






'''simple docstring'''

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest






class        a_		(    lowerCamelCase       ):
  lowercase  	=   (PNDMScheduler,)
  lowercase  	=   (('num_inference_steps', 50),)

  def 						A__		(   self    ,	**_SCREAMING_SNAKE_CASE   )						->   Optional[Any]:



        """simple docstring"""






        UpperCamelCase       						=      {
            """num_train_timesteps""": 1000,
            """beta_start""": 0.0_0_0_1,
            """beta_end""": 0.0_2,
            """beta_schedule""": """linear""",
        }

        config.update(**_SCREAMING_SNAKE_CASE   )
        return config

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE=0    ,	**_SCREAMING_SNAKE_CASE   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      dict(self.forward_default_kwargs   )
        UpperCamelCase       						=      kwargs.pop("""num_inference_steps"""    ,	_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      self.dummy_sample
        UpperCamelCase       						=      0.1 * sample
        UpperCamelCase       						=      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

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

              UpperCamelCase       						=      scheduler.step_prk(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   ).prev_sample
              UpperCamelCase       						=      new_scheduler.step_prk(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   ).prev_sample

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

              UpperCamelCase       						=      scheduler.step_plms(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   ).prev_sample
              UpperCamelCase       						=      new_scheduler.step_plms(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   ).prev_sample

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

  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        pass

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE=0    ,	**_SCREAMING_SNAKE_CASE   )						->   str:



        """simple docstring"""






        UpperCamelCase       						=      dict(self.forward_default_kwargs   )
        UpperCamelCase       						=      kwargs.pop("""num_inference_steps"""    ,	_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      self.dummy_sample
        UpperCamelCase       						=      0.1 * sample
        UpperCamelCase       						=      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

        for scheduler_class in self.scheduler_classes:
              UpperCamelCase       						=      self.get_scheduler_config()
              UpperCamelCase       						=      scheduler_class(**_SCREAMING_SNAKE_CASE   )
              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE   )

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

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

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

              UpperCamelCase       						=      scheduler.step_prk(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   ).prev_sample
              UpperCamelCase       						=      new_scheduler.step_prk(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   ).prev_sample

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

              UpperCamelCase       						=      scheduler.step_plms(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   ).prev_sample
              UpperCamelCase       						=      new_scheduler.step_plms(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   ).prev_sample

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

  def 						A__		(   self    ,	**_SCREAMING_SNAKE_CASE   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      self.scheduler_classes[0]
        UpperCamelCase       						=      self.get_scheduler_config(**_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      scheduler_class(**_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      10
        UpperCamelCase       						=      self.dummy_model()
        UpperCamelCase       						=      self.dummy_sample_deter
        scheduler.set_timesteps(_SCREAMING_SNAKE_CASE   )

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

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

        return sample

  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      dict(self.forward_default_kwargs   )

        UpperCamelCase       						=      kwargs.pop("""num_inference_steps"""    ,	_SCREAMING_SNAKE_CASE   )

        for scheduler_class in self.scheduler_classes:
              UpperCamelCase       						=      self.get_scheduler_config()
              UpperCamelCase       						=      scheduler_class(**_SCREAMING_SNAKE_CASE   )

              UpperCamelCase       						=      self.dummy_sample
              UpperCamelCase       						=      0.1 * sample

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

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

              UpperCamelCase       						=      scheduler.step_prk(_SCREAMING_SNAKE_CASE    ,	0    ,	_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   ).prev_sample
              UpperCamelCase       						=      scheduler.step_prk(_SCREAMING_SNAKE_CASE    ,	1    ,	_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   ).prev_sample

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

              UpperCamelCase       						=      scheduler.step_plms(_SCREAMING_SNAKE_CASE    ,	0    ,	_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   ).prev_sample
              UpperCamelCase       						=      scheduler.step_plms(_SCREAMING_SNAKE_CASE    ,	1    ,	_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   ).prev_sample

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

  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        for timesteps in [100, 1000]:
              self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        for steps_offset in [0, 1]:
              self.check_over_configs(steps_offset=_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      self.scheduler_classes[0]
        UpperCamelCase       						=      self.get_scheduler_config(steps_offset=1   )
        UpperCamelCase       						=      scheduler_class(**_SCREAMING_SNAKE_CASE   )
        scheduler.set_timesteps(10   )
        assert torch.equal(
            scheduler.timesteps    ,	torch.LongTensor(
                [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]   )    ,	)

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



        """simple docstring"""






        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=_SCREAMING_SNAKE_CASE    ,	beta_end=_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






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

  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        for t in [1, 5, 10]:
              self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        for t, num_inference_steps in zip([1, 5, 10]    ,	[10, 50, 100]   ):
              self.check_over_forward(num_inference_steps=_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      27

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

              UpperCamelCase       						=      self.get_scheduler_config()
              UpperCamelCase       						=      scheduler_class(**_SCREAMING_SNAKE_CASE   )

              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE   )

              # 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]   ):
                    UpperCamelCase       						=      scheduler.step_prk(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ).prev_sample

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



        """simple docstring"""






        with self.assertRaises(_SCREAMING_SNAKE_CASE   ):
              UpperCamelCase       						=      self.scheduler_classes[0]
              UpperCamelCase       						=      self.get_scheduler_config()
              UpperCamelCase       						=      scheduler_class(**_SCREAMING_SNAKE_CASE   )

              scheduler.step_plms(self.dummy_sample    ,	1    ,	self.dummy_sample   ).prev_sample

  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      self.full_loop()
        UpperCamelCase       						=      torch.sum(torch.abs(_SCREAMING_SNAKE_CASE   )   )
        UpperCamelCase       						=      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE   )   )

        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 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






        UpperCamelCase       						=      self.full_loop(prediction_type="""v_prediction"""   )
        UpperCamelCase       						=      torch.sum(torch.abs(_SCREAMING_SNAKE_CASE   )   )
        UpperCamelCase       						=      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE   )   )

        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 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.full_loop(set_alpha_to_one=_SCREAMING_SNAKE_CASE    ,	beta_start=0.0_1   )
        UpperCamelCase       						=      torch.sum(torch.abs(_SCREAMING_SNAKE_CASE   )   )
        UpperCamelCase       						=      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE   )   )

        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 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.full_loop(set_alpha_to_one=_SCREAMING_SNAKE_CASE    ,	beta_start=0.0_1   )
        UpperCamelCase       						=      torch.sum(torch.abs(_SCREAMING_SNAKE_CASE   )   )
        UpperCamelCase       						=      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE   )   )

        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








'''simple docstring'''

import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__											=     argparse.ArgumentParser()

     parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

     parser.add_argument(
         '--txt2img_unclip',
         default='kakaobrain/karlo-v1-alpha',
         type=str,
         required=False,
         help='The pretrained txt2img unclip.',
     )

     SCREAMING_SNAKE_CASE__											=     parser.parse_args()

     SCREAMING_SNAKE_CASE__											=     UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

     SCREAMING_SNAKE_CASE__											=     CLIPImageProcessor()
     SCREAMING_SNAKE_CASE__											=     CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

     SCREAMING_SNAKE_CASE__											=     UnCLIPImageVariationPipeline(
         decoder=txtaimg.decoder,
         text_encoder=txtaimg.text_encoder,
         tokenizer=txtaimg.tokenizer,
         text_proj=txtaimg.text_proj,
         feature_extractor=feature_extractor,
         image_encoder=image_encoder,
         super_res_first=txtaimg.super_res_first,
         super_res_last=txtaimg.super_res_last,
         decoder_scheduler=txtaimg.decoder_scheduler,
         super_res_scheduler=txtaimg.super_res_scheduler,
     )

     imgaimg.save_pretrained(args.dump_path)
