


from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
       import PIL

lowerCAmelCase    :     Optional[int]					=						logging.get_logger(__name__)






def 			lowercase				(_A					):







       """simple docstring"""







       if isinstance(_A   ,						(list, tuple)					) and isinstance(videos[0]   ,						(list, tuple)					) and is_valid_image(videos[0][0]					):
              return videos

       elif isinstance(_A   ,						(list, tuple)					) and is_valid_image(videos[0]					):
              return [videos]

       elif is_valid_image(_A					):
              return [[videos]]

       raise ValueError(f'Could not make batched video from {videos}'					)
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    ["pixel_values"]







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

            '''simple docstring'''

            super().__init__(**snake_case__   )
            _lowerCAmelCase    :							Optional[Any]   			=       size if size is not None else {'shortest_edge': 256}
            _lowerCAmelCase    :							str   			=       get_size_dict(snake_case__      ,	default_to_square=snake_case__   )
            _lowerCAmelCase    :							Union[str, Any]   			=       crop_size if crop_size is not None else {'height': 224, 'width': 224}
            _lowerCAmelCase    :							Any   			=       get_size_dict(snake_case__      ,	param_name='crop_size'   )

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







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__ = PILImageResampling.BILINEAR      ,	snake_case__ = None      ,	**snake_case__      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       get_size_dict(snake_case__      ,	default_to_square=snake_case__   )
            if "shortest_edge" in size:
                   _lowerCAmelCase    :							List[Any]   			=       get_resize_output_image_size(snake_case__      ,	size['shortest_edge']      ,	default_to_square=snake_case__   )
            elif "height" in size and "width" in size:
                   _lowerCAmelCase    :							Union[str, Any]   			=       (size['height'], size['width'])
            else:
                   raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'   )
            return resize(snake_case__      ,	size=snake_case__      ,	resample=snake_case__      ,	data_format=snake_case__      ,	**snake_case__   )







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__ = None      ,	**snake_case__      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       get_size_dict(snake_case__   )
            if "height" not in size or "width" not in size:
                   raise ValueError(F'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'   )
            return center_crop(snake_case__      ,	size=(size['height'], size['width'])      ,	data_format=snake_case__      ,	**snake_case__   )







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__ = True      ,	snake_case__ = None      ,	**snake_case__      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       image.astype(np.floataa   )
            if offset:
                   _lowerCAmelCase    :							Union[str, Any]   			=       image - (scale / 2)
            return rescale(snake_case__      ,	scale=snake_case__      ,	data_format=snake_case__      ,	**snake_case__   )







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__ = None      ,	**snake_case__      ,	):

            '''simple docstring'''

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







     def a	( self      ,	snake_case__      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = ChannelDimension.FIRST      ,	):

            '''simple docstring'''

            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.'   )

            if offset and not do_rescale:
                   raise ValueError('For offset, do_rescale must also be set to True.'   )

            # All transformations expect numpy arrays.
            _lowerCAmelCase    :							Dict   			=       to_numpy_array(snake_case__   )

            if do_resize:
                   _lowerCAmelCase    :							Dict   			=       self.resize(image=snake_case__      ,	size=snake_case__      ,	resample=snake_case__   )

            if do_center_crop:
                   _lowerCAmelCase    :							Union[str, Any]   			=       self.center_crop(snake_case__      ,	size=snake_case__   )

            if do_rescale:
                   _lowerCAmelCase    :							Union[str, Any]   			=       self.rescale(image=snake_case__      ,	scale=snake_case__      ,	offset=snake_case__   )

            if do_normalize:
                   _lowerCAmelCase    :							int   			=       self.normalize(image=snake_case__      ,	mean=snake_case__      ,	std=snake_case__   )

            _lowerCAmelCase    :							List[str]   			=       to_channel_dimension_format(snake_case__      ,	snake_case__   )
            return image







     def a	( self      ,	snake_case__      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = ChannelDimension.FIRST      ,	**snake_case__      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       do_resize if do_resize is not None else self.do_resize
            _lowerCAmelCase    :							Any   			=       resample if resample is not None else self.resample
            _lowerCAmelCase    :							Optional[int]   			=       do_center_crop if do_center_crop is not None else self.do_center_crop
            _lowerCAmelCase    :							str   			=       do_rescale if do_rescale is not None else self.do_rescale
            _lowerCAmelCase    :							List[Any]   			=       rescale_factor if rescale_factor is not None else self.rescale_factor
            _lowerCAmelCase    :							str   			=       offset if offset is not None else self.offset
            _lowerCAmelCase    :							Any   			=       do_normalize if do_normalize is not None else self.do_normalize
            _lowerCAmelCase    :							Union[str, Any]   			=       image_mean if image_mean is not None else self.image_mean
            _lowerCAmelCase    :							List[str]   			=       image_std if image_std is not None else self.image_std

            _lowerCAmelCase    :							Tuple   			=       size if size is not None else self.size
            _lowerCAmelCase    :							Union[str, Any]   			=       get_size_dict(snake_case__      ,	default_to_square=snake_case__   )
            _lowerCAmelCase    :							str   			=       crop_size if crop_size is not None else self.crop_size
            _lowerCAmelCase    :							List[str]   			=       get_size_dict(snake_case__      ,	param_name='crop_size'   )

            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.'   )

            _lowerCAmelCase    :							int   			=       make_batched(snake_case__   )

            _lowerCAmelCase    :							Optional[int]   			=       [
                [
                    self._preprocess_image(
                        image=snake_case__      ,	do_resize=snake_case__      ,	size=snake_case__      ,	resample=snake_case__      ,	do_center_crop=snake_case__      ,	crop_size=snake_case__      ,	do_rescale=snake_case__      ,	rescale_factor=snake_case__      ,	offset=snake_case__      ,	do_normalize=snake_case__      ,	image_mean=snake_case__      ,	image_std=snake_case__      ,	data_format=snake_case__      ,	)
                    for img in video
                ]
                for video in videos
            ]

            _lowerCAmelCase    :							Any   			=       {'pixel_values': videos}
            return BatchFeature(data=snake_case__      ,	tensor_type=snake_case__   )





'''simple docstring'''







import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    (DDPMScheduler,)







     def a	( self      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       {
                'num_train_timesteps': 1000,
                'beta_start': 0.0001,
                'beta_end': 0.02,
                'beta_schedule': 'linear',
                'variance_type': 'fixed_small',
                'clip_sample': True,
            }

            config.update(**snake_case__   )
            return config







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

            for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]      ,	[0.002, 0.02, 0.2, 2]   ):
                   self.check_over_configs(beta_start=snake_case__      ,	beta_end=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Optional[Any]   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							List[Any]   			=       scheduler_class(**snake_case__   )

            assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 0.0   )   ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(487   ) - 0.0_0979   )   ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.02   )   ) < 1E-5







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Optional[Any]   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							List[Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Optional[int]   			=       len(snake_case__   )

            _lowerCAmelCase    :							str   			=       self.dummy_model()
            _lowerCAmelCase    :							str   			=       self.dummy_sample_deter
            _lowerCAmelCase    :							Union[str, Any]   			=       torch.manual_seed(0   )

            for t in reversed(range(snake_case__   )   ):
                   # 1. predict noise residual
                   _lowerCAmelCase    :							List[Any]   			=       model(snake_case__      ,	snake_case__   )

                   # 2. predict previous mean of sample x_t-1
                   _lowerCAmelCase    :							Any   			=       scheduler.step(snake_case__      ,	snake_case__      ,	snake_case__      ,	generator=snake_case__   ).prev_sample

                   # if t > 0:
                   #     noise = self.dummy_sample_deter
                   #     variance = scheduler.get_variance(t) ** (0.5) * noise
                   #
                   # sample = pred_prev_sample + variance
                   _lowerCAmelCase    :							Dict   			=       pred_prev_sample

            _lowerCAmelCase    :							Dict   			=       torch.sum(torch.abs(snake_case__   )   )
            _lowerCAmelCase    :							List[str]   			=       torch.mean(torch.abs(snake_case__   )   )

            assert abs(result_sum.item() - 258.9606   ) < 1E-2
            assert abs(result_mean.item() - 0.3372   ) < 1E-3







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Any   			=       self.get_scheduler_config(prediction_type='v_prediction'   )
            _lowerCAmelCase    :							Union[str, Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Optional[int]   			=       len(snake_case__   )

            _lowerCAmelCase    :							Any   			=       self.dummy_model()
            _lowerCAmelCase    :							Tuple   			=       self.dummy_sample_deter
            _lowerCAmelCase    :							Optional[int]   			=       torch.manual_seed(0   )

            for t in reversed(range(snake_case__   )   ):
                   # 1. predict noise residual
                   _lowerCAmelCase    :							Union[str, Any]   			=       model(snake_case__      ,	snake_case__   )

                   # 2. predict previous mean of sample x_t-1
                   _lowerCAmelCase    :							Dict   			=       scheduler.step(snake_case__      ,	snake_case__      ,	snake_case__      ,	generator=snake_case__   ).prev_sample

                   # if t > 0:
                   #     noise = self.dummy_sample_deter
                   #     variance = scheduler.get_variance(t) ** (0.5) * noise
                   #
                   # sample = pred_prev_sample + variance
                   _lowerCAmelCase    :							Tuple   			=       pred_prev_sample

            _lowerCAmelCase    :							Any   			=       torch.sum(torch.abs(snake_case__   )   )
            _lowerCAmelCase    :							Dict   			=       torch.mean(torch.abs(snake_case__   )   )

            assert abs(result_sum.item() - 202.0296   ) < 1E-2
            assert abs(result_mean.item() - 0.2631   ) < 1E-3







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Optional[int]   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							Union[str, Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Union[str, Any]   			=       [100, 87, 50, 1, 0]

            scheduler.set_timesteps(timesteps=snake_case__   )

            _lowerCAmelCase    :							Union[str, Any]   			=       scheduler.timesteps

            for i, timestep in enumerate(snake_case__   ):
                   if i == len(snake_case__   ) - 1:
                          _lowerCAmelCase    :							str   			=       -1
                   else:
                          _lowerCAmelCase    :							Optional[Any]   			=       timesteps[i + 1]

                   _lowerCAmelCase    :							int   			=       scheduler.previous_timestep(snake_case__   )
                   _lowerCAmelCase    :							int   			=       prev_t.item()

                   self.assertEqual(snake_case__      ,	snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Tuple   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							List[Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Optional[int]   			=       [100, 87, 50, 51, 0]

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							List[str]   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							Union[str, Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Optional[int]   			=       [100, 87, 50, 1, 0]
            _lowerCAmelCase    :							int   			=       len(snake_case__   )

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							int   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							Any   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Any   			=       [scheduler.config.num_train_timesteps]

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

