


"""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												=							logging.get_logger(__name__)


if is_vision_available():
   import PIL


class      __lowerCamelCase	(       a__					):

     '''simple docstring'''
     A_							:						Union[str, Any]     =		['pixel_values']


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

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

          _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 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None				)							->				np.ndarray:
          _a						,   _a     						=					get_image_size(__UpperCAmelCase				)
          _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(__UpperCAmelCase     ,					3				)

          if data_format is not None:
               _a     						=					to_channel_dimension_format(__UpperCAmelCase     ,					__UpperCAmelCase				)

          return image


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = None     ,					)							->				np.ndarray:
          _a						,   _a     						=					size['''height'''], size['''width''']
          _a						,   _a     						=					get_image_size(__UpperCAmelCase				)

          _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(__UpperCAmelCase     ,					__UpperCAmelCase     ,					data_format=__UpperCAmelCase				)


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


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase = PILImageResampling.BICUBIC     ,					__UpperCAmelCase = None     ,					**__UpperCAmelCase     ,					)							->				np.ndarray:
          _a						,   _a     						=					get_image_size(__UpperCAmelCase				)
          _a						,   _a     						=					size['''height'''], size['''width''']

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

          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(
              __UpperCAmelCase     ,					size=(height, width)     ,					resample=__UpperCAmelCase     ,					reducing_gap=2.0     ,					data_format=__UpperCAmelCase     ,					**__UpperCAmelCase				)


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


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None     ,					**__UpperCAmelCase     ,					)							->				Optional[Any]:
          return rescale(__UpperCAmelCase     ,					scale=__UpperCAmelCase     ,					data_format=__UpperCAmelCase     ,					**__UpperCAmelCase				)


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





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = ChannelDimension.FIRST     ,					**__UpperCAmelCase     ,					)							->				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(__UpperCAmelCase     ,					(tuple, list)				):
               # Previous feature extractor had size in (width, height) format
               _a     						=					size[::-1]
          _a     						=					get_size_dict(__UpperCAmelCase				)
          _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(__UpperCAmelCase				)

          if not valid_images(__UpperCAmelCase				):
               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(__UpperCAmelCase				) for image in images]

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

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

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

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

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

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

          _a     						=					[to_channel_dimension_format(__UpperCAmelCase     ,					__UpperCAmelCase				) for image in images]

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



"""simple docstring"""






import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class      __lowerCamelCase	(       a__					):

     '''simple docstring'''
     A_							:						Tuple     =		(DDIMScheduler,)
     A_							:						Tuple     =		(('eta', 0.0), ('num_inference_steps', 50))


     def 				_UpperCAmelCase   (   self     ,					**__UpperCAmelCase				)							->				str:
          _a     						=					{
              '''num_train_timesteps''': 1000,
              '''beta_start''': 0.0001,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
              '''clip_sample''': True,
          }

          config.update(**__UpperCAmelCase				)
          return config


     def 				_UpperCAmelCase   (   self     ,					**__UpperCAmelCase				)							->				List[Any]:
          _a     						=					self.scheduler_classes[0]
          _a     						=					self.get_scheduler_config(**__UpperCAmelCase				)
          _a     						=					scheduler_class(**__UpperCAmelCase				)

          _a						,   _a     						=					10, 0.0

          _a     						=					self.dummy_model()
          _a     						=					self.dummy_sample_deter

          scheduler.set_timesteps(__UpperCAmelCase				)

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

          return sample


     def 				_UpperCAmelCase   (   self				)							->				str:
          for timesteps in [100, 500, 1000]:
               self.check_over_configs(num_train_timesteps=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Dict:
          for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=__UpperCAmelCase				)

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


     def 				_UpperCAmelCase   (   self				)							->				int:
          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=__UpperCAmelCase     ,					beta_end=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Union[str, Any]:
          for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				int:
          for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Union[str, Any]:
          for timestep_spacing in ["trailing", "leading"]:
               self.check_over_configs(timestep_spacing=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          self.check_over_configs(thresholding=__UpperCAmelCase				)
          for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(
                        thresholding=__UpperCAmelCase     ,					prediction_type=__UpperCAmelCase     ,					sample_max_value=__UpperCAmelCase     ,					)


     def 				_UpperCAmelCase   (   self				)							->				int:
          for t in [1, 10, 49]:
               self.check_over_forward(time_step=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Tuple:
          for t, num_inference_steps in zip([1, 10, 50]     ,					[10, 50, 500]				):
               self.check_over_forward(time_step=__UpperCAmelCase     ,					num_inference_steps=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Optional[Any]:
          for t, eta in zip([1, 10, 49]     ,					[0.0, 0.5, 1.0]				):
               self.check_over_forward(time_step=__UpperCAmelCase     ,					eta=__UpperCAmelCase				)


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

          assert torch.sum(torch.abs(scheduler._get_variance(0     ,					0				) - 0.0				)				) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(420     ,					400				) - 0.14771				)				) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(980     ,					960				) - 0.32460				)				) < 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				) - 0.00979				)				) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(999     ,					998				) - 0.02				)				) < 1e-5


     def 				_UpperCAmelCase   (   self				)							->				Optional[int]:
          _a     						=					self.full_loop()

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

          assert abs(result_sum.item() - 172.0067				) < 1e-2
          assert abs(result_mean.item() - 0.223967				) < 1e-3


     def 				_UpperCAmelCase   (   self				)							->				Any:
          _a     						=					self.full_loop(prediction_type='''v_prediction'''				)

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

          assert abs(result_sum.item() - 52.5302				) < 1e-2
          assert abs(result_mean.item() - 0.0684				) < 1e-3


     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          # We specify different beta, so that the first alpha is 0.99
          _a     						=					self.full_loop(set_alpha_to_one=__UpperCAmelCase     ,					beta_start=0.01				)
          _a     						=					torch.sum(torch.abs(__UpperCAmelCase				)				)
          _a     						=					torch.mean(torch.abs(__UpperCAmelCase				)				)

          assert abs(result_sum.item() - 149.8295				) < 1e-2
          assert abs(result_mean.item() - 0.1951				) < 1e-3





     def 				_UpperCAmelCase   (   self				)							->				int:
          # We specify different beta, so that the first alpha is 0.99
          _a     						=					self.full_loop(set_alpha_to_one=__UpperCAmelCase     ,					beta_start=0.01				)
          _a     						=					torch.sum(torch.abs(__UpperCAmelCase				)				)
          _a     						=					torch.mean(torch.abs(__UpperCAmelCase				)				)

          assert abs(result_sum.item() - 149.0784				) < 1e-2
          assert abs(result_mean.item() - 0.1941				) < 1e-3