






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest





class 			__a  (    A__       ):
 _lowerCAmelCase       :       int        = (UniPCMultistepScheduler,)
 _lowerCAmelCase       :       Union[str, Any]        = (('''num_inference_steps''', 2_5),)







 def 					__lowercase					(		self					:   Any							,   **SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  {
          "num_train_timesteps": 10_00,
          "beta_start": 0.0_0_0_1,
          "beta_end": 0.0_2,
          "beta_schedule": "linear",
          "solver_order": 2,
          "solver_type": "bh2",
      }

      config.update(**SCREAMING_SNAKE_CASE						)
      return config







 def 					__lowercase					(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   Any=0							,   **SCREAMING_SNAKE_CASE					:   Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  dict(self.forward_default_kwargs						)
      UpperCamelCase__			:   Union[str, Any]       				=  kwargs.pop("num_inference_steps"							,   SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[Any]       				=  self.dummy_sample
      UpperCamelCase__			:   Optional[Any]       				=  0.1 * sample
      UpperCamelCase__			:   Tuple       				=  [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

      for scheduler_class in self.scheduler_classes:
           UpperCamelCase__			:   Optional[Any]       				=  self.get_scheduler_config(**SCREAMING_SNAKE_CASE						)
           UpperCamelCase__			:   Optional[Any]       				=  scheduler_class(**SCREAMING_SNAKE_CASE						)
           scheduler.set_timesteps(SCREAMING_SNAKE_CASE						)
           # copy over dummy past residuals
           UpperCamelCase__			:   str       				=  dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(SCREAMING_SNAKE_CASE						)
                UpperCamelCase__			:   Tuple       				=  scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE						)
                new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE						)
                # copy over dummy past residuals
                UpperCamelCase__			:   Tuple       				=  dummy_past_residuals[: new_scheduler.config.solver_order]

           UpperCamelCase__				,     UpperCamelCase__			:   Optional[int]       				=  sample, sample
           for t in range(SCREAMING_SNAKE_CASE							,   time_step + scheduler.config.solver_order + 1						):
                UpperCamelCase__			:   Optional[Any]       				=  scheduler.step(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						).prev_sample
                UpperCamelCase__			:   Tuple       				=  new_scheduler.step(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 					__lowercase					(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Dict=0							,   **SCREAMING_SNAKE_CASE					:   Any						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  dict(self.forward_default_kwargs						)
      UpperCamelCase__			:   int       				=  kwargs.pop("num_inference_steps"							,   SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[Any]       				=  self.dummy_sample
      UpperCamelCase__			:   str       				=  0.1 * sample
      UpperCamelCase__			:   Dict       				=  [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

      for scheduler_class in self.scheduler_classes:
           UpperCamelCase__			:   Optional[int]       				=  self.get_scheduler_config()
           UpperCamelCase__			:   Dict       				=  scheduler_class(**SCREAMING_SNAKE_CASE						)
           scheduler.set_timesteps(SCREAMING_SNAKE_CASE						)

           # copy over dummy past residuals (must be after setting timesteps)
           UpperCamelCase__			:   Dict       				=  dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(SCREAMING_SNAKE_CASE						)
                UpperCamelCase__			:   Tuple       				=  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__			:   Optional[Any]       				=  dummy_past_residuals[: new_scheduler.config.solver_order]

           UpperCamelCase__			:   Dict       				=  scheduler.step(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						).prev_sample
           UpperCamelCase__			:   List[Any]       				=  new_scheduler.step(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 					__lowercase					(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Any=None							,   **SCREAMING_SNAKE_CASE					:   List[Any]						):


      '''simple docstring'''






      if scheduler is None:
           UpperCamelCase__			:   List[Any]       				=  self.scheduler_classes[0]
           UpperCamelCase__			:   str       				=  self.get_scheduler_config(**SCREAMING_SNAKE_CASE						)
           UpperCamelCase__			:   Tuple       				=  scheduler_class(**SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Optional[Any]       				=  self.scheduler_classes[0]
      UpperCamelCase__			:   List[Any]       				=  self.get_scheduler_config(**SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[int]       				=  scheduler_class(**SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Any       				=  10
      UpperCamelCase__			:   Dict       				=  self.dummy_model()
      UpperCamelCase__			:   List[Any]       				=  self.dummy_sample_deter
      scheduler.set_timesteps(SCREAMING_SNAKE_CASE						)

      for i, t in enumerate(scheduler.timesteps						):
           UpperCamelCase__			:   int       				=  model(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
           UpperCamelCase__			:   List[str]       				=  scheduler.step(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						).prev_sample

      return sample







 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  dict(self.forward_default_kwargs						)

      UpperCamelCase__			:   Any       				=  kwargs.pop("num_inference_steps"							,   SCREAMING_SNAKE_CASE						)

      for scheduler_class in self.scheduler_classes:
           UpperCamelCase__			:   Optional[int]       				=  self.get_scheduler_config()
           UpperCamelCase__			:   int       				=  scheduler_class(**SCREAMING_SNAKE_CASE						)

           UpperCamelCase__			:   Optional[int]       				=  self.dummy_sample
           UpperCamelCase__			:   Optional[Any]       				=  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__			:   Optional[int]       				=  num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           UpperCamelCase__			:   str       				=  [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
           UpperCamelCase__			:   Tuple       				=  dummy_past_residuals[: scheduler.config.solver_order]

           UpperCamelCase__			:   Optional[Any]       				=  scheduler.timesteps[5]
           UpperCamelCase__			:   int       				=  scheduler.timesteps[6]

           UpperCamelCase__			:   Union[str, Any]       				=  scheduler.step(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						).prev_sample
           UpperCamelCase__			:   Union[str, Any]       				=  scheduler.step(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						).prev_sample

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







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  UniPCMultistepScheduler(**self.get_scheduler_config()						)
      UpperCamelCase__			:   List[Any]       				=  self.full_loop(scheduler=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   int       				=  torch.mean(torch.abs(SCREAMING_SNAKE_CASE						)						)

      assert abs(result_mean.item() - 0.2_4_6_4						) < 1e-3

      UpperCamelCase__			:   Dict       				=  DPMSolverSinglestepScheduler.from_config(scheduler.config						)
      UpperCamelCase__			:   int       				=  DEISMultistepScheduler.from_config(scheduler.config						)
      UpperCamelCase__			:   str       				=  DPMSolverMultistepScheduler.from_config(scheduler.config						)
      UpperCamelCase__			:   Union[str, Any]       				=  UniPCMultistepScheduler.from_config(scheduler.config						)

      UpperCamelCase__			:   Optional[int]       				=  self.full_loop(scheduler=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[int]       				=  torch.mean(torch.abs(SCREAMING_SNAKE_CASE						)						)

      assert abs(result_mean.item() - 0.2_4_6_4						) < 1e-3







 def 					__lowercase					(		self					:   Optional[int]						):


      '''simple docstring'''






      for timesteps in [25, 50, 1_00, 9_99, 10_00]:
           self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE						)







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


      '''simple docstring'''






      self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE						)
      for order in [1, 2, 3]:
           for solver_type in ["bh1", "bh2"]:
                for threshold in [0.5, 1.0, 2.0]:
                     for prediction_type in ["epsilon", "sample"]:
                          self.check_over_configs(
                              thresholding=SCREAMING_SNAKE_CASE							,   prediction_type=SCREAMING_SNAKE_CASE							,   sample_max_value=SCREAMING_SNAKE_CASE							,   solver_order=SCREAMING_SNAKE_CASE							,   solver_type=SCREAMING_SNAKE_CASE							,   )







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






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







 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      for solver_type in ["bh1", "bh2"]:
           for order in [1, 2, 3]:
                for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         solver_order=SCREAMING_SNAKE_CASE							,   solver_type=SCREAMING_SNAKE_CASE							,   prediction_type=SCREAMING_SNAKE_CASE							,   )
                     UpperCamelCase__			:   Optional[int]       				=  self.full_loop(
                         solver_order=SCREAMING_SNAKE_CASE							,   solver_type=SCREAMING_SNAKE_CASE							,   prediction_type=SCREAMING_SNAKE_CASE							,   )
                     assert not torch.isnan(SCREAMING_SNAKE_CASE						).any(), "Samples have nan numbers"







 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE						)
      self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
           self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE							,   time_step=0						)







 def 					__lowercase					(		self					:   Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__			:   Union[str, Any]       				=  self.full_loop()
      UpperCamelCase__			:   Optional[int]       				=  torch.mean(torch.abs(SCREAMING_SNAKE_CASE						)						)

      assert abs(result_mean.item() - 0.2_4_6_4						) < 1e-3







 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  self.full_loop(prediction_type="v_prediction"						)
      UpperCamelCase__			:   Optional[Any]       				=  torch.mean(torch.abs(SCREAMING_SNAKE_CASE						)						)

      assert abs(result_mean.item() - 0.1_0_1_4						) < 1e-3







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  self.scheduler_classes[0]
      UpperCamelCase__			:   Any       				=  self.get_scheduler_config(thresholding=SCREAMING_SNAKE_CASE							,   dynamic_thresholding_ratio=0						)
      UpperCamelCase__			:   Dict       				=  scheduler_class(**SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Tuple       				=  10
      UpperCamelCase__			:   int       				=  self.dummy_model()
      UpperCamelCase__			:   Tuple       				=  self.dummy_sample_deter.half()
      scheduler.set_timesteps(SCREAMING_SNAKE_CASE						)

      for i, t in enumerate(scheduler.timesteps						):
           UpperCamelCase__			:   int       				=  model(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
           UpperCamelCase__			:   int       				=  scheduler.step(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						).prev_sample

      assert sample.dtype == torch.floataa







 def 					__lowercase					(		self					:   Tuple							,   **SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






      for scheduler_class in self.scheduler_classes:
           UpperCamelCase__			:   Optional[int]       				=  self.get_scheduler_config(**SCREAMING_SNAKE_CASE						)
           UpperCamelCase__			:   Tuple       				=  scheduler_class(**SCREAMING_SNAKE_CASE						)

           scheduler.set_timesteps(scheduler.config.num_train_timesteps						)
           assert len(scheduler.timesteps.unique()						) == scheduler.num_inference_steps







import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


lowerCamelCase	:							Tuple				=False





class 			__a  (    unittest.TestCase       ):
 pass







@slow
@require_torch_gpu
class 			__a  (    unittest.TestCase       ):







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"						)
      pipe.to(SCREAMING_SNAKE_CASE						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Union[str, Any]       				=  load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"						)
      UpperCamelCase__			:   List[Any]       				=  torch.manual_seed(0						)
      UpperCamelCase__			:   List[Any]       				=  pipe(
          image=SCREAMING_SNAKE_CASE							,   generator=SCREAMING_SNAKE_CASE							,   guidance_scale=7.5							,   num_inference_steps=50							,   output_type="numpy"							,   ).images

      UpperCamelCase__			:   Union[str, Any]       				=  image[0, 2_53:2_56, 2_53:2_56, -1]

      assert image.shape == (1, 5_12, 5_12, 3)
      UpperCamelCase__			:   Optional[Any]       				=  np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5]						)

      assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2