



import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class    SCREAMING_SNAKE_CASE			(							UpperCAmelCase__					):
							__lowerCamelCase	:	Optional[int]    =(UniPCMultistepScheduler,)
							__lowerCamelCase	:	Union[str, Any]    =(('num_inference_steps', 25),)





							def 						UpperCamelCase_					(   self		:		List[Any]      ,				**__lowercase		:		Optional[int]	):

											'''simple docstring'''
											__a       =		{
											    """num_train_timesteps""": 1000,
											    """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		:		List[Any]      ,				__lowercase		:		Optional[int]=0      ,				**__lowercase		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		dict(self.forward_default_kwargs	)
											__a       =		kwargs.pop("""num_inference_steps"""      ,				__lowerCAmelCase	)
											__a       =		self.dummy_sample
											__a       =		0.1 * sample
											__a       =		[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

															__a     ,					__a       =		sample, sample
															for t in range(__lowerCAmelCase      ,				time_step + scheduler.config.solver_order + 1	):
																			__a       =		scheduler.step(__lowerCAmelCase      ,				__lowerCAmelCase      ,				__lowerCAmelCase      ,				**__lowerCAmelCase	).prev_sample
																			__a       =		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		:		List[Any]      ,				__lowercase		:		Optional[Any]=0      ,				**__lowercase		:		List[Any]	):

											'''simple docstring'''
											__a       =		dict(self.forward_default_kwargs	)
											__a       =		kwargs.pop("""num_inference_steps"""      ,				__lowerCAmelCase	)
											__a       =		self.dummy_sample
											__a       =		0.1 * sample
											__a       =		[residual + 0.2, residual + 0.15, residual + 0.10]

											for scheduler_class in self.scheduler_classes:
															__a       =		self.get_scheduler_config()
															__a       =		scheduler_class(**__lowerCAmelCase	)
															scheduler.set_timesteps(__lowerCAmelCase	)

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

															with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(__lowerCAmelCase	)
																			__a       =		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       =		dummy_past_residuals[: new_scheduler.config.solver_order]

															__a       =		scheduler.step(__lowerCAmelCase      ,				__lowerCAmelCase      ,				__lowerCAmelCase      ,				**__lowerCAmelCase	).prev_sample
															__a       =		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		:		str      ,				__lowercase		:		Any=None      ,				**__lowercase		:		List[str]	):

											'''simple docstring'''






											if scheduler is None:
															__a       =		self.scheduler_classes[0]
															__a       =		self.get_scheduler_config(**__lowerCAmelCase	)
															__a       =		scheduler_class(**__lowerCAmelCase	)

											__a       =		self.scheduler_classes[0]
											__a       =		self.get_scheduler_config(**__lowerCAmelCase	)
											__a       =		scheduler_class(**__lowerCAmelCase	)

											__a       =		10
											__a       =		self.dummy_model()
											__a       =		self.dummy_sample_deter
											scheduler.set_timesteps(__lowerCAmelCase	)

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

											return sample





							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											__a       =		dict(self.forward_default_kwargs	)

											__a       =		kwargs.pop("""num_inference_steps"""      ,				__lowerCAmelCase	)

											for scheduler_class in self.scheduler_classes:
															__a       =		self.get_scheduler_config()
															__a       =		scheduler_class(**__lowerCAmelCase	)

															__a       =		self.dummy_sample
															__a       =		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       =		num_inference_steps

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

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

															__a       =		scheduler.step(__lowerCAmelCase      ,				__lowerCAmelCase      ,				__lowerCAmelCase      ,				**__lowerCAmelCase	).prev_sample
															__a       =		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		:		List[str]	):

											'''simple docstring'''
											__a       =		UniPCMultistepScheduler(**self.get_scheduler_config()	)
											__a       =		self.full_loop(scheduler=__lowerCAmelCase	)
											__a       =		torch.mean(torch.abs(__lowerCAmelCase	)	)

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

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

											__a       =		self.full_loop(scheduler=__lowerCAmelCase	)
											__a       =		torch.mean(torch.abs(__lowerCAmelCase	)	)

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





							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''






											for timesteps in [25, 50, 100, 999, 1000]:
															self.check_over_configs(num_train_timesteps=__lowerCAmelCase	)





							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											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		:		Optional[Any]	):

											'''simple docstring'''






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





							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''






											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      ,				)
																							__a       =		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		:		Optional[int]	):

											'''simple docstring'''
											self.check_over_configs(lower_order_final=__lowerCAmelCase	)
											self.check_over_configs(lower_order_final=__lowerCAmelCase	)





							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''






											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 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											__a       =		self.full_loop()
											__a       =		torch.mean(torch.abs(__lowerCAmelCase	)	)

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





							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											__a       =		self.full_loop(prediction_type="""v_prediction"""	)
											__a       =		torch.mean(torch.abs(__lowerCAmelCase	)	)

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





							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											__a       =		self.scheduler_classes[0]
											__a       =		self.get_scheduler_config(thresholding=__lowerCAmelCase      ,				dynamic_thresholding_ratio=0	)
											__a       =		scheduler_class(**__lowerCAmelCase	)

											__a       =		10
											__a       =		self.dummy_model()
											__a       =		self.dummy_sample_deter.half()
											scheduler.set_timesteps(__lowerCAmelCase	)

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

											assert sample.dtype == torch.floataa







							def 						UpperCamelCase_					(   self		:		List[str]      ,				**__lowercase		:		int	):

											'''simple docstring'''






											for scheduler_class in self.scheduler_classes:
															__a       =		self.get_scheduler_config(**__lowerCAmelCase	)
															__a       =		scheduler_class(**__lowerCAmelCase	)

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




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


lowerCamelCase__			= logging.get_logger(__name__)






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	str    =['pixel_values']





							def __init__(   self		:		str      ,				__lowercase		:		bool = True      ,				__lowercase		:		Dict[str, int] = None      ,				__lowercase		:		PILImageResampling = PIL.Image.BICUBIC      ,				__lowercase		:		bool = True      ,				__lowercase		:		Dict[str, int] = None      ,				__lowercase		:		Union[int, float] = 1 / 255      ,				__lowercase		:		bool = True      ,				__lowercase		:		bool = True      ,				__lowercase		:		Optional[Union[float, List[float]]] = None      ,				__lowercase		:		Optional[Union[float, List[float]]] = None      ,				**__lowercase		:		Any      ,				):

											'''simple docstring'''
											super().__init__(**__lowercase	)
											__a       =		size if size is not None else {"""height""": 256, """width""": 256}
											__a       =		get_size_dict(__lowercase	)
											__a       =		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
											__a       =		get_size_dict(__lowercase      ,				param_name="""crop_size"""	)

											__a       =		do_resize
											__a       =		size
											__a       =		resample
											__a       =		do_center_crop
											__a       =		crop_size
											__a       =		do_rescale
											__a       =		rescale_factor
											__a       =		do_normalize
											__a       =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__a       =		image_std if image_std is not None else IMAGENET_STANDARD_STD





							def 						UpperCamelCase_					(   self		:		List[Any]      ,				__lowercase		:		np.ndarray      ,				__lowercase		:		Dict[str, int]      ,				__lowercase		:		PILImageResampling = PIL.Image.BICUBIC      ,				__lowercase		:		Optional[Union[str, ChannelDimension]] = None      ,				**__lowercase		:		Union[str, Any]      ,				):

											'''simple docstring'''
											__a       =		get_size_dict(__lowercase	)
											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(
											    __lowercase      ,				size=(size["""height"""], size["""width"""])      ,				resample=__lowercase      ,				data_format=__lowercase      ,				**__lowercase	)





							def 						UpperCamelCase_					(   self		:		Dict      ,				__lowercase		:		np.ndarray      ,				__lowercase		:		Dict[str, int]      ,				__lowercase		:		Optional[Union[str, ChannelDimension]] = None      ,				**__lowercase		:		str      ,				):

											'''simple docstring'''
											__a       =		get_size_dict(__lowercase	)
											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(__lowercase      ,				size=(size["""height"""], size["""width"""])      ,				data_format=__lowercase      ,				**__lowercase	)





							def 						UpperCamelCase_					(   self		:		int      ,				__lowercase		:		np.ndarray      ,				__lowercase		:		Union[int, float]      ,				__lowercase		:		Optional[Union[str, ChannelDimension]] = None      ,				**__lowercase		:		List[str]      ,				):

											'''simple docstring'''
											return rescale(__lowercase      ,				scale=__lowercase      ,				data_format=__lowercase      ,				**__lowercase	)





							def 						UpperCamelCase_					(   self		:		Tuple      ,				__lowercase		:		np.ndarray      ,				__lowercase		:		Union[float, List[float]]      ,				__lowercase		:		Union[float, List[float]]      ,				__lowercase		:		Optional[Union[str, ChannelDimension]] = None      ,				**__lowercase		:		str      ,				):

											'''simple docstring'''
											return normalize(__lowercase      ,				mean=__lowercase      ,				std=__lowercase      ,				data_format=__lowercase      ,				**__lowercase	)







							def 						UpperCamelCase_					(   self		:		Optional[Any]      ,				__lowercase		:		ImageInput      ,				__lowercase		:		bool = None      ,				__lowercase		:		Dict[str, int] = None      ,				__lowercase		:		Dict=None      ,				__lowercase		:		bool = None      ,				__lowercase		:		Dict[str, int] = None      ,				__lowercase		:		bool = None      ,				__lowercase		:		float = None      ,				__lowercase		:		bool = None      ,				__lowercase		:		Optional[Union[float, List[float]]] = None      ,				__lowercase		:		Optional[Union[float, List[float]]] = None      ,				__lowercase		:		Optional[Union[str, TensorType]] = None      ,				__lowercase		:		ChannelDimension = ChannelDimension.FIRST      ,				**__lowercase		:		Optional[Any]      ,				):

											'''simple docstring'''
											__a       =		do_resize if do_resize is not None else self.do_resize
											__a       =		resample if resample is not None else self.resample
											__a       =		do_center_crop if do_center_crop is not None else self.do_center_crop
											__a       =		do_rescale if do_rescale is not None else self.do_rescale
											__a       =		rescale_factor if rescale_factor is not None else self.rescale_factor
											__a       =		do_normalize if do_normalize is not None else self.do_normalize
											__a       =		image_mean if image_mean is not None else self.image_mean
											__a       =		image_std if image_std is not None else self.image_std

											__a       =		size if size is not None else self.size
											__a       =		get_size_dict(__lowercase	)
											__a       =		crop_size if crop_size is not None else self.crop_size
											__a       =		get_size_dict(__lowercase      ,				param_name="""crop_size"""	)

											__a       =		make_list_of_images(__lowercase	)

											if not valid_images(__lowercase	):
															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       =		[to_numpy_array(__lowercase	) for image in images]

											if do_resize:
															__a       =		[self.resize(image=__lowercase      ,				size=__lowercase      ,				resample=__lowercase	) for image in images]

											if do_center_crop:
															__a       =		[self.center_crop(image=__lowercase      ,				size=__lowercase	) for image in images]

											if do_rescale:
															__a       =		[self.rescale(image=__lowercase      ,				scale=__lowercase	) for image in images]

											if do_normalize:
															__a       =		[self.normalize(image=__lowercase      ,				mean=__lowercase      ,				std=__lowercase	) for image in images]

											__a       =		[to_channel_dimension_format(__lowercase      ,				__lowercase	) for image in images]

											__a       =		{"""pixel_values""": images}
											return BatchFeature(data=__lowercase      ,				tensor_type=__lowercase	)
