'''simple docstring'''




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Optional[int]						=			(UnCLIPScheduler,)



			def 				a_			(  self			,       **__lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    {
					    """num_train_timesteps""": 1000,
					    """variance_type""": """fixed_small_log""",
					    """clip_sample""": True,
					    """clip_sample_range""": 1.0,
					    """prediction_type""": """epsilon""",
					}

					config.update(**__lowerCAmelCase)
					return config



			def 				a_			(  self):




					"""simple docstring"""


					for timesteps in [1, 5, 100, 1000]:
							self.check_over_configs(num_train_timesteps=__lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


					for variance in ["fixed_small_log", "learned_range"]:
							self.check_over_configs(variance_type=__lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


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



			def 				a_			(  self):




					"""simple docstring"""


					for clip_sample_range in [1, 5, 10, 20]:
							self.check_over_configs(clip_sample_range=__lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


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



			def 				a_			(  self):




					"""simple docstring"""


					for time_step in [0, 500, 999]:
							for prev_timestep in [None, 5, 100, 250, 500, 750]:
									if prev_timestep is not None and prev_timestep >= time_step:
											continue

									self.check_over_forward(time_step=__lowerCAmelCase			,       prev_timestep=__lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.scheduler_classes[0]
					lowerCAmelCase     =    self.get_scheduler_config(variance_type="""fixed_small_log""")
					lowerCAmelCase     =    scheduler_class(**__lowerCAmelCase)

					assert torch.sum(torch.abs(scheduler._get_variance(0) - 1.0_0_0_0E-1_0)) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0549625)) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.9994987)) < 1E-5



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.scheduler_classes[0]
					lowerCAmelCase     =    self.get_scheduler_config(variance_type="""learned_range""")
					lowerCAmelCase     =    scheduler_class(**__lowerCAmelCase)

					lowerCAmelCase     =    0.5

					assert scheduler._get_variance(1			,       predicted_variance=__lowerCAmelCase) - -10.1712790 < 1E-5
					assert scheduler._get_variance(487			,       predicted_variance=__lowerCAmelCase) - -5.7998052 < 1E-5
					assert scheduler._get_variance(999			,       predicted_variance=__lowerCAmelCase) - -0.0010011 < 1E-5



			def 				a_			(  self):




					"""simple docstring"""


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

					lowerCAmelCase     =    scheduler.timesteps

					lowerCAmelCase     =    self.dummy_model()
					lowerCAmelCase     =    self.dummy_sample_deter
					lowerCAmelCase     =    torch.manual_seed(0)

					for i, t in enumerate(__lowerCAmelCase):
							# 1. predict noise residual
							lowerCAmelCase     =    model(__lowerCAmelCase			,       __lowerCAmelCase)

							# 2. predict previous mean of sample x_t-1
							lowerCAmelCase     =    scheduler.step(__lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       generator=__lowerCAmelCase).prev_sample

							lowerCAmelCase     =    pred_prev_sample

					lowerCAmelCase     =    torch.sum(torch.abs(__lowerCAmelCase))
					lowerCAmelCase     =    torch.mean(torch.abs(__lowerCAmelCase))

					assert abs(result_sum.item() - 252.2682495) < 1E-2
					assert abs(result_mean.item() - 0.3284743) < 1E-3



			def 				a_			(  self):




					"""simple docstring"""


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

					scheduler.set_timesteps(25)

					lowerCAmelCase     =    scheduler.timesteps

					lowerCAmelCase     =    self.dummy_model()
					lowerCAmelCase     =    self.dummy_sample_deter
					lowerCAmelCase     =    torch.manual_seed(0)

					for i, t in enumerate(__lowerCAmelCase):
							# 1. predict noise residual
							lowerCAmelCase     =    model(__lowerCAmelCase			,       __lowerCAmelCase)

							if i + 1 == timesteps.shape[0]:
									lowerCAmelCase     =    None
							else:
									lowerCAmelCase     =    timesteps[i + 1]

							# 2. predict previous mean of sample x_t-1
							lowerCAmelCase     =    scheduler.step(
							    __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       prev_timestep=__lowerCAmelCase			,       generator=__lowerCAmelCase).prev_sample

							lowerCAmelCase     =    pred_prev_sample

					lowerCAmelCase     =    torch.sum(torch.abs(__lowerCAmelCase))
					lowerCAmelCase     =    torch.mean(torch.abs(__lowerCAmelCase))

					assert abs(result_sum.item() - 258.2044983) < 1E-2
					assert abs(result_mean.item() - 0.3362038) < 1E-3



			def 				a_			(  self):




					"""simple docstring"""


					pass







			def 				a_			(  self):




					"""simple docstring"""


					pass





'''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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_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


__lowercase        =    logging.get_logger(__name__)






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[Any]						=			['''pixel_values''']



			def __init__(  self			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       __lowerCAmelCase = PILImageResampling.BICUBIC			,       __lowerCAmelCase = True			,       __lowerCAmelCase = 1 / 255			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = True			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__(**__lowerCAmelCase)
					lowerCAmelCase     =    size if size is not None else {"""height""": 384, """width""": 384}
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase			,       default_to_square=__lowerCAmelCase)

					lowerCAmelCase     =    do_resize
					lowerCAmelCase     =    size
					lowerCAmelCase     =    resample
					lowerCAmelCase     =    do_rescale
					lowerCAmelCase     =    rescale_factor
					lowerCAmelCase     =    do_normalize
					lowerCAmelCase     =    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
					lowerCAmelCase     =    image_std if image_std is not None else OPENAI_CLIP_STD
					lowerCAmelCase     =    do_convert_rgb



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = PILImageResampling.BICUBIC			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase			,       default_to_square=__lowerCAmelCase)
					if "height" not in size or "width" not in size:
							raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
					lowerCAmelCase     =    (size["""height"""], size["""width"""])
					return resize(__lowerCAmelCase			,       size=__lowerCAmelCase			,       resample=__lowerCAmelCase			,       data_format=__lowerCAmelCase			,       **__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


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



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					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 = ChannelDimension.FIRST			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    do_resize if do_resize is not None else self.do_resize
					lowerCAmelCase     =    resample if resample is not None else self.resample
					lowerCAmelCase     =    do_rescale if do_rescale is not None else self.do_rescale
					lowerCAmelCase     =    rescale_factor if rescale_factor is not None else self.rescale_factor
					lowerCAmelCase     =    do_normalize if do_normalize is not None else self.do_normalize
					lowerCAmelCase     =    image_mean if image_mean is not None else self.image_mean
					lowerCAmelCase     =    image_std if image_std is not None else self.image_std
					lowerCAmelCase     =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

					lowerCAmelCase     =    size if size is not None else self.size
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase			,       default_to_square=__lowerCAmelCase)

					lowerCAmelCase     =    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_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.""")

					# PIL RGBA images are converted to RGB
					if do_convert_rgb:
							lowerCAmelCase     =    [convert_to_rgb(__lowerCAmelCase) for image in images]

					# All transformations expect numpy arrays.
					lowerCAmelCase     =    [to_numpy_array(__lowerCAmelCase) for image in images]

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

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

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

					lowerCAmelCase     =    [to_channel_dimension_format(__lowerCAmelCase			,       __lowerCAmelCase) for image in images]

					lowerCAmelCase     =    BatchFeature(data={"""pixel_values""": images}			,       tensor_type=__lowerCAmelCase)

					return encoded_outputs




