





"""simple docstring"""




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class 			__UpperCamelCase							(		a__		):
							lowerCamelCase  :   Optional[int]    =(DEISMultistepScheduler,)
							lowerCamelCase  :   Optional[Any]    =(("""num_inference_steps""", 25),)






							def        __a  (  self ,   **lowerCAmelCase__ )      ->					int:
								a :						Optional[Any]      =							{
								    "num_train_timesteps": 1000,
								    "beta_start": 0.0_001,
								    "beta_end": 0.02,
								    "beta_schedule": "linear",
								    "solver_order": 2,
								}

								config.update(**lowerCAmelCase__ )
								return config






							def        __a  (  self ,   lowerCAmelCase__=0 ,   **lowerCAmelCase__ )      ->					str:
								a :						int      =							dict(self.forward_default_kwargs )
								a :						Union[str, Any]      =							kwargs.pop("num_inference_steps" ,   lowerCAmelCase__ )
								a :						Any      =							self.dummy_sample
								a :						str      =							0.1 * sample
								a :						List[str]      =							[residual + 0.2, residual + 0.15, residual + 0.10]

								for scheduler_class in self.scheduler_classes:
									a :						List[Any]      =							self.get_scheduler_config(**lowerCAmelCase__ )
									a :						List[str]      =							scheduler_class(**lowerCAmelCase__ )
									scheduler.set_timesteps(lowerCAmelCase__ )
									# copy over dummy past residuals
									a :						Dict      =							dummy_past_residuals[: scheduler.config.solver_order]

									with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(lowerCAmelCase__ )
										a :						Dict      =							scheduler_class.from_pretrained(lowerCAmelCase__ )
										new_scheduler.set_timesteps(lowerCAmelCase__ )
										# copy over dummy past residuals
										a :						Optional[Any]      =							dummy_past_residuals[: new_scheduler.config.solver_order]

									a,			a :						int      =							sample, sample
									for t in range(lowerCAmelCase__ ,   time_step + scheduler.config.solver_order + 1 ):
										a :						int      =							scheduler.step(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   **lowerCAmelCase__ ).prev_sample
										a :						List[str]      =							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        __a  (  self )      ->					str:
								pass






							def        __a  (  self ,   lowerCAmelCase__=0 ,   **lowerCAmelCase__ )      ->					Dict:
								a :						int      =							dict(self.forward_default_kwargs )
								a :						Tuple      =							kwargs.pop("num_inference_steps" ,   lowerCAmelCase__ )
								a :						Optional[int]      =							self.dummy_sample
								a :						List[Any]      =							0.1 * sample
								a :						Dict      =							[residual + 0.2, residual + 0.15, residual + 0.10]

								for scheduler_class in self.scheduler_classes:
									a :						List[Any]      =							self.get_scheduler_config()
									a :						Optional[Any]      =							scheduler_class(**lowerCAmelCase__ )
									scheduler.set_timesteps(lowerCAmelCase__ )

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

									with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(lowerCAmelCase__ )
										a :						Dict      =							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)
										a :						Optional[Any]      =							dummy_past_residuals[: new_scheduler.config.solver_order]

									a :						List[str]      =							scheduler.step(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   **lowerCAmelCase__ ).prev_sample
									a :						Optional[int]      =							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        __a  (  self ,   lowerCAmelCase__=None ,   **lowerCAmelCase__ )      ->					int:
								if scheduler is None:
									a :						List[str]      =							self.scheduler_classes[0]
									a :						List[str]      =							self.get_scheduler_config(**lowerCAmelCase__ )
									a :						str      =							scheduler_class(**lowerCAmelCase__ )

								a :						List[Any]      =							self.scheduler_classes[0]
								a :						int      =							self.get_scheduler_config(**lowerCAmelCase__ )
								a :						List[Any]      =							scheduler_class(**lowerCAmelCase__ )

								a :						List[Any]      =							10
								a :						List[Any]      =							self.dummy_model()
								a :						List[str]      =							self.dummy_sample_deter
								scheduler.set_timesteps(lowerCAmelCase__ )

								for i, t in enumerate(scheduler.timesteps ):
									a :						str      =							model(lowerCAmelCase__ ,   lowerCAmelCase__ )
									a :						Tuple      =							scheduler.step(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ).prev_sample

								return sample






							def        __a  (  self )      ->					Union[str, Any]:
								a :						Dict      =							dict(self.forward_default_kwargs )

								a :						Dict      =							kwargs.pop("num_inference_steps" ,   lowerCAmelCase__ )

								for scheduler_class in self.scheduler_classes:
									a :						Tuple      =							self.get_scheduler_config()
									a :						int      =							scheduler_class(**lowerCAmelCase__ )

									a :						Any      =							self.dummy_sample
									a :						Any      =							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" ):
										a :						Any      =							num_inference_steps

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

									a :						Union[str, Any]      =							scheduler.timesteps[5]
									a :						List[str]      =							scheduler.timesteps[6]

									a :						Tuple      =							scheduler.step(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   **lowerCAmelCase__ ).prev_sample
									a :						Optional[int]      =							scheduler.step(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   **lowerCAmelCase__ ).prev_sample

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






							def        __a  (  self )      ->					Dict:
								# make sure that iterating over schedulers with same config names gives same results
								# for defaults
								a :						List[str]      =							DEISMultistepScheduler(**self.get_scheduler_config() )
								a :						Optional[Any]      =							self.full_loop(scheduler=lowerCAmelCase__ )
								a :						List[Any]      =							torch.mean(torch.abs(lowerCAmelCase__ ) )

								assert abs(result_mean.item() - 0.23_916 ) < 1E-3

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

								a :						List[str]      =							self.full_loop(scheduler=lowerCAmelCase__ )
								a :						Dict      =							torch.mean(torch.abs(lowerCAmelCase__ ) )

								assert abs(result_mean.item() - 0.23_916 ) < 1E-3






							def        __a  (  self )      ->					Dict:
								for timesteps in [25, 50, 100, 999, 1000]:
									self.check_over_configs(num_train_timesteps=lowerCAmelCase__ )






							def        __a  (  self )      ->					Optional[Any]:
								self.check_over_configs(thresholding=lowerCAmelCase__ )
								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=lowerCAmelCase__ ,   prediction_type=lowerCAmelCase__ ,   sample_max_value=lowerCAmelCase__ ,   algorithm_type="deis" ,   solver_order=lowerCAmelCase__ ,   solver_type=lowerCAmelCase__ ,   )






							def        __a  (  self )      ->					Any:
								for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=lowerCAmelCase__ )






							def        __a  (  self )      ->					Optional[int]:
								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=lowerCAmelCase__ ,   solver_type=lowerCAmelCase__ ,   prediction_type=lowerCAmelCase__ ,   algorithm_type=lowerCAmelCase__ ,   )
												a :						Optional[Any]      =							self.full_loop(
												    solver_order=lowerCAmelCase__ ,   solver_type=lowerCAmelCase__ ,   prediction_type=lowerCAmelCase__ ,   algorithm_type=lowerCAmelCase__ ,   )
												assert not torch.isnan(lowerCAmelCase__ ).any(), "Samples have nan numbers"






							def        __a  (  self )      ->					Dict:
								self.check_over_configs(lower_order_final=lowerCAmelCase__ )
								self.check_over_configs(lower_order_final=lowerCAmelCase__ )






							def        __a  (  self )      ->					Any:
								for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
									self.check_over_forward(num_inference_steps=lowerCAmelCase__ ,   time_step=0 )






							def        __a  (  self )      ->					Any:
								a :						Optional[Any]      =							self.full_loop()
								a :						Dict      =							torch.mean(torch.abs(lowerCAmelCase__ ) )

								assert abs(result_mean.item() - 0.23_916 ) < 1E-3






							def        __a  (  self )      ->					Optional[Any]:
								a :						Dict      =							self.full_loop(prediction_type="v_prediction" )
								a :						int      =							torch.mean(torch.abs(lowerCAmelCase__ ) )

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






							def        __a  (  self )      ->					Union[str, Any]:
								a :						Optional[Any]      =							self.scheduler_classes[0]
								a :						Optional[Any]      =							self.get_scheduler_config(thresholding=lowerCAmelCase__ ,   dynamic_thresholding_ratio=0 )
								a :						Tuple      =							scheduler_class(**lowerCAmelCase__ )

								a :						List[Any]      =							10
								a :						Union[str, Any]      =							self.dummy_model()
								a :						Union[str, Any]      =							self.dummy_sample_deter.half()
								scheduler.set_timesteps(lowerCAmelCase__ )

								for i, t in enumerate(scheduler.timesteps ):
									a :						List[Any]      =							model(lowerCAmelCase__ ,   lowerCAmelCase__ )
									a :						Dict      =							scheduler.step(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ).prev_sample

								assert sample.dtype == torch.floataa









"""simple docstring"""




from typing import Dict, List, Optional, Union

import numpy as np

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


if is_vision_available():
		import PIL


a		:					int	      =     logging.get_logger(__name__)


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






							def __init__(  self ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = PIL.Image.BICUBIC ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = 1 / 255 ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					None:
								super().__init__(**lowerCAmelCase__ )
								a :						Optional[int]      =							size if size is not None else {"height": 256, "width": 256}
								a :						Tuple      =							get_size_dict(lowerCAmelCase__ )
								a :						Any      =							crop_size if crop_size is not None else {"height": 224, "width": 224}
								a :						Dict      =							get_size_dict(lowerCAmelCase__ ,   param_name="crop_size" )

								a :						Tuple      =							do_resize
								a :						int      =							size
								a :						List[str]      =							resample
								a :						List[Any]      =							do_center_crop
								a :						List[Any]      =							crop_size
								a :						Union[str, Any]      =							do_rescale
								a :						List[Any]      =							rescale_factor
								a :						Union[str, Any]      =							do_normalize
								a :						Union[str, Any]      =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								a :						List[str]      =							image_std if image_std is not None else IMAGENET_STANDARD_STD






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = PIL.Image.BICUBIC ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					np.ndarray:
								a :						Optional[int]      =							get_size_dict(lowerCAmelCase__ )
								if "height" not in size or "width" not in size:
									raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""" )
								return resize(
								    lowerCAmelCase__ ,   size=(size["height"], size["width"]) ,   resample=lowerCAmelCase__ ,   data_format=lowerCAmelCase__ ,   **lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					np.ndarray:
								a :						List[str]      =							get_size_dict(lowerCAmelCase__ )
								if "height" not in size or "width" not in size:
									raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""" )
								return center_crop(lowerCAmelCase__ ,   size=(size["height"], size["width"]) ,   data_format=lowerCAmelCase__ ,   **lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					Union[str, Any]:
								return rescale(lowerCAmelCase__ ,   scale=lowerCAmelCase__ ,   data_format=lowerCAmelCase__ ,   **lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					np.ndarray:
								return normalize(lowerCAmelCase__ ,   mean=lowerCAmelCase__ ,   std=lowerCAmelCase__ ,   data_format=lowerCAmelCase__ ,   **lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__=None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = ChannelDimension.FIRST ,   **lowerCAmelCase__ ,   )      ->					PIL.Image.Image:
								a :						List[Any]      =							do_resize if do_resize is not None else self.do_resize
								a :						str      =							resample if resample is not None else self.resample
								a :						Dict      =							do_center_crop if do_center_crop is not None else self.do_center_crop
								a :						Union[str, Any]      =							do_rescale if do_rescale is not None else self.do_rescale
								a :						str      =							rescale_factor if rescale_factor is not None else self.rescale_factor
								a :						Dict      =							do_normalize if do_normalize is not None else self.do_normalize
								a :						List[str]      =							image_mean if image_mean is not None else self.image_mean
								a :						Union[str, Any]      =							image_std if image_std is not None else self.image_std

								a :						str      =							size if size is not None else self.size
								a :						Any      =							get_size_dict(lowerCAmelCase__ )
								a :						Dict      =							crop_size if crop_size is not None else self.crop_size
								a :						List[str]      =							get_size_dict(lowerCAmelCase__ ,   param_name="crop_size" )

								a :						str      =							make_list_of_images(lowerCAmelCase__ )

								if not valid_images(lowerCAmelCase__ ):
									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 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." )

								# All transformations expect numpy arrays.
								a :						Optional[Any]      =							[to_numpy_array(lowerCAmelCase__ ) for image in images]

								if do_resize:
									a :						Optional[int]      =							[self.resize(image=lowerCAmelCase__ ,   size=lowerCAmelCase__ ,   resample=lowerCAmelCase__ ) for image in images]

								if do_center_crop:
									a :						Dict      =							[self.center_crop(image=lowerCAmelCase__ ,   size=lowerCAmelCase__ ) for image in images]

								if do_rescale:
									a :						Any      =							[self.rescale(image=lowerCAmelCase__ ,   scale=lowerCAmelCase__ ) for image in images]

								if do_normalize:
									a :						Optional[Any]      =							[self.normalize(image=lowerCAmelCase__ ,   mean=lowerCAmelCase__ ,   std=lowerCAmelCase__ ) for image in images]

								a :						Optional[Any]      =							[to_channel_dimension_format(lowerCAmelCase__ ,   lowerCAmelCase__ ) for image in images]

								a :						Tuple      =							{"pixel_values": images}
								return BatchFeature(data=lowerCAmelCase__ ,   tensor_type=lowerCAmelCase__ )


