





'''simple docstring'''



import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						List[Any]      =   (UniPCMultistepScheduler,)
     UpperCAmelCase__       :						Dict      =   (('''num_inference_steps''', 25),)

     def 	UpperCamelCase(					self						,   **lowerCamelCase	):
       _snake_case     		=				{
           "num_train_timesteps": 1_000,
           "beta_start": 0.0001,
           "beta_end": 0.02,
           "beta_schedule": "linear",
           "solver_order": 2,
           "solver_type": "bh2",
       }

       config.update(**lowerCamelCase	)
       return config

     def 	UpperCamelCase(					self						,   lowerCamelCase=0						,   **lowerCamelCase	):
       _snake_case     		=				dict(self.forward_default_kwargs	)
       _snake_case     		=				kwargs.pop("num_inference_steps"						,   lowerCamelCase	)
       _snake_case     		=				self.dummy_sample
       _snake_case     		=				0.1 * sample
       _snake_case     		=				[residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
         _snake_case     		=				self.get_scheduler_config(**lowerCamelCase	)
         _snake_case     		=				scheduler_class(**lowerCamelCase	)
         scheduler.set_timesteps(lowerCamelCase	)
         # copy over dummy past residuals
         _snake_case     		=				dummy_past_residuals[: scheduler.config.solver_order]

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

         _snake_case		,		_snake_case     		=				sample, sample
         for t in range(lowerCamelCase						,   time_step + scheduler.config.solver_order + 1	):
           _snake_case     		=				scheduler.step(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   **lowerCamelCase	).prev_sample
           _snake_case     		=				new_scheduler.step(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   **lowerCamelCase	).prev_sample

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

     def 	UpperCamelCase(					self						,   lowerCamelCase=0						,   **lowerCamelCase	):
       _snake_case     		=				dict(self.forward_default_kwargs	)
       _snake_case     		=				kwargs.pop("num_inference_steps"						,   lowerCamelCase	)
       _snake_case     		=				self.dummy_sample
       _snake_case     		=				0.1 * sample
       _snake_case     		=				[residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
         _snake_case     		=				self.get_scheduler_config()
         _snake_case     		=				scheduler_class(**lowerCamelCase	)
         scheduler.set_timesteps(lowerCamelCase	)

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

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

           # copy over dummy past residual (must be after setting timesteps)
           _snake_case     		=				dummy_past_residuals[: new_scheduler.config.solver_order]

         _snake_case     		=				scheduler.step(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   **lowerCamelCase	).prev_sample
         _snake_case     		=				new_scheduler.step(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   **lowerCamelCase	).prev_sample

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

     def 	UpperCamelCase(					self						,   lowerCamelCase=None						,   **lowerCamelCase	):
       if scheduler is None:
         _snake_case     		=				self.scheduler_classes[0]
         _snake_case     		=				self.get_scheduler_config(**lowerCamelCase	)
         _snake_case     		=				scheduler_class(**lowerCamelCase	)

       _snake_case     		=				self.scheduler_classes[0]
       _snake_case     		=				self.get_scheduler_config(**lowerCamelCase	)
       _snake_case     		=				scheduler_class(**lowerCamelCase	)

       _snake_case     		=				10
       _snake_case     		=				self.dummy_model()
       _snake_case     		=				self.dummy_sample_deter
       scheduler.set_timesteps(lowerCamelCase	)

       for i, t in enumerate(scheduler.timesteps	):
         _snake_case     		=				model(lowerCamelCase						,   lowerCamelCase	)
         _snake_case     		=				scheduler.step(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	).prev_sample

       return sample

     def 	UpperCamelCase(					self	):
       _snake_case     		=				dict(self.forward_default_kwargs	)

       _snake_case     		=				kwargs.pop("num_inference_steps"						,   lowerCamelCase	)

       for scheduler_class in self.scheduler_classes:
         _snake_case     		=				self.get_scheduler_config()
         _snake_case     		=				scheduler_class(**lowerCamelCase	)

         _snake_case     		=				self.dummy_sample
         _snake_case     		=				0.1 * sample

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

         # copy over dummy past residuals (must be done after set_timesteps)
         _snake_case     		=				[residual + 0.2, residual + 0.15, residual + 0.10]
         _snake_case     		=				dummy_past_residuals[: scheduler.config.solver_order]

         _snake_case     		=				scheduler.timesteps[5]
         _snake_case     		=				scheduler.timesteps[6]

         _snake_case     		=				scheduler.step(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   **lowerCamelCase	).prev_sample
         _snake_case     		=				scheduler.step(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   **lowerCamelCase	).prev_sample

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

     def 	UpperCamelCase(					self	):
       # make sure that iterating over schedulers with same config names gives same results
       # for defaults
       _snake_case     		=				UniPCMultistepScheduler(**self.get_scheduler_config()	)
       _snake_case     		=				self.full_loop(scheduler=lowerCamelCase	)
       _snake_case     		=				torch.mean(torch.abs(lowerCamelCase	)	)

       assert abs(result_mean.item() - 0.2464	) < 1e-3

       _snake_case     		=				DPMSolverSinglestepScheduler.from_config(scheduler.config	)
       _snake_case     		=				DEISMultistepScheduler.from_config(scheduler.config	)
       _snake_case     		=				DPMSolverMultistepScheduler.from_config(scheduler.config	)
       _snake_case     		=				UniPCMultistepScheduler.from_config(scheduler.config	)

       _snake_case     		=				self.full_loop(scheduler=lowerCamelCase	)
       _snake_case     		=				torch.mean(torch.abs(lowerCamelCase	)	)

       assert abs(result_mean.item() - 0.2464	) < 1e-3

     def 	UpperCamelCase(					self	):
       for timesteps in [25, 50, 100, 999, 1_000]:
         self.check_over_configs(num_train_timesteps=lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       self.check_over_configs(thresholding=lowerCamelCase	)
       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=lowerCamelCase						,   prediction_type=lowerCamelCase						,   sample_max_value=lowerCamelCase						,   solver_order=lowerCamelCase						,   solver_type=lowerCamelCase						,   )

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

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

     def 	UpperCamelCase(					self	):
       self.check_over_configs(lower_order_final=lowerCamelCase	)
       self.check_over_configs(lower_order_final=lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
         self.check_over_forward(num_inference_steps=lowerCamelCase						,   time_step=0	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.full_loop()
       _snake_case     		=				torch.mean(torch.abs(lowerCamelCase	)	)

       assert abs(result_mean.item() - 0.2464	) < 1e-3

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.full_loop(prediction_type="v_prediction"	)
       _snake_case     		=				torch.mean(torch.abs(lowerCamelCase	)	)

       assert abs(result_mean.item() - 0.1014	) < 1e-3

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.scheduler_classes[0]
       _snake_case     		=				self.get_scheduler_config(thresholding=lowerCamelCase						,   dynamic_thresholding_ratio=0	)
       _snake_case     		=				scheduler_class(**lowerCamelCase	)

       _snake_case     		=				10
       _snake_case     		=				self.dummy_model()
       _snake_case     		=				self.dummy_sample_deter.half()
       scheduler.set_timesteps(lowerCamelCase	)

       for i, t in enumerate(scheduler.timesteps	):
         _snake_case     		=				model(lowerCamelCase						,   lowerCamelCase	)
         _snake_case     		=				scheduler.step(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	).prev_sample

       assert sample.dtype == torch.floataa






     def 	UpperCamelCase(					self						,   **lowerCamelCase	):
       for scheduler_class in self.scheduler_classes:
         _snake_case     		=				self.get_scheduler_config(**lowerCamelCase	)
         _snake_case     		=				scheduler_class(**lowerCamelCase	)

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









'''simple docstring'''



from __future__ import annotations

from PIL import Image

# Define glider example
__magic_name__   :						str    =			[
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
__magic_name__   :						List[Any]    =			[[0, 1, 0], [0, 1, 0], [0, 1, 0]]




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				[]
  for i in range(len(SCREAMING_SNAKE_CASE__       )       ):
    _snake_case     		=				[]
    for j in range(len(cells[i]       )       ):
      # Get the number of live neighbours
      _snake_case     		=				0
      if i > 0 and j > 0:
        neighbour_count += cells[i - 1][j - 1]
      if i > 0:
        neighbour_count += cells[i - 1][j]
      if i > 0 and j < len(cells[i]       ) - 1:
        neighbour_count += cells[i - 1][j + 1]
      if j > 0:
        neighbour_count += cells[i][j - 1]
      if j < len(cells[i]       ) - 1:
        neighbour_count += cells[i][j + 1]
      if i < len(SCREAMING_SNAKE_CASE__       ) - 1 and j > 0:
        neighbour_count += cells[i + 1][j - 1]
      if i < len(SCREAMING_SNAKE_CASE__       ) - 1:
        neighbour_count += cells[i + 1][j]
      if i < len(SCREAMING_SNAKE_CASE__       ) - 1 and j < len(cells[i]       ) - 1:
        neighbour_count += cells[i + 1][j + 1]

      # Rules of the game of life (excerpt from Wikipedia):
      # 1. Any live cell with two or three live neighbours survives.
      # 2. Any dead cell with three live neighbours becomes a live cell.
      # 3. All other live cells die in the next generation.
      #    Similarly, all other dead cells stay dead.
      _snake_case     		=				cells[i][j] == 1
      if (
          (alive and 2 <= neighbour_count <= 3)
          or not alive
          and neighbour_count == 3
      ):
        next_generation_row.append(1       )
      else:
        next_generation_row.append(0       )

    next_generation.append(SCREAMING_SNAKE_CASE__       )
  return next_generation




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				[]
  for _ in range(SCREAMING_SNAKE_CASE__       ):
    # Create output image
    _snake_case     		=				Image.new("RGB"					,	(len(cells[0]       ), len(SCREAMING_SNAKE_CASE__       ))       )
    _snake_case     		=				img.load()

    # Save cells to image
    for x in range(len(SCREAMING_SNAKE_CASE__       )       ):
      for y in range(len(cells[0]       )       ):
        _snake_case     		=				2_55 - cells[y][x] * 2_55
        _snake_case     		=				(colour, colour, colour)

        # Save image
    images.append(SCREAMING_SNAKE_CASE__       )
    _snake_case     		=				new_generation(SCREAMING_SNAKE_CASE__       )
  return images


if __name__ == "__main__":
 __magic_name__   :						Optional[Any]    =			generate_images(GLIDER, 16)
 images[0].save("""out.gif""", save_all=True, append_images=images[1:])


