
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
    import PIL

lowerCamelCase  =logging.get_logger(__name__)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__		):
      if isinstance(UpperCamelCase__			,		(list, tuple)		) and isinstance(videos[0]			,		(list, tuple)		) and is_valid_image(videos[0][0]		):
            return videos

      elif isinstance(UpperCamelCase__			,		(list, tuple)		) and is_valid_image(videos[0]		):
            return [videos]

      elif is_valid_image(UpperCamelCase__		):
            return [[videos]]

      raise ValueError(f'''Could not make batched video from {videos}'''		)




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	['''pixel_values''']






     def __init__(				self    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = 1 / 2_5_5    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> None:




           """simple docstring"""







           super().__init__(**__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Dict											=					size if size is not None else {'''shortest_edge''': 2_5_6}
           UpperCamelCase__     : Union[str, Any]											=					get_size_dict(__SCREAMING_SNAKE_CASE    ,    default_to_square=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : int											=					crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
           UpperCamelCase__     : Dict											=					get_size_dict(__SCREAMING_SNAKE_CASE    ,    param_name='''crop_size'''	)

           UpperCamelCase__     : List[Any]											=					do_resize
           UpperCamelCase__     : List[str]											=					size
           UpperCamelCase__     : Optional[int]											=					do_center_crop
           UpperCamelCase__     : str											=					crop_size
           UpperCamelCase__     : List[Any]											=					resample
           UpperCamelCase__     : str											=					do_rescale
           UpperCamelCase__     : Tuple											=					rescale_factor
           UpperCamelCase__     : str											=					offset
           UpperCamelCase__     : Dict											=					do_normalize
           UpperCamelCase__     : Tuple											=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           UpperCamelCase__     : Any											=					image_std if image_std is not None else IMAGENET_STANDARD_STD






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> np.ndarray:




           """simple docstring"""







           UpperCamelCase__     : Any											=					get_size_dict(__SCREAMING_SNAKE_CASE    ,    default_to_square=__SCREAMING_SNAKE_CASE	)
           if "shortest_edge" in size:
                 UpperCamelCase__     : Any											=					get_resize_output_image_size(__SCREAMING_SNAKE_CASE    ,    size['''shortest_edge''']    ,    default_to_square=__SCREAMING_SNAKE_CASE	)
           elif "height" in size and "width" in size:
                 UpperCamelCase__     : int											=					(size['''height'''], size['''width'''])
           else:
                 raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''	)
           return resize(__SCREAMING_SNAKE_CASE    ,    size=__SCREAMING_SNAKE_CASE    ,    resample=__SCREAMING_SNAKE_CASE    ,    data_format=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           UpperCamelCase__     : int											=					get_size_dict(__SCREAMING_SNAKE_CASE	)
           if "height" not in size or "width" not in size:
                 raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''	)
           return center_crop(__SCREAMING_SNAKE_CASE    ,    size=(size['''height'''], size['''width'''])    ,    data_format=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Optional[int]											=					image.astype(np.floataa	)
           if offset:
                 UpperCamelCase__     : int											=					image - (scale / 2)
           return rescale(__SCREAMING_SNAKE_CASE    ,    scale=__SCREAMING_SNAKE_CASE    ,    data_format=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







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






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = ChannelDimension.FIRST    ,    )	-> np.ndarray:




           """simple docstring"""







           if do_resize and size is None or resample is None:
                 raise ValueError('''Size and resample 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.'''	)

           if offset and not do_rescale:
                 raise ValueError('''For offset, do_rescale must also be set to True.'''	)

           # All transformations expect numpy arrays.
           UpperCamelCase__     : Dict											=					to_numpy_array(__SCREAMING_SNAKE_CASE	)

           if do_resize:
                 UpperCamelCase__     : List[str]											=					self.resize(image=__SCREAMING_SNAKE_CASE    ,    size=__SCREAMING_SNAKE_CASE    ,    resample=__SCREAMING_SNAKE_CASE	)

           if do_center_crop:
                 UpperCamelCase__     : int											=					self.center_crop(__SCREAMING_SNAKE_CASE    ,    size=__SCREAMING_SNAKE_CASE	)

           if do_rescale:
                 UpperCamelCase__     : str											=					self.rescale(image=__SCREAMING_SNAKE_CASE    ,    scale=__SCREAMING_SNAKE_CASE    ,    offset=__SCREAMING_SNAKE_CASE	)

           if do_normalize:
                 UpperCamelCase__     : Optional[int]											=					self.normalize(image=__SCREAMING_SNAKE_CASE    ,    mean=__SCREAMING_SNAKE_CASE    ,    std=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : int											=					to_channel_dimension_format(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)
           return image






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = ChannelDimension.FIRST    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> PIL.Image.Image:




           """simple docstring"""







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

           UpperCamelCase__     : List[Any]											=					size if size is not None else self.size
           UpperCamelCase__     : Union[str, Any]											=					get_size_dict(__SCREAMING_SNAKE_CASE    ,    default_to_square=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Union[str, Any]											=					crop_size if crop_size is not None else self.crop_size
           UpperCamelCase__     : str											=					get_size_dict(__SCREAMING_SNAKE_CASE    ,    param_name='''crop_size'''	)

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

           UpperCamelCase__     : List[str]											=					make_batched(__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Optional[int]											=					[
               [
                   self._preprocess_image(
                       image=__SCREAMING_SNAKE_CASE    ,    do_resize=__SCREAMING_SNAKE_CASE    ,    size=__SCREAMING_SNAKE_CASE    ,    resample=__SCREAMING_SNAKE_CASE    ,    do_center_crop=__SCREAMING_SNAKE_CASE    ,    crop_size=__SCREAMING_SNAKE_CASE    ,    do_rescale=__SCREAMING_SNAKE_CASE    ,    rescale_factor=__SCREAMING_SNAKE_CASE    ,    offset=__SCREAMING_SNAKE_CASE    ,    do_normalize=__SCREAMING_SNAKE_CASE    ,    image_mean=__SCREAMING_SNAKE_CASE    ,    image_std=__SCREAMING_SNAKE_CASE    ,    data_format=__SCREAMING_SNAKE_CASE    ,    )
                   for img in video
               ]
               for video in videos
           ]

           UpperCamelCase__     : Optional[int]											=					{'''pixel_values''': videos}
           return BatchFeature(data=__SCREAMING_SNAKE_CASE    ,    tensor_type=__SCREAMING_SNAKE_CASE	)




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	(DEISMultistepScheduler,)
     SCREAMING_SNAKE_CASE_		 =	(('''num_inference_steps''', 25),)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    **__SCREAMING_SNAKE_CASE	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					{
               '''num_train_timesteps''': 1_0_0_0,
               '''beta_start''': 0.0001,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
               '''solver_order''': 2,
           }

           config.update(**__SCREAMING_SNAKE_CASE	)
           return config






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE=0    ,    **__SCREAMING_SNAKE_CASE	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					dict(self.forward_default_kwargs	)
           UpperCamelCase__     : Dict											=					kwargs.pop('''num_inference_steps'''    ,    __SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Tuple											=					self.dummy_sample
           UpperCamelCase__     : Optional[int]											=					0.1 * sample
           UpperCamelCase__     : Tuple											=					[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 UpperCamelCase__     : Union[str, 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__     : Optional[Any]											=					dummy_past_residuals[: scheduler.config.solver_order]

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

                 UpperCamelCase__					,UpperCamelCase__     : Any											=					sample, sample
                 for t in range(__SCREAMING_SNAKE_CASE    ,    time_step + scheduler.config.solver_order + 1	):
                       UpperCamelCase__     : Union[str, Any]											=					scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	).prev_sample
                       UpperCamelCase__     : int											=					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  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           pass






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE=0    ,    **__SCREAMING_SNAKE_CASE	)	-> Optional[int]:




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					dict(self.forward_default_kwargs	)
           UpperCamelCase__     : Tuple											=					kwargs.pop('''num_inference_steps'''    ,    __SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Union[str, Any]											=					self.dummy_sample
           UpperCamelCase__     : List[Any]											=					0.1 * sample
           UpperCamelCase__     : Dict											=					[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 UpperCamelCase__     : Optional[int]											=					self.get_scheduler_config()
                 UpperCamelCase__     : Union[str, Any]											=					scheduler_class(**__SCREAMING_SNAKE_CASE	)
                 scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	)

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

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(__SCREAMING_SNAKE_CASE	)
                       UpperCamelCase__     : str											=					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[int]											=					dummy_past_residuals[: new_scheduler.config.solver_order]

                 UpperCamelCase__     : Optional[Any]											=					scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	).prev_sample
                 UpperCamelCase__     : Dict											=					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  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE=None    ,    **__SCREAMING_SNAKE_CASE	)	-> int:




           """simple docstring"""







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

           UpperCamelCase__     : List[str]											=					self.scheduler_classes[0]
           UpperCamelCase__     : Tuple											=					self.get_scheduler_config(**__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Any											=					scheduler_class(**__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					1_0
           UpperCamelCase__     : Tuple											=					self.dummy_model()
           UpperCamelCase__     : Dict											=					self.dummy_sample_deter
           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	)

           for i, t in enumerate(scheduler.timesteps	):
                 UpperCamelCase__     : List[Any]											=					model(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)
                 UpperCamelCase__     : Optional[Any]											=					scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	).prev_sample

           return sample






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           UpperCamelCase__     : Optional[int]											=					dict(self.forward_default_kwargs	)

           UpperCamelCase__     : List[Any]											=					kwargs.pop('''num_inference_steps'''    ,    __SCREAMING_SNAKE_CASE	)

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

                 UpperCamelCase__     : int											=					self.dummy_sample
                 UpperCamelCase__     : int											=					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__     : str											=					num_inference_steps

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

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

                 UpperCamelCase__     : Any											=					scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	).prev_sample
                 UpperCamelCase__     : 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  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					DEISMultistepScheduler(**self.get_scheduler_config()	)
           UpperCamelCase__     : Tuple											=					self.full_loop(scheduler=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Optional[Any]											=					torch.mean(torch.abs(__SCREAMING_SNAKE_CASE	)	)

           assert abs(result_mean.item() - 0.23916	) < 1e-3

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

           UpperCamelCase__     : Dict											=					self.full_loop(scheduler=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Optional[Any]											=					torch.mean(torch.abs(__SCREAMING_SNAKE_CASE	)	)

           assert abs(result_mean.item() - 0.23916	) < 1e-3






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




           """simple docstring"""







           for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict:




           """simple docstring"""







           self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE	)
           for order in [1, 2, 3]:
                 for solver_type in ["logrho"]:
                       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    ,    algorithm_type='''deis'''    ,    solver_order=__SCREAMING_SNAKE_CASE    ,    solver_type=__SCREAMING_SNAKE_CASE    ,    )






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




           """simple docstring"""







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






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           for algorithm_type in ["deis"]:
                 for solver_type in ["logrho"]:
                       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    ,    algorithm_type=__SCREAMING_SNAKE_CASE    ,    )
                                   UpperCamelCase__     : Any											=					self.full_loop(
                                       solver_order=__SCREAMING_SNAKE_CASE    ,    solver_type=__SCREAMING_SNAKE_CASE    ,    prediction_type=__SCREAMING_SNAKE_CASE    ,    algorithm_type=__SCREAMING_SNAKE_CASE    ,    )
                                   assert not torch.isnan(__SCREAMING_SNAKE_CASE	).any(), "Samples have nan numbers"






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE	)
           self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_forward(num_inference_steps=__SCREAMING_SNAKE_CASE    ,    time_step=0	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : str											=					self.full_loop()
           UpperCamelCase__     : Optional[int]											=					torch.mean(torch.abs(__SCREAMING_SNAKE_CASE	)	)

           assert abs(result_mean.item() - 0.23916	) < 1e-3






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : Any											=					self.full_loop(prediction_type='''v_prediction'''	)
           UpperCamelCase__     : List[Any]											=					torch.mean(torch.abs(__SCREAMING_SNAKE_CASE	)	)

           assert abs(result_mean.item() - 0.091	) < 1e-3






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					self.scheduler_classes[0]
           UpperCamelCase__     : Optional[Any]											=					self.get_scheduler_config(thresholding=__SCREAMING_SNAKE_CASE    ,    dynamic_thresholding_ratio=0	)
           UpperCamelCase__     : Tuple											=					scheduler_class(**__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					1_0
           UpperCamelCase__     : Dict											=					self.dummy_model()
           UpperCamelCase__     : Tuple											=					self.dummy_sample_deter.half()
           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	)

           for i, t in enumerate(scheduler.timesteps	):
                 UpperCamelCase__     : Any											=					model(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)
                 UpperCamelCase__     : Tuple											=					scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	).prev_sample

           assert sample.dtype == torch.floataa


