

'''simple docstring'''

import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	(DDIMScheduler,)
       lowerCAmelCase_				=	(("eta", 0.0), ("num_inference_steps", 5_0))







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







            """simple docstring"""







            __lowercase     						={
                'num_train_timesteps': 1000,
                'beta_start': 0.0_0_0_1,
                'beta_end': 0.0_2,
                'beta_schedule': 'linear',
                'clip_sample': True,
            }

            config.update(**__lowercase )
            return config







       def      snake_case				(							self     :	Optional[Any]  , **__lowercase     :	str ):







            """simple docstring"""







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

            __lowercase						,       __lowercase     						=10, 0.0

            __lowercase     						=self.dummy_model()
            __lowercase     						=self.dummy_sample_deter

            scheduler.set_timesteps(__lowercase )

            for t in scheduler.timesteps:
                 __lowercase     						=model(__lowercase  , __lowercase )
                 __lowercase     						=scheduler.step(__lowercase  , __lowercase  , __lowercase  , __lowercase ).prev_sample

            return sample







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            for timesteps in [100, 500, 1000]:
                 self.check_over_configs(num_train_timesteps=__lowercase )







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=__lowercase )

            __lowercase     						=self.scheduler_classes[0]
            __lowercase     						=self.get_scheduler_config(steps_offset=1 )
            __lowercase     						=scheduler_class(**__lowercase )
            scheduler.set_timesteps(5 )
            assert torch.equal(scheduler.timesteps  , torch.LongTensor([801, 601, 401, 201, 1] ) )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            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=__lowercase  , beta_end=__lowercase )







       def      snake_case				(							self     :	str ):







            """simple docstring"""







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







       def      snake_case				(							self     :	str ):







            """simple docstring"""







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







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







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







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            for timestep_spacing in ["trailing", "leading"]:
                 self.check_over_configs(timestep_spacing=__lowercase )







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            for rescale_betas_zero_snr in [True, False]:
                 self.check_over_configs(rescale_betas_zero_snr=__lowercase )







       def      snake_case				(							self     :	int ):







            """simple docstring"""







            self.check_over_configs(thresholding=__lowercase )
            for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "v_prediction"]:
                      self.check_over_configs(
                          thresholding=__lowercase  , prediction_type=__lowercase  , sample_max_value=__lowercase  , )







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            for t in [1, 10, 49]:
                 self.check_over_forward(time_step=__lowercase )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            for t, num_inference_steps in zip([1, 10, 50]  , [10, 50, 500] ):
                 self.check_over_forward(time_step=__lowercase  , num_inference_steps=__lowercase )







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            for t, eta in zip([1, 10, 49]  , [0.0, 0.5, 1.0] ):
                 self.check_over_forward(time_step=__lowercase  , eta=__lowercase )







       def      snake_case				(							self     :	Optional[int] ):







            """simple docstring"""







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

            assert torch.sum(torch.abs(scheduler._get_variance(0  , 0 ) - 0.0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(420  , 400 ) - 0.1_4_7_7_1 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(980  , 960 ) - 0.3_2_4_6_0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(0  , 0 ) - 0.0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(487  , 486 ) - 0.0_0_9_7_9 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(999  , 998 ) - 0.0_2 ) ) < 1E-5







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            __lowercase     						=self.full_loop()

            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 172.0067 ) < 1E-2
            assert abs(result_mean.item() - 0.2_2_3_9_6_7 ) < 1E-3







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            __lowercase     						=self.full_loop(prediction_type='v_prediction' )

            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 52.5302 ) < 1E-2
            assert abs(result_mean.item() - 0.0_6_8_4 ) < 1E-3







       def      snake_case				(							self     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						=self.full_loop(set_alpha_to_one=__lowercase  , beta_start=0.0_1 )
            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 149.8295 ) < 1E-2
            assert abs(result_mean.item() - 0.1_9_5_1 ) < 1E-3







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            __lowercase     						=self.full_loop(set_alpha_to_one=__lowercase  , beta_start=0.0_1 )
            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 149.0784 ) < 1E-2
            assert abs(result_mean.item() - 0.1_9_4_1 ) < 1E-3





'''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 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, logging


UpperCAmelCase							=					logging.get_logger(__name__)


class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	["pixel_values"]







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







            """simple docstring"""







            super().__init__(**__lowercase )
            __lowercase     						=size if size is not None else {'height': 224, 'width': 224}
            __lowercase     						=get_size_dict(__lowercase )
            __lowercase     						=do_resize
            __lowercase     						=do_rescale
            __lowercase     						=do_normalize
            __lowercase     						=size
            __lowercase     						=resample
            __lowercase     						=rescale_factor
            __lowercase     						=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            __lowercase     						=image_std if image_std is not None else IMAGENET_STANDARD_STD







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







            """simple docstring"""







            __lowercase     						=get_size_dict(__lowercase )
            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()}''' )
            return resize(
                __lowercase  , size=(size['height'], size['width'])  , resample=__lowercase  , data_format=__lowercase  , **__lowercase )







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







            """simple docstring"""







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







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







            """simple docstring"""







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

            __lowercase     						=size if size is not None else self.size
            __lowercase     						=get_size_dict(__lowercase )

            __lowercase     						=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:
                 raise ValueError('Size 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.' )

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

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

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

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

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

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


