




from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_lowerCamelCase      :  List[str]      						=						logging.get_logger(__name__)
class        __snake_case  (_a		):
				lowerCAmelCase__				=     ["pixel_values"]



				def __init__(      self      :		int		,	_UpperCAmelCase      :		bool = True		,	_UpperCAmelCase      :		Dict[str, int] = None		,	_UpperCAmelCase      :		PILImageResampling = PILImageResampling.BICUBIC		,	_UpperCAmelCase      :		bool = True		,	_UpperCAmelCase      :		Dict[str, int] = None		,	_UpperCAmelCase      :		bool = True		,	_UpperCAmelCase      :		Union[int, float] = 1 / 255		,	_UpperCAmelCase      :		bool = True		,	_UpperCAmelCase      :		Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN		,	_UpperCAmelCase      :		Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD		,	**_UpperCAmelCase      :		Optional[int]		,	)  ->    None:





								'''simple docstring'''




								super().__init__(**_UpperCAmelCase      )
								_lowerCAmelCase    :    Union[str, Any]										=      size if size is not None else {"""shortest_edge""": 224}
								_lowerCAmelCase    :    str										=      get_size_dict(_UpperCAmelCase		,	default_to_square=_UpperCAmelCase      )
								_lowerCAmelCase    :    int										=      crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
								_lowerCAmelCase    :    List[Any]										=      get_size_dict(_UpperCAmelCase		,	param_name="""crop_size"""      )

								_lowerCAmelCase    :    str										=      do_resize
								_lowerCAmelCase    :    List[Any]										=      size
								_lowerCAmelCase    :    Tuple										=      resample
								_lowerCAmelCase    :    Any										=      do_center_crop
								_lowerCAmelCase    :    List[str]										=      crop_size
								_lowerCAmelCase    :    Any										=      do_rescale
								_lowerCAmelCase    :    Union[str, Any]										=      rescale_factor
								_lowerCAmelCase    :    str										=      do_normalize
								_lowerCAmelCase    :    Any										=      image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
								_lowerCAmelCase    :    Union[str, Any]										=      image_std if image_std is not None else IMAGENET_DEFAULT_STD



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]		,	_UpperCAmelCase      :		np.ndarray		,	_UpperCAmelCase      :		Dict[str, int]		,	_UpperCAmelCase      :		PILImageResampling = PILImageResampling.BICUBIC		,	_UpperCAmelCase      :		Optional[Union[str, ChannelDimension]] = None		,	**_UpperCAmelCase      :		int		,	)  ->    np.ndarray:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      get_size_dict(_UpperCAmelCase		,	default_to_square=_UpperCAmelCase      )
								# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
								if "shortest_edge" in size:
												_lowerCAmelCase    :    Union[str, Any]										=      int((256 / 224) * size["""shortest_edge"""]      )
												_lowerCAmelCase    :    int										=      get_resize_output_image_size(_UpperCAmelCase		,	size=_UpperCAmelCase		,	default_to_square=_UpperCAmelCase      )
												_lowerCAmelCase    :    Union[str, Any]										=      {"""height""": output_size[0], """width""": output_size[1]}
								if "height" not in size_dict or "width" not in size_dict:
												raise ValueError(
												    f"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"      )
								return resize(
								    _UpperCAmelCase		,	size=(size_dict["""height"""], size_dict["""width"""])		,	resample=_UpperCAmelCase		,	data_format=_UpperCAmelCase		,	**_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]		,	_UpperCAmelCase      :		np.ndarray		,	_UpperCAmelCase      :		Dict[str, int]		,	_UpperCAmelCase      :		Optional[Union[str, ChannelDimension]] = None		,	**_UpperCAmelCase      :		Optional[Any]		,	)  ->    np.ndarray:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      get_size_dict(_UpperCAmelCase      )
								if "height" not in size or "width" not in size:
												raise ValueError(f"Size dict must have keys 'height' and 'width'. Got {size.keys()}"      )
								return center_crop(_UpperCAmelCase		,	size=(size["""height"""], size["""width"""])		,	data_format=_UpperCAmelCase		,	**_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		int		,	_UpperCAmelCase      :		np.ndarray		,	_UpperCAmelCase      :		Union[int, float]		,	_UpperCAmelCase      :		Optional[Union[str, ChannelDimension]] = None		,	**_UpperCAmelCase      :		Optional[Any]		,	)  ->    np.ndarray:





								'''simple docstring'''




								return rescale(_UpperCAmelCase		,	scale=_UpperCAmelCase		,	data_format=_UpperCAmelCase		,	**_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]		,	_UpperCAmelCase      :		np.ndarray		,	_UpperCAmelCase      :		Union[float, List[float]]		,	_UpperCAmelCase      :		Union[float, List[float]]		,	_UpperCAmelCase      :		Optional[Union[str, ChannelDimension]] = None		,	**_UpperCAmelCase      :		List[Any]		,	)  ->    np.ndarray:





								'''simple docstring'''




								return normalize(_UpperCAmelCase		,	mean=_UpperCAmelCase		,	std=_UpperCAmelCase		,	data_format=_UpperCAmelCase		,	**_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]		,	_UpperCAmelCase      :		ImageInput		,	_UpperCAmelCase      :		Optional[bool] = None		,	_UpperCAmelCase      :		Optional[Dict[str, int]] = None		,	_UpperCAmelCase      :		PILImageResampling = None		,	_UpperCAmelCase      :		Optional[bool] = None		,	_UpperCAmelCase      :		Optional[Dict[str, int]] = None		,	_UpperCAmelCase      :		Optional[bool] = None		,	_UpperCAmelCase      :		Optional[float] = None		,	_UpperCAmelCase      :		Optional[bool] = None		,	_UpperCAmelCase      :		Optional[Union[float, Iterable[float]]] = None		,	_UpperCAmelCase      :		Optional[Union[float, Iterable[float]]] = None		,	_UpperCAmelCase      :		Optional[TensorType] = None		,	_UpperCAmelCase      :		ChannelDimension = ChannelDimension.FIRST		,	**_UpperCAmelCase      :		Optional[Any]		,	)  ->    BatchFeature:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      do_resize if do_resize is not None else self.do_resize
								_lowerCAmelCase    :    str										=      resample if resample is not None else self.resample
								_lowerCAmelCase    :    List[str]										=      do_center_crop if do_center_crop is not None else self.do_center_crop
								_lowerCAmelCase    :    Optional[int]										=      do_rescale if do_rescale is not None else self.do_rescale
								_lowerCAmelCase    :    Tuple										=      rescale_factor if rescale_factor is not None else self.rescale_factor
								_lowerCAmelCase    :    Dict										=      do_normalize if do_normalize is not None else self.do_normalize
								_lowerCAmelCase    :    Dict										=      image_mean if image_mean is not None else self.image_mean
								_lowerCAmelCase    :    str										=      image_std if image_std is not None else self.image_std

								_lowerCAmelCase    :    Tuple										=      size if size is not None else self.size
								_lowerCAmelCase    :    List[Any]										=      get_size_dict(_UpperCAmelCase		,	default_to_square=_UpperCAmelCase      )
								_lowerCAmelCase    :    List[str]										=      crop_size if crop_size is not None else self.crop_size
								_lowerCAmelCase    :    List[str]										=      get_size_dict(_UpperCAmelCase		,	param_name="""crop_size"""      )

								_lowerCAmelCase    :    Optional[int]										=      make_list_of_images(_UpperCAmelCase      )

								if not valid_images(_UpperCAmelCase      ):
												raise ValueError(
												    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
												    """torch.Tensor, tf.Tensor or jax.ndarray."""      )

								if do_resize and size is None:
												raise ValueError("""Size must be specified if do_resize is True."""      )

								if do_center_crop and crop_size is None:
												raise ValueError("""Crop size must be specified if do_center_crop is True."""      )

								if do_rescale and rescale_factor is None:
												raise ValueError("""Rescale factor must be specified if do_rescale is True."""      )

								if do_normalize and (image_mean is None or image_std is None):
												raise ValueError("""Image mean and std must be specified if do_normalize is True."""      )

								# All transformations expect numpy arrays.
								_lowerCAmelCase    :    Union[str, Any]										=      [to_numpy_array(_UpperCAmelCase      ) for image in images]

								if do_resize:
												_lowerCAmelCase    :    Union[str, Any]										=      [self.resize(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      ) for image in images]

								if do_center_crop:
												_lowerCAmelCase    :    Optional[Any]										=      [self.center_crop(_UpperCAmelCase		,	_UpperCAmelCase      ) for image in images]

								if do_rescale:
												_lowerCAmelCase    :    Dict										=      [self.rescale(_UpperCAmelCase		,	_UpperCAmelCase      ) for image in images]

								if do_normalize:
												_lowerCAmelCase    :    List[str]										=      [self.normalize(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      ) for image in images]

								_lowerCAmelCase    :    List[Any]										=      [to_channel_dimension_format(_UpperCAmelCase		,	_UpperCAmelCase      ) for image in images]

								_lowerCAmelCase    :    Tuple										=      {"""pixel_values""": images}
								return BatchFeature(data=_UpperCAmelCase		,	tensor_type=_UpperCAmelCase      )







import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class        __snake_case  (_a		):
				lowerCAmelCase__				=     (PNDMScheduler,)
				lowerCAmelCase__				=     (("num_inference_steps", 5_0),)



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]		,	**_UpperCAmelCase      :		Optional[int]      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      {
								    """num_train_timesteps""": 1000,
								    """beta_start""": 0.0001,
								    """beta_end""": 0.02,
								    """beta_schedule""": """linear""",
								}

								config.update(**_UpperCAmelCase      )
								return config



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





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      dict(self.forward_default_kwargs      )
								_lowerCAmelCase    :    str										=      kwargs.pop("""num_inference_steps"""		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[Any]										=      self.dummy_sample
								_lowerCAmelCase    :    List[str]										=      0.1 * sample
								_lowerCAmelCase    :    Tuple										=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												_lowerCAmelCase    :    Any										=      self.get_scheduler_config(**_UpperCAmelCase      )
												_lowerCAmelCase    :    Optional[Any]										=      scheduler_class(**_UpperCAmelCase      )
												scheduler.set_timesteps(_UpperCAmelCase      )
												# copy over dummy past residuals
												_lowerCAmelCase    :    Tuple										=      dummy_past_residuals[:]

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

												_lowerCAmelCase    :    int										=      scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    List[str]										=      new_scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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

												_lowerCAmelCase    :    Tuple										=      scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    Optional[int]										=      new_scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    str:





								'''simple docstring'''




								pass



				def       SCREAMING_SNAKE_CASE       (      self      :		int		,	_UpperCAmelCase      :		Any=0		,	**_UpperCAmelCase      :		Optional[int]      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      dict(self.forward_default_kwargs      )
								_lowerCAmelCase    :    Any										=      kwargs.pop("""num_inference_steps"""		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    List[Any]										=      self.dummy_sample
								_lowerCAmelCase    :    Dict										=      0.1 * sample
								_lowerCAmelCase    :    Dict										=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												_lowerCAmelCase    :    Any										=      self.get_scheduler_config()
												_lowerCAmelCase    :    Optional[Any]										=      scheduler_class(**_UpperCAmelCase      )
												scheduler.set_timesteps(_UpperCAmelCase      )

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

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(_UpperCAmelCase      )
																_lowerCAmelCase    :    List[Any]										=      scheduler_class.from_pretrained(_UpperCAmelCase      )
																# copy over dummy past residuals
																new_scheduler.set_timesteps(_UpperCAmelCase      )

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

												_lowerCAmelCase    :    Tuple										=      scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    str										=      new_scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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

												_lowerCAmelCase    :    Any										=      scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    Any										=      new_scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	**_UpperCAmelCase      :		Dict      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      self.scheduler_classes[0]
								_lowerCAmelCase    :    int										=      self.get_scheduler_config(**_UpperCAmelCase      )
								_lowerCAmelCase    :    List[str]										=      scheduler_class(**_UpperCAmelCase      )

								_lowerCAmelCase    :    List[Any]										=      10
								_lowerCAmelCase    :    Any										=      self.dummy_model()
								_lowerCAmelCase    :    Tuple										=      self.dummy_sample_deter
								scheduler.set_timesteps(_UpperCAmelCase      )

								for i, t in enumerate(scheduler.prk_timesteps      ):
												_lowerCAmelCase    :    List[Any]										=      model(_UpperCAmelCase		,	_UpperCAmelCase      )
												_lowerCAmelCase    :    Tuple										=      scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      ).prev_sample

								for i, t in enumerate(scheduler.plms_timesteps      ):
												_lowerCAmelCase    :    List[Any]										=      model(_UpperCAmelCase		,	_UpperCAmelCase      )
												_lowerCAmelCase    :    Dict										=      scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      ).prev_sample

								return sample



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      dict(self.forward_default_kwargs      )

								_lowerCAmelCase    :    int										=      kwargs.pop("""num_inference_steps"""		,	_UpperCAmelCase      )

								for scheduler_class in self.scheduler_classes:
												_lowerCAmelCase    :    Tuple										=      self.get_scheduler_config()
												_lowerCAmelCase    :    Union[str, Any]										=      scheduler_class(**_UpperCAmelCase      )

												_lowerCAmelCase    :    str										=      self.dummy_sample
												_lowerCAmelCase    :    str										=      0.1 * sample

												if num_inference_steps is not None and hasattr(_UpperCAmelCase		,	"""set_timesteps"""      ):
																scheduler.set_timesteps(_UpperCAmelCase      )
												elif num_inference_steps is not None and not hasattr(_UpperCAmelCase		,	"""set_timesteps"""      ):
																_lowerCAmelCase    :    Tuple										=      num_inference_steps

												# copy over dummy past residuals (must be done after set_timesteps)
												_lowerCAmelCase    :    List[Any]										=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
												_lowerCAmelCase    :    List[str]										=      dummy_past_residuals[:]

												_lowerCAmelCase    :    Optional[int]										=      scheduler.step_prk(_UpperCAmelCase		,	0		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    Optional[int]										=      scheduler.step_prk(_UpperCAmelCase		,	1		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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

												_lowerCAmelCase    :    Tuple										=      scheduler.step_plms(_UpperCAmelCase		,	0		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    Optional[Any]										=      scheduler.step_plms(_UpperCAmelCase		,	1		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    List[Any]:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    Optional[int]:





								'''simple docstring'''




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

								_lowerCAmelCase    :    Dict										=      self.scheduler_classes[0]
								_lowerCAmelCase    :    Any										=      self.get_scheduler_config(steps_offset=1      )
								_lowerCAmelCase    :    Any										=      scheduler_class(**_UpperCAmelCase      )
								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       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    List[Any]:





								'''simple docstring'''




								for beta_start, beta_end in zip([0.0001, 0.001]		,	[0.002, 0.02]      ):
												self.check_over_configs(beta_start=_UpperCAmelCase		,	beta_end=_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    int:





								'''simple docstring'''




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



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





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    List[str]:





								'''simple docstring'''




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



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





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      27

								for scheduler_class in self.scheduler_classes:
												_lowerCAmelCase    :    Optional[Any]										=      self.dummy_sample
												_lowerCAmelCase    :    Any										=      0.1 * sample

												_lowerCAmelCase    :    List[str]										=      self.get_scheduler_config()
												_lowerCAmelCase    :    Any										=      scheduler_class(**_UpperCAmelCase      )

												scheduler.set_timesteps(_UpperCAmelCase      )

												# 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]      ):
																_lowerCAmelCase    :    List[Any]										=      scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      ).prev_sample



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





								'''simple docstring'''




								with self.assertRaises(_UpperCAmelCase      ):
												_lowerCAmelCase    :    Tuple										=      self.scheduler_classes[0]
												_lowerCAmelCase    :    List[str]										=      self.get_scheduler_config()
												_lowerCAmelCase    :    Any										=      scheduler_class(**_UpperCAmelCase      )

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



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      self.full_loop()
								_lowerCAmelCase    :    str										=      torch.sum(torch.abs(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    List[Any]										=      torch.mean(torch.abs(_UpperCAmelCase      )      )

								assert abs(result_sum.item() - 198.1318      ) < 1E-2
								assert abs(result_mean.item() - 0.2580      ) < 1E-3



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





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      self.full_loop(prediction_type="""v_prediction"""      )
								_lowerCAmelCase    :    Optional[int]										=      torch.sum(torch.abs(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    Optional[int]										=      torch.mean(torch.abs(_UpperCAmelCase      )      )

								assert abs(result_sum.item() - 67.3986      ) < 1E-2
								assert abs(result_mean.item() - 0.0878      ) < 1E-3



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    Optional[Any]:





								'''simple docstring'''




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

								assert abs(result_sum.item() - 230.0399      ) < 1E-2
								assert abs(result_mean.item() - 0.2995      ) < 1E-3



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





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      self.full_loop(set_alpha_to_one=_UpperCAmelCase		,	beta_start=0.01      )
								_lowerCAmelCase    :    List[Any]										=      torch.sum(torch.abs(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    Tuple										=      torch.mean(torch.abs(_UpperCAmelCase      )      )

								assert abs(result_sum.item() - 186.9482      ) < 1E-2
								assert abs(result_mean.item() - 0.2434      ) < 1E-3

