





'''simple docstring'''

import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class lowerCamelCase						(a__					):
  _lowercase  :	Optional[int]       					=				(DDIMParallelScheduler,)
  _lowercase  :	int       					=				(("""eta""", 0.0), ("""num_inference_steps""", 50))







  def 				UpperCAmelCase_				(		self     ,     **lowercase__		)		->						int:






        """simple docstring"""





        _snake_case		:	str					     =     {
            '''num_train_timesteps''': 1_000,
            '''beta_start''': 0.0_001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''clip_sample''': True,
        }

        config.update(**lowercase__		)
        return config







  def 				UpperCAmelCase_				(		self     ,     **lowercase__		)		->						Tuple:






        """simple docstring"""





        _snake_case		:	List[Any]					     =     self.scheduler_classes[0]
        _snake_case		:	List[Any]					     =     self.get_scheduler_config(**lowercase__		)
        _snake_case		:	str					     =     scheduler_class(**lowercase__		)

        _snake_case     ,							_snake_case		:	Union[str, Any]					     =     10, 0.0

        _snake_case		:	List[str]					     =     self.dummy_model()
        _snake_case		:	Tuple					     =     self.dummy_sample_deter

        scheduler.set_timesteps(lowercase__		)

        for t in scheduler.timesteps:
              _snake_case		:	Union[str, Any]					     =     model(lowercase__     ,     lowercase__		)
              _snake_case		:	Any					     =     scheduler.step(lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__		).prev_sample

        return sample







  def 				UpperCAmelCase_				(		self		)		->						Dict:






        """simple docstring"""





        for timesteps in [100, 500, 1_000]:
              self.check_over_configs(num_train_timesteps=lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





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

        _snake_case		:	int					     =     self.scheduler_classes[0]
        _snake_case		:	int					     =     self.get_scheduler_config(steps_offset=1		)
        _snake_case		:	Optional[int]					     =     scheduler_class(**lowercase__		)
        scheduler.set_timesteps(5		)
        assert torch.equal(scheduler.timesteps     ,     torch.LongTensor([801, 601, 401, 201, 1]		)		)







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






        """simple docstring"""





        for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]     ,     [0.002, 0.02, 0.2, 2]		):
              self.check_over_configs(beta_start=lowercase__     ,     beta_end=lowercase__		)







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






        """simple docstring"""





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







  def 				UpperCAmelCase_				(		self		)		->						str:






        """simple docstring"""





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







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






        """simple docstring"""





        for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=lowercase__		)







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






        """simple docstring"""





        for timestep_spacing in ["trailing", "leading"]:
              self.check_over_configs(timestep_spacing=lowercase__		)







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






        """simple docstring"""





        for rescale_betas_zero_snr in [True, False]:
              self.check_over_configs(rescale_betas_zero_snr=lowercase__		)







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






        """simple docstring"""





        self.check_over_configs(thresholding=lowercase__		)
        for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(
                        thresholding=lowercase__     ,     prediction_type=lowercase__     ,     sample_max_value=lowercase__     ,     )







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






        """simple docstring"""





        for t in [1, 10, 49]:
              self.check_over_forward(time_step=lowercase__		)







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






        """simple docstring"""





        for t, num_inference_steps in zip([1, 10, 50]     ,     [10, 50, 500]		):
              self.check_over_forward(time_step=lowercase__     ,     num_inference_steps=lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						List[str]:






        """simple docstring"""





        for t, eta in zip([1, 10, 49]     ,     [0.0, 0.5, 1.0]		):
              self.check_over_forward(time_step=lowercase__     ,     eta=lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						List[str]:






        """simple docstring"""





        _snake_case		:	List[Any]					     =     self.scheduler_classes[0]
        _snake_case		:	List[Any]					     =     self.get_scheduler_config()
        _snake_case		:	Union[str, Any]					     =     scheduler_class(**lowercase__		)

        assert torch.sum(torch.abs(scheduler._get_variance(0     ,     0		) - 0.0		)		) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(420     ,     400		) - 0.14_771		)		) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(980     ,     960		) - 0.32_460		)		) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(0     ,     0		) - 0.0		)		) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(487     ,     486		) - 0.00_979		)		) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(999     ,     998		) - 0.02		)		) < 1E-5







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






        """simple docstring"""





        _snake_case		:	Any					     =     self.scheduler_classes[0]
        _snake_case		:	Optional[int]					     =     self.get_scheduler_config()
        _snake_case		:	Optional[int]					     =     scheduler_class(**lowercase__		)

        _snake_case     ,							_snake_case		:	Tuple					     =     10, 0.0
        scheduler.set_timesteps(lowercase__		)

        _snake_case		:	Tuple					     =     self.dummy_model()
        _snake_case		:	Any					     =     self.dummy_sample_deter
        _snake_case		:	Tuple					     =     self.dummy_sample_deter + 0.1
        _snake_case		:	Optional[Any]					     =     self.dummy_sample_deter - 0.1

        _snake_case		:	Optional[int]					     =     samplea.shape[0]
        _snake_case		:	List[str]					     =     torch.stack([samplea, samplea, samplea]     ,     dim=0		)
        _snake_case		:	Optional[Any]					     =     torch.arange(lowercase__		)[0:3, None].repeat(1     ,     lowercase__		)

        _snake_case		:	List[str]					     =     model(samples.flatten(0     ,     1		)     ,     timesteps.flatten(0     ,     1		)		)
        _snake_case		:	Dict					     =     scheduler.batch_step_no_noise(lowercase__     ,     timesteps.flatten(0     ,     1		)     ,     samples.flatten(0     ,     1		)     ,     lowercase__		)

        _snake_case		:	List[Any]					     =     torch.sum(torch.abs(lowercase__		)		)
        _snake_case		:	Optional[int]					     =     torch.mean(torch.abs(lowercase__		)		)

        assert abs(result_sum.item() - 1_147.7_904		) < 1E-2
        assert abs(result_mean.item() - 0.4_982		) < 1E-3







  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        _snake_case		:	List[Any]					     =     self.full_loop()

        _snake_case		:	int					     =     torch.sum(torch.abs(lowercase__		)		)
        _snake_case		:	int					     =     torch.mean(torch.abs(lowercase__		)		)

        assert abs(result_sum.item() - 172.0_067		) < 1E-2
        assert abs(result_mean.item() - 0.223_967		) < 1E-3







  def 				UpperCAmelCase_				(		self		)		->						str:






        """simple docstring"""





        _snake_case		:	Tuple					     =     self.full_loop(prediction_type='''v_prediction'''		)

        _snake_case		:	int					     =     torch.sum(torch.abs(lowercase__		)		)
        _snake_case		:	Optional[Any]					     =     torch.mean(torch.abs(lowercase__		)		)

        assert abs(result_sum.item() - 52.5_302		) < 1E-2
        assert abs(result_mean.item() - 0.0_684		) < 1E-3







  def 				UpperCAmelCase_				(		self		)		->						str:






        """simple docstring"""





        _snake_case		:	Tuple					     =     self.full_loop(set_alpha_to_one=lowercase__     ,     beta_start=0.01		)
        _snake_case		:	str					     =     torch.sum(torch.abs(lowercase__		)		)
        _snake_case		:	Optional[Any]					     =     torch.mean(torch.abs(lowercase__		)		)

        assert abs(result_sum.item() - 149.8_295		) < 1E-2
        assert abs(result_mean.item() - 0.1_951		) < 1E-3







  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        _snake_case		:	Optional[Any]					     =     self.full_loop(set_alpha_to_one=lowercase__     ,     beta_start=0.01		)
        _snake_case		:	Dict					     =     torch.sum(torch.abs(lowercase__		)		)
        _snake_case		:	Optional[Any]					     =     torch.mean(torch.abs(lowercase__		)		)

        assert abs(result_sum.item() - 149.0_784		) < 1E-2
        assert abs(result_mean.item() - 0.1_941		) < 1E-3









'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase					:		Dict		=							logging.get_logger(__name__)





class lowerCamelCase						(a__					):
  _lowercase  :	int       					=				["""pixel_values"""]







  def __init__(		self     ,     lowercase__ = True     ,     lowercase__ = 32     ,     lowercase__=PILImageResampling.BILINEAR     ,     lowercase__ = True     ,     **lowercase__     ,     )		->						None:






        """simple docstring"""





        _snake_case		:	Any					     =     do_resize
        _snake_case		:	List[str]					     =     do_rescale
        _snake_case		:	Any					     =     size_divisor
        _snake_case		:	Optional[Any]					     =     resample
        super().__init__(**lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__ = None     ,     **lowercase__		)		->						np.ndarray:






        """simple docstring"""





        _snake_case     ,							_snake_case		:	Dict					     =     get_image_size(lowercase__		)
        # Rounds the height and width down to the closest multiple of size_divisor
        _snake_case		:	Optional[int]					     =     height // size_divisor * size_divisor
        _snake_case		:	Dict					     =     width // size_divisor * size_divisor
        _snake_case		:	str					     =     resize(lowercase__     ,     (new_h, new_w)     ,     resample=lowercase__     ,     data_format=lowercase__     ,     **lowercase__		)
        return image







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__ = None     ,     **lowercase__		)		->						np.ndarray:






        """simple docstring"""





        return rescale(image=lowercase__     ,     scale=lowercase__     ,     data_format=lowercase__     ,     **lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__=None     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = ChannelDimension.FIRST     ,     **lowercase__     ,     )		->						BatchFeature:






        """simple docstring"""





        _snake_case		:	Any					     =     do_resize if do_resize is not None else self.do_resize
        _snake_case		:	List[Any]					     =     do_rescale if do_rescale is not None else self.do_rescale
        _snake_case		:	List[str]					     =     size_divisor if size_divisor is not None else self.size_divisor
        _snake_case		:	int					     =     resample if resample is not None else self.resample

        if do_resize and size_divisor is None:
              raise ValueError('''size_divisor is required for resizing'''		)

        _snake_case		:	Tuple					     =     make_list_of_images(lowercase__		)

        if not valid_images(lowercase__		):
              raise ValueError('''Invalid image(s)'''		)

        # All transformations expect numpy arrays.
        _snake_case		:	Tuple					     =     [to_numpy_array(lowercase__		) for img in images]

        if do_resize:
              _snake_case		:	Optional[int]					     =     [self.resize(lowercase__     ,     size_divisor=lowercase__     ,     resample=lowercase__		) for image in images]

        if do_rescale:
              _snake_case		:	Union[str, Any]					     =     [self.rescale(lowercase__     ,     scale=1 / 255		) for image in images]

        _snake_case		:	Union[str, Any]					     =     [to_channel_dimension_format(lowercase__     ,     lowercase__		) for image in images]

        _snake_case		:	List[str]					     =     {'''pixel_values''': images}
        return BatchFeature(data=lowercase__     ,     tensor_type=lowercase__		)


