'''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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


__snake_case  : str		       =    logging.get_logger(__name__)


if is_vision_available():
  import PIL




class       __UpperCAmelCase							(							_UpperCamelCase	):





       '''simple docstring'''



       __lowercase   :   Any  					=							['pixel_values']

       def __init__( self       ,	_SCREAMING_SNAKE_CASE = True       ,	_SCREAMING_SNAKE_CASE = None       ,	_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR       ,	_SCREAMING_SNAKE_CASE = True       ,	_SCREAMING_SNAKE_CASE = False       ,	_SCREAMING_SNAKE_CASE = True       ,	_SCREAMING_SNAKE_CASE = True       ,	_SCREAMING_SNAKE_CASE = 1 / 255       ,	_SCREAMING_SNAKE_CASE = True       ,	_SCREAMING_SNAKE_CASE = None       ,	_SCREAMING_SNAKE_CASE = None       ,	**_SCREAMING_SNAKE_CASE       ,	)						->	None:
           super().__init__(**_SCREAMING_SNAKE_CASE					)

           A_      =  size if size is not None else {'''height''': 2560, '''width''': 1920}
           if isinstance(_SCREAMING_SNAKE_CASE       ,	(tuple, list)					):
               # The previous feature extractor size parameter was in (width, height) format
               A_      =  size[::-1]
           A_      =  get_size_dict(_SCREAMING_SNAKE_CASE					)

           A_      =  do_resize
           A_      =  size
           A_      =  resample
           A_      =  do_thumbnail
           A_      =  do_align_long_axis
           A_      =  do_pad
           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    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = None					)						->	np.ndarray:
           A_  ,A_      =  get_image_size(_SCREAMING_SNAKE_CASE					)
           A_  ,A_      =  size['''height'''], size['''width''']

           if (output_width < output_height and input_width > input_height) or (
               output_width > output_height and input_width < input_height
           ):
               A_      =  np.rotaa(_SCREAMING_SNAKE_CASE       ,	3					)

           if data_format is not None:
               A_      =  to_channel_dimension_format(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)

           return image

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = False       ,	_SCREAMING_SNAKE_CASE = None       ,	)						->	np.ndarray:
           A_  ,A_      =  size['''height'''], size['''width''']
           A_  ,A_      =  get_image_size(_SCREAMING_SNAKE_CASE					)

           A_      =  output_width - input_width
           A_      =  output_height - input_height

           if random_padding:
               A_      =  np.random.randint(low=0       ,	high=delta_height + 1					)
               A_      =  np.random.randint(low=0       ,	high=delta_width + 1					)
           else:
               A_      =  delta_height // 2
               A_      =  delta_width // 2

           A_      =  delta_height - pad_top
           A_      =  delta_width - pad_left

           A_      =  ((pad_top, pad_bottom), (pad_left, pad_right))
           return pad(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	data_format=_SCREAMING_SNAKE_CASE					)

       def    __A			( self       ,	*_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)						->	Any:
           logger.info('''pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'''					)
           return self.pad_image(*_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC       ,	_SCREAMING_SNAKE_CASE = None       ,	**_SCREAMING_SNAKE_CASE       ,	)						->	np.ndarray:
           A_  ,A_      =  get_image_size(_SCREAMING_SNAKE_CASE					)
           A_  ,A_      =  size['''height'''], size['''width''']

           # We always resize to the smallest of either the input or output size.
           A_      =  min(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
           A_      =  min(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)

           if height == input_height and width == input_width:
               return image

           if input_height > input_width:
               A_      =  int(input_width * height / input_height					)
           elif input_width > input_height:
               A_      =  int(input_height * width / input_width					)

           return resize(
               _SCREAMING_SNAKE_CASE       ,	size=(height, width)       ,	resample=_SCREAMING_SNAKE_CASE       ,	reducing_gap=2.0       ,	data_format=_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC       ,	_SCREAMING_SNAKE_CASE = None       ,	**_SCREAMING_SNAKE_CASE       ,	)						->	np.ndarray:
           A_      =  get_size_dict(_SCREAMING_SNAKE_CASE					)
           A_      =  min(size['''height''']       ,	size['''width''']					)
           A_      =  get_resize_output_image_size(_SCREAMING_SNAKE_CASE       ,	size=_SCREAMING_SNAKE_CASE       ,	default_to_square=_SCREAMING_SNAKE_CASE					)
           A_      =  resize(_SCREAMING_SNAKE_CASE       ,	size=_SCREAMING_SNAKE_CASE       ,	resample=_SCREAMING_SNAKE_CASE       ,	data_format=_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)
           return resized_image

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = None       ,	**_SCREAMING_SNAKE_CASE       ,	)						->	List[str]:
           return rescale(_SCREAMING_SNAKE_CASE       ,	scale=_SCREAMING_SNAKE_CASE       ,	data_format=_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = None       ,	**_SCREAMING_SNAKE_CASE       ,	)						->	np.ndarray:
           return normalize(_SCREAMING_SNAKE_CASE       ,	mean=_SCREAMING_SNAKE_CASE       ,	std=_SCREAMING_SNAKE_CASE       ,	data_format=_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = None       ,	_SCREAMING_SNAKE_CASE = None       ,	_SCREAMING_SNAKE_CASE = None       ,	_SCREAMING_SNAKE_CASE = None       ,	_SCREAMING_SNAKE_CASE = None       ,	_SCREAMING_SNAKE_CASE = None       ,	_SCREAMING_SNAKE_CASE = False       ,	_SCREAMING_SNAKE_CASE = None       ,	_SCREAMING_SNAKE_CASE = None       ,	_SCREAMING_SNAKE_CASE = None       ,	_SCREAMING_SNAKE_CASE = None       ,	_SCREAMING_SNAKE_CASE = None       ,	_SCREAMING_SNAKE_CASE = None       ,	_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST       ,	**_SCREAMING_SNAKE_CASE       ,	)						->	PIL.Image.Image:
           A_      =  do_resize if do_resize is not None else self.do_resize
           A_      =  size if size is not None else self.size
           if isinstance(_SCREAMING_SNAKE_CASE       ,	(tuple, list)					):
               # Previous feature extractor had size in (width, height) format
               A_      =  size[::-1]
           A_      =  get_size_dict(_SCREAMING_SNAKE_CASE					)
           A_      =  resample if resample is not None else self.resample
           A_      =  do_thumbnail if do_thumbnail is not None else self.do_thumbnail
           A_      =  do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
           A_      =  do_pad if do_pad is not None else self.do_pad
           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_      =  make_list_of_images(_SCREAMING_SNAKE_CASE					)

           if not valid_images(_SCREAMING_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_rescale and rescale_factor is None:
               raise ValueError('''Rescale factor must be specified if do_rescale is True.'''					)

           if do_pad and size is None:
               raise ValueError('''Size must be specified if do_pad 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(_SCREAMING_SNAKE_CASE					) for image in images]

           if do_align_long_axis:
               A_      =  [self.align_long_axis(_SCREAMING_SNAKE_CASE       ,	size=_SCREAMING_SNAKE_CASE					) for image in images]

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

           if do_thumbnail:
               A_      =  [self.thumbnail(image=_SCREAMING_SNAKE_CASE       ,	size=_SCREAMING_SNAKE_CASE					) for image in images]

           if do_pad:
               A_      =  [self.pad_image(image=_SCREAMING_SNAKE_CASE       ,	size=_SCREAMING_SNAKE_CASE       ,	random_padding=_SCREAMING_SNAKE_CASE					) for image in images]

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

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

           A_      =  [to_channel_dimension_format(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					) for image in images]

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







'''simple docstring'''




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler




class       __UpperCAmelCase							(							unittest.TestCase	):





       '''simple docstring'''



       __lowercase   :   str  					=							(ScoreSdeVeScheduler,)
       __lowercase   :   List[str]  					=							()

       @property
       def    __A			( self					)						->	List[str]:
           A_      =  4
           A_      =  3
           A_      =  8
           A_      =  8

           A_      =  torch.rand((batch_size, num_channels, height, width)					)

           return sample

       @property
       def    __A			( self					)						->	Optional[Any]:
           A_      =  4
           A_      =  3
           A_      =  8
           A_      =  8

           A_      =  batch_size * num_channels * height * width
           A_      =  torch.arange(_SCREAMING_SNAKE_CASE					)
           A_      =  sample.reshape(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
           A_      =  sample / num_elems
           A_      =  sample.permute(3       ,	0       ,	1       ,	2					)

           return sample

       def    __A			( self					)						->	Union[str, Any]:
           def model(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	*_SCREAMING_SNAKE_CASE					):
               return sample * t / (t + 1)

           return model

       def    __A			( self       ,	**_SCREAMING_SNAKE_CASE					)						->	Tuple:
           A_      =  {
               '''num_train_timesteps''': 2000,
               '''snr''': 0.15,
               '''sigma_min''': 0.01,
               '''sigma_max''': 1348,
               '''sampling_eps''': 1E-5,
           }

           config.update(**_SCREAMING_SNAKE_CASE					)
           return config

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE=0       ,	**_SCREAMING_SNAKE_CASE					)						->	Any:
           A_      =  dict(self.forward_default_kwargs					)

           for scheduler_class in self.scheduler_classes:
               A_      =  self.dummy_sample
               A_      =  0.1 * sample

               A_      =  self.get_scheduler_config(**_SCREAMING_SNAKE_CASE					)
               A_      =  scheduler_class(**_SCREAMING_SNAKE_CASE					)

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_SCREAMING_SNAKE_CASE					)
                   A_      =  scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE					)

               A_      =  scheduler.step_pred(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample
               A_      =  new_scheduler.step_pred(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

               assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler outputs are not identical"

               A_      =  scheduler.step_correct(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample
               A_      =  new_scheduler.step_correct(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

               assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler correction are not identical"

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE=0       ,	**_SCREAMING_SNAKE_CASE					)						->	Any:
           A_      =  dict(self.forward_default_kwargs					)
           kwargs.update(_SCREAMING_SNAKE_CASE					)

           for scheduler_class in self.scheduler_classes:
               A_      =  self.dummy_sample
               A_      =  0.1 * sample

               A_      =  self.get_scheduler_config()
               A_      =  scheduler_class(**_SCREAMING_SNAKE_CASE					)

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_SCREAMING_SNAKE_CASE					)
                   A_      =  scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE					)

               A_      =  scheduler.step_pred(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample
               A_      =  new_scheduler.step_pred(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

               assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler outputs are not identical"

               A_      =  scheduler.step_correct(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample
               A_      =  new_scheduler.step_correct(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

               assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler correction are not identical"

       def    __A			( self					)						->	Any:
           for timesteps in [10, 100, 1000]:
               self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	Dict:
           for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]       ,	[1, 100, 1000]					):
               self.check_over_configs(sigma_min=_SCREAMING_SNAKE_CASE       ,	sigma_max=_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	Optional[int]:
           for t in [0.1, 0.5, 0.75]:
               self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	int:
           A_      =  dict(self.forward_default_kwargs					)

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

           A_      =  3

           A_      =  self.dummy_model()
           A_      =  self.dummy_sample_deter

           scheduler.set_sigmas(_SCREAMING_SNAKE_CASE					)
           scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)
           A_      =  torch.manual_seed(0					)

           for i, t in enumerate(scheduler.timesteps					):
               A_      =  scheduler.sigmas[i]

               for _ in range(scheduler.config.correct_steps					):
                   with torch.no_grad():
                       A_      =  model(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
                   A_      =  scheduler.step_correct(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

               with torch.no_grad():
                   A_      =  model(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)

               A_      =  scheduler.step_pred(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)
               A_  ,A_      =  output.prev_sample, output.prev_sample_mean

           A_      =  torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
           A_      =  torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

           assert np.isclose(result_sum.item()       ,	14_372_758_528.0					)
           assert np.isclose(result_mean.item()       ,	18_714_530.0					)

       def    __A			( self					)						->	List[Any]:
           A_      =  dict(self.forward_default_kwargs					)

           A_      =  kwargs.pop('''num_inference_steps'''       ,	_SCREAMING_SNAKE_CASE					)

           for scheduler_class in self.scheduler_classes:
               A_      =  self.get_scheduler_config()
               A_      =  scheduler_class(**_SCREAMING_SNAKE_CASE					)

               A_      =  self.dummy_sample
               A_      =  0.1 * sample

               if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE       ,	'''set_timesteps'''					):
                   scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)
               elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE       ,	'''set_timesteps'''					):
                   A_      =  num_inference_steps

               A_      =  scheduler.step_pred(_SCREAMING_SNAKE_CASE       ,	0       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample
               A_      =  scheduler.step_pred(_SCREAMING_SNAKE_CASE       ,	1       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

               self.assertEqual(output_a.shape       ,	sample.shape					)
               self.assertEqual(output_a.shape       ,	output_a.shape					)






