2_5_8_0							) < 1E-3
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							List[Any]:
     A__    =      self.full_loop(prediction_type="v_prediction"							)
     A__    =      torch.sum(torch.abs(lowercase_							)							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_sum.item() - 6_7.3_9_8_6							) < 1E-2
     assert abs(result_mean.item() - 0.0_8_7_8							) < 1E-3
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Tuple:
     # We specify different beta, so that the first alpha is 0.99
     A__    =      self.full_loop(set_alpha_to_one=lowercase_						,	beta_start=0.0_1							)
     A__    =      torch.sum(torch.abs(lowercase_							)							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_sum.item() - 2_3_0.0_3_9_9							) < 1E-2
     assert abs(result_mean.item() - 0.2_9_9_5							) < 1E-3



 def 					UpperCAmelCase_							(    self				:Dict							)->							Any:
     # We specify different beta, so that the first alpha is 0.99
     A__    =      self.full_loop(set_alpha_to_one=lowercase_						,	beta_start=0.0_1							)
     A__    =      torch.sum(torch.abs(lowercase_							)							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_sum.item() - 1_8_6.9_4_8_2							) < 1E-2
     assert abs(result_mean.item() - 0.2_4_3_4							) < 1E-3



