
"""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 center_crop, 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, is_vision_available, logging


if is_vision_available():
    import PIL


_a   :      Any       =				logging.get_logger(__name__)






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			Any	=       ["""pixel_values"""]
    def __init__(     self    ,     snake_case_ = True    ,     snake_case_ = None    ,     snake_case_ = PIL.Image.NEAREST    ,     snake_case_ = False    ,     snake_case_ = None    ,     snake_case_ = 1 / 2_55    ,     snake_case_ = False    ,     snake_case_ = True    ,     snake_case_ = True    ,     snake_case_ = None    ,     snake_case_ = None    ,     snake_case_ = True    ,     **snake_case_    ,     ):
      super().__init__(**snake_case_  )
      _snake_case  :    Tuple            = size if size is not None else {"height": 3_46, "width": 3_46}
      _snake_case  :    Any            = get_size_dict(snake_case_  )
      _snake_case  :    List[str]            = crop_size if crop_size is not None else {"height": 2_89, "width": 2_89}
      _snake_case  :    List[Any]            = get_size_dict(snake_case_    ,     param_name="crop_size"  )

      _snake_case  :    Any            = do_resize
      _snake_case  :    Optional[int]            = size
      _snake_case  :    List[str]            = resample
      _snake_case  :    Union[str, Any]            = do_center_crop
      _snake_case  :    str            = crop_size
      _snake_case  :    str            = do_rescale
      _snake_case  :    Tuple            = rescale_factor
      _snake_case  :    Optional[Any]            = rescale_offset
      _snake_case  :    str            = do_normalize
      _snake_case  :    Dict            = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _snake_case  :    Optional[Any]            = image_std if image_std is not None else IMAGENET_STANDARD_STD
      _snake_case  :    Dict            = include_top
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_ = PIL.Image.NEAREST    ,     snake_case_ = None    ,     **snake_case_    ,     ):
      _snake_case  :    Any            = get_size_dict(snake_case_  )
      if "height" not in size or "width" not in size:
        raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'  )
      return resize(
          snake_case_    ,     size=(size["height"], size["width"])    ,     resample=snake_case_    ,     data_format=snake_case_    ,     **snake_case_  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_ = None    ,     **snake_case_    ,     ):
      _snake_case  :    int            = get_size_dict(snake_case_  )
      if "height" not in size or "width" not in size:
        raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'  )
      return center_crop(snake_case_    ,     size=(size["height"], size["width"])    ,     data_format=snake_case_    ,     **snake_case_  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_ = True    ,     snake_case_ = None    ,     **snake_case_    ,     ):
      if offset:
        _snake_case  :    Optional[int]            = (image - 127.5) * scale
        if data_format is not None:
          _snake_case  :    Union[str, Any]            = to_channel_dimension_format(snake_case_    ,     snake_case_  )
        _snake_case  :    Tuple            = rescaled_image.astype(np.floataa  )
      else:
        _snake_case  :    Optional[int]            = rescale(snake_case_    ,     scale=snake_case_    ,     data_format=snake_case_    ,     **snake_case_  )
      return rescaled_image
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_ = None    ,     **snake_case_    ,     ):
      return normalize(snake_case_    ,     mean=snake_case_    ,     std=snake_case_    ,     data_format=snake_case_    ,     **snake_case_  )


    def        lowerCamelCase__						(     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_ = None    ,     snake_case_ = ChannelDimension.FIRST    ,     **snake_case_    ,     ):
      _snake_case  :    Dict            = do_resize if do_resize is not None else self.do_resize
      _snake_case  :    Tuple            = resample if resample is not None else self.resample
      _snake_case  :    Any            = do_center_crop if do_center_crop is not None else self.do_center_crop
      _snake_case  :    str            = do_rescale if do_rescale is not None else self.do_rescale
      _snake_case  :    int            = rescale_factor if rescale_factor is not None else self.rescale_factor
      _snake_case  :    Any            = rescale_offset if rescale_offset is not None else self.rescale_offset
      _snake_case  :    Tuple            = do_normalize if do_normalize is not None else self.do_normalize
      _snake_case  :    str            = image_mean if image_mean is not None else self.image_mean
      _snake_case  :    Any            = image_std if image_std is not None else self.image_std
      _snake_case  :    Any            = include_top if include_top is not None else self.include_top

      _snake_case  :    List[str]            = size if size is not None else self.size
      _snake_case  :    Any            = get_size_dict(snake_case_  )
      _snake_case  :    str            = crop_size if crop_size is not None else self.crop_size
      _snake_case  :    Optional[Any]            = get_size_dict(snake_case_    ,     param_name="crop_size"  )

      _snake_case  :    List[str]            = make_list_of_images(snake_case_  )

      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 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."  )

      # All transformations expect numpy arrays.
      _snake_case  :    str            = [to_numpy_array(snake_case_  ) for image in images]

      if do_resize:
        _snake_case  :    List[str]            = [self.resize(image=snake_case_    ,     size=snake_case_    ,     resample=snake_case_  ) for image in images]

      if do_center_crop:
        _snake_case  :    Optional[Any]            = [self.center_crop(image=snake_case_    ,     size=snake_case_  ) for image in images]

      if do_rescale:
        _snake_case  :    Tuple            = [self.rescale(image=snake_case_    ,     scale=snake_case_    ,     offset=snake_case_  ) for image in images]

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

      if include_top:
        _snake_case  :    Optional[int]            = [self.normalize(image=snake_case_    ,     mean=[0, 0, 0]    ,     std=snake_case_  ) for image in images]

      _snake_case  :    Union[str, Any]            = [to_channel_dimension_format(snake_case_    ,     snake_case_  ) for image in images]

      _snake_case  :    Any            = {"pixel_values": images}
      return BatchFeature(data=snake_case_    ,     tensor_type=snake_case_  )





"""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