





import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class 	__magic_name__			(SCREAMING_SNAKE_CASE_ ):
					lowerCamelCase__     						=     (DDPMParallelScheduler,)




					def 					__a     (       self	,   **_a						)			->					str:
							lowerCAmelCase_												=				{
							    'num_train_timesteps': 1000,
							    'beta_start': 0.0_0_0_1,
							    'beta_end': 0.0_2,
							    'beta_schedule': 'linear',
							    'variance_type': 'fixed_small',
							    'clip_sample': True,
							}

							config.update(**_a						)
							return config




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


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




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


							for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]	,   [0.0_0_2, 0.0_2, 0.2, 2]						):
									self.check_over_configs(beta_start=_a	,   beta_end=_a						)




					def 					__a     (       self						)			->					str:


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




					def 					__a     (       self						)			->					int:


							for variance in ["fixed_small", "fixed_large", "other"]:
									self.check_over_configs(variance_type=_a						)




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


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




					def 					__a     (       self						)			->					List[Any]:
							self.check_over_configs(thresholding=_a						)
							for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "sample", "v_prediction"]:
											self.check_over_configs(
											    thresholding=_a	,   prediction_type=_a	,   sample_max_value=_a	,   )




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


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




					def 					__a     (       self						)			->					str:


							for t in [0, 500, 999]:
									self.check_over_forward(time_step=_a						)




					def 					__a     (       self						)			->					str:
							lowerCAmelCase_												=				self.scheduler_classes[0]
							lowerCAmelCase_												=				self.get_scheduler_config()
							lowerCAmelCase_												=				scheduler_class(**_a						)

							assert torch.sum(torch.abs(scheduler._get_variance(0						) - 0.0						)						) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(487						) - 0.0_0_9_7_9						)						) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(999						) - 0.0_2						)						) < 1E-5




					def 					__a     (       self						)			->					int:
							lowerCAmelCase_												=				self.scheduler_classes[0]
							lowerCAmelCase_												=				self.get_scheduler_config()
							lowerCAmelCase_												=				scheduler_class(**_a						)

							lowerCAmelCase_												=				len(_a						)

							lowerCAmelCase_												=				self.dummy_model()
							lowerCAmelCase_												=				self.dummy_sample_deter
							lowerCAmelCase_												=				self.dummy_sample_deter + 0.1
							lowerCAmelCase_												=				self.dummy_sample_deter - 0.1

							lowerCAmelCase_												=				samplea.shape[0]
							lowerCAmelCase_												=				torch.stack([samplea, samplea, samplea]	,   dim=0						)
							lowerCAmelCase_												=				torch.arange(_a						)[0:3, None].repeat(1	,   _a						)

							lowerCAmelCase_												=				model(samples.flatten(0	,   1						)	,   timesteps.flatten(0	,   1						)						)
							lowerCAmelCase_												=				scheduler.batch_step_no_noise(_a	,   timesteps.flatten(0	,   1						)	,   samples.flatten(0	,   1						)						)

							lowerCAmelCase_												=				torch.sum(torch.abs(_a						)						)
							lowerCAmelCase_												=				torch.mean(torch.abs(_a						)						)

							assert abs(result_sum.item() - 1_1_5_3.1_8_3_3						) < 1E-2
							assert abs(result_mean.item() - 0.5_0_0_5						) < 1E-3




					def 					__a     (       self						)			->					Optional[Any]:
							lowerCAmelCase_												=				self.scheduler_classes[0]
							lowerCAmelCase_												=				self.get_scheduler_config()
							lowerCAmelCase_												=				scheduler_class(**_a						)

							lowerCAmelCase_												=				len(_a						)

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

							for t in reversed(range(_a						)						):
									# 1. predict noise residual
									lowerCAmelCase_												=				model(_a	,   _a						)

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

									lowerCAmelCase_												=				pred_prev_sample

							lowerCAmelCase_												=				torch.sum(torch.abs(_a						)						)
							lowerCAmelCase_												=				torch.mean(torch.abs(_a						)						)

							assert abs(result_sum.item() - 2_5_8.9_6_0_6						) < 1E-2
							assert abs(result_mean.item() - 0.3_3_7_2						) < 1E-3




					def 					__a     (       self						)			->					List[str]:
							lowerCAmelCase_												=				self.scheduler_classes[0]
							lowerCAmelCase_												=				self.get_scheduler_config(prediction_type="v_prediction"						)
							lowerCAmelCase_												=				scheduler_class(**_a						)

							lowerCAmelCase_												=				len(_a						)

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

							for t in reversed(range(_a						)						):
									# 1. predict noise residual
									lowerCAmelCase_												=				model(_a	,   _a						)

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

									lowerCAmelCase_												=				pred_prev_sample

							lowerCAmelCase_												=				torch.sum(torch.abs(_a						)						)
							lowerCAmelCase_												=				torch.mean(torch.abs(_a						)						)

							assert abs(result_sum.item() - 2_0_2.0_2_9_6						) < 1E-2
							assert abs(result_mean.item() - 0.2_6_3_1						) < 1E-3




					def 					__a     (       self						)			->					Tuple:
							lowerCAmelCase_												=				self.scheduler_classes[0]
							lowerCAmelCase_												=				self.get_scheduler_config()
							lowerCAmelCase_												=				scheduler_class(**_a						)

							lowerCAmelCase_												=				[100, 87, 50, 1, 0]

							scheduler.set_timesteps(timesteps=_a						)

							lowerCAmelCase_												=				scheduler.timesteps

							for i, timestep in enumerate(_a						):
									if i == len(_a						) - 1:
											lowerCAmelCase_												=				-1
									else:
											lowerCAmelCase_												=				timesteps[i + 1]

									lowerCAmelCase_												=				scheduler.previous_timestep(_a						)
									lowerCAmelCase_												=				prev_t.item()

									self.assertEqual(_a	,   _a						)




					def 					__a     (       self						)			->					Dict:
							lowerCAmelCase_												=				self.scheduler_classes[0]
							lowerCAmelCase_												=				self.get_scheduler_config()
							lowerCAmelCase_												=				scheduler_class(**_a						)

							lowerCAmelCase_												=				[100, 87, 50, 51, 0]

							with self.assertRaises(_a	,   msg="`custom_timesteps` must be in descending order."						):
									scheduler.set_timesteps(timesteps=_a						)




					def 					__a     (       self						)			->					Optional[Any]:
							lowerCAmelCase_												=				self.scheduler_classes[0]
							lowerCAmelCase_												=				self.get_scheduler_config()
							lowerCAmelCase_												=				scheduler_class(**_a						)

							lowerCAmelCase_												=				[100, 87, 50, 1, 0]
							lowerCAmelCase_												=				len(_a						)

							with self.assertRaises(_a	,   msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."						):
									scheduler.set_timesteps(num_inference_steps=_a	,   timesteps=_a						)




					def 					__a     (       self						)			->					Tuple:
							lowerCAmelCase_												=				self.scheduler_classes[0]
							lowerCAmelCase_												=				self.get_scheduler_config()
							lowerCAmelCase_												=				scheduler_class(**_a						)

							lowerCAmelCase_												=				[scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    _a	,   msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"	,   ):
									scheduler.set_timesteps(timesteps=_a						)






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,
    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,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_lowerCamelCase    :		List[str]		      =    logging.get_logger(__name__)


class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						UpperCAmelCase			:		List[str]		=		['pixel_values']







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



											'''simple docstring'''


											super().__init__(**snake_case	)
											SCREAMING_SNAKE_CASE							:     List[str]						=				size if size is not None else {'height': 224, 'width': 224}
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				get_size_dict(snake_case	)
											SCREAMING_SNAKE_CASE							:     Any						=				crop_size if crop_size is not None else {'height': 224, 'width': 224}
											SCREAMING_SNAKE_CASE							:     int						=				get_size_dict(snake_case       ,	default_to_square=snake_case       ,	param_name='crop_size'	)

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







						def       lowerCamelCase_				(  self    :				int       ,	snake_case    :				np.ndarray       ,	snake_case    :				Dict[str, int]       ,	snake_case    :				PILImageResampling = PILImageResampling.BILINEAR       ,	snake_case    :				Optional[Union[str, ChannelDimension]] = None       ,	**snake_case    :				Any       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				get_size_dict(snake_case	)

											if "shortest_edge" in size:
																SCREAMING_SNAKE_CASE							:     Tuple						=				get_resize_output_image_size(snake_case       ,	size=size['shortest_edge']       ,	default_to_square=snake_case	)
																# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
											elif "height" in size and "width" in size:
																SCREAMING_SNAKE_CASE							:     int						=				(size['height'], size['width'])
											else:
																raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''	)
											return resize(snake_case       ,	size=snake_case       ,	resample=snake_case       ,	data_format=snake_case       ,	**snake_case	)







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



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				get_size_dict(snake_case	)
											if "height" not in size or "width" not in size:
																raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''	)
											return center_crop(snake_case       ,	size=(size['height'], size['width'])       ,	data_format=snake_case       ,	**snake_case	)







						def       lowerCamelCase_				(  self    :				Any       ,	snake_case    :				np.ndarray       ,	snake_case    :				float       ,	snake_case    :				Optional[Union[str, ChannelDimension]] = None       ,	**snake_case    :				Dict	):



											'''simple docstring'''


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







						def       lowerCamelCase_				(  self    :				List[Any]       ,	snake_case    :				np.ndarray       ,	snake_case    :				Union[float, List[float]]       ,	snake_case    :				Union[float, List[float]]       ,	snake_case    :				Optional[Union[str, ChannelDimension]] = None       ,	**snake_case    :				List[Any]       ,	):



											'''simple docstring'''


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







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



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Any						=				do_resize if do_resize is not None else self.do_resize
											SCREAMING_SNAKE_CASE							:     int						=				do_rescale if do_rescale is not None else self.do_rescale
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				do_normalize if do_normalize is not None else self.do_normalize
											SCREAMING_SNAKE_CASE							:     List[str]						=				do_center_crop if do_center_crop is not None else self.do_center_crop
											SCREAMING_SNAKE_CASE							:     List[str]						=				crop_size if crop_size is not None else self.crop_size
											SCREAMING_SNAKE_CASE							:     List[Any]						=				get_size_dict(snake_case       ,	param_name='crop_size'       ,	default_to_square=snake_case	)
											SCREAMING_SNAKE_CASE							:     Any						=				resample if resample is not None else self.resample
											SCREAMING_SNAKE_CASE							:     Any						=				rescale_factor if rescale_factor is not None else self.rescale_factor
											SCREAMING_SNAKE_CASE							:     str						=				image_mean if image_mean is not None else self.image_mean
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				image_std if image_std is not None else self.image_std

											SCREAMING_SNAKE_CASE							:     List[str]						=				size if size is not None else self.size
											SCREAMING_SNAKE_CASE							:     str						=				get_size_dict(snake_case	)

											if not is_batched(snake_case	):
																SCREAMING_SNAKE_CASE							:     str						=				[images]

											if not valid_images(snake_case	):
																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.'	)

											# All transformations expect numpy arrays.
											SCREAMING_SNAKE_CASE							:     Dict						=				[to_numpy_array(snake_case	) for image in images]

											if do_resize:
																SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				[self.resize(image=snake_case       ,	size=snake_case       ,	resample=snake_case	) for image in images]

											if do_center_crop:
																SCREAMING_SNAKE_CASE							:     str						=				[self.center_crop(image=snake_case       ,	size=snake_case	) for image in images]

											if do_rescale:
																SCREAMING_SNAKE_CASE							:     Tuple						=				[self.rescale(image=snake_case       ,	scale=snake_case	) for image in images]

											if do_normalize:
																SCREAMING_SNAKE_CASE							:     int						=				[self.normalize(image=snake_case       ,	mean=snake_case       ,	std=snake_case	) for image in images]

											SCREAMING_SNAKE_CASE							:     List[str]						=				[to_channel_dimension_format(snake_case       ,	snake_case	) for image in images]

											SCREAMING_SNAKE_CASE							:     Any						=				{'pixel_values': images}
											return BatchFeature(data=snake_case       ,	tensor_type=snake_case	)