




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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCamelCase			:			Optional[Any]								=       logging.get_logger(__name__)




class 		__lowercase						(snake_case__				):




      """simple docstring"""





      _snake_case					=		['pixel_values']

      def __init__( self							, A = True							, A = None							, A = PILImageResampling.BICUBIC							, A = True							, A = True							, A = 1 / 2_5_5							, A = None							, A = True							, A = None							, A = None							, **A							, )						->							str:
         super().__init__(**A      )
         snake_case :							Union[str, Any]           =			size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
         snake_case :							Optional[int]           =			get_size_dict(A      )
         snake_case :							List[str]           =			crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
         snake_case :							str           =			get_size_dict(A							, default_to_square=A							, param_name="""crop_size"""      )

         snake_case :							List[str]           =			do_resize
         snake_case :							List[Any]           =			do_rescale
         snake_case :							Any           =			do_normalize
         snake_case :							List[str]           =			do_center_crop
         snake_case :							Dict           =			crop_size
         snake_case :							str           =			size
         snake_case :							Optional[int]           =			resample
         snake_case :							Union[str, Any]           =			rescale_factor
         snake_case :							List[str]           =			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         snake_case :							Union[str, Any]           =			image_std if image_std is not None else IMAGENET_DEFAULT_STD

      def       UpperCAmelCase ( self							, A							, A							, A = PILImageResampling.BILINEAR							, A = None							, **A							, )						->							str:
         snake_case :							Optional[int]           =			get_size_dict(A      )

         if "shortest_edge" in size:
            snake_case :							int           =			get_resize_output_image_size(A							, size=size["""shortest_edge"""]							, default_to_square=A      )
            # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
         elif "height" in size and "width" in size:
            snake_case :							int           =			(size['height'], size['width'])
         else:
            raise ValueError(f"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"""      )
         return resize(A							, size=A							, resample=A							, data_format=A							, **A      )

      def       UpperCAmelCase ( self							, A							, A							, A = None							, **A							, )						->							Dict:
         snake_case :							List[str]           =			get_size_dict(A      )
         if "height" not in size or "width" not in size:
            raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""      )
         return center_crop(A							, size=(size["""height"""], size["""width"""])							, data_format=A							, **A      )

      def       UpperCAmelCase ( self							, A							, A							, A = None							, **A      )						->							Tuple:
         return rescale(A							, scale=A							, data_format=A							, **A      )

      def       UpperCAmelCase ( self							, A							, A							, A							, A = None							, **A							, )						->							List[Any]:
         return normalize(A							, mean=A							, std=A							, data_format=A							, **A      )

      def       UpperCAmelCase ( self							, A							, A = None							, A = None							, A = None							, A = None							, A = None							, A = None							, A = None							, A = None							, A = None							, A = None							, A = None							, A = ChannelDimension.FIRST							, **A							, )						->							Union[str, Any]:
         snake_case :							Union[str, Any]           =			do_resize if do_resize is not None else self.do_resize
         snake_case :							Any           =			do_rescale if do_rescale is not None else self.do_rescale
         snake_case :							int           =			do_normalize if do_normalize is not None else self.do_normalize
         snake_case :							Any           =			do_center_crop if do_center_crop is not None else self.do_center_crop
         snake_case :							Any           =			crop_size if crop_size is not None else self.crop_size
         snake_case :							Tuple           =			get_size_dict(A							, param_name="""crop_size"""							, default_to_square=A      )
         snake_case :							List[Any]           =			resample if resample is not None else self.resample
         snake_case :							List[str]           =			rescale_factor if rescale_factor is not None else self.rescale_factor
         snake_case :							Tuple           =			image_mean if image_mean is not None else self.image_mean
         snake_case :							int           =			image_std if image_std is not None else self.image_std

         snake_case :							Optional[Any]           =			size if size is not None else self.size
         snake_case :							Union[str, Any]           =			get_size_dict(A      )

         if not is_batched(A      ):
            snake_case :							Union[str, Any]           =			[images]

         if not valid_images(A      ):
            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_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."""      )

         # All transformations expect numpy arrays.
         snake_case :							int           =			[to_numpy_array(A      ) for image in images]

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

         if do_center_crop:
            snake_case :							List[Any]           =			[self.center_crop(image=A							, size=A      ) for image in images]

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

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

         snake_case :							Dict           =			[to_channel_dimension_format(A							, A      ) for image in images]

         snake_case :							Dict           =			{'pixel_values': images}
         return BatchFeature(data=A							, tensor_type=A      )












import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest






@require_torchsde
class 						UpperCamelCase_  (  snake_case__							):
					_a      :						Union[str, Any]						    =					(DPMSolverSDEScheduler,)
					_a      :						List[Any]						    =					1_0



					def   __a     (		self     :     Any       ,      **lowerCamelCase     :     str						):
							lowerCamelCase_	:     Any 						=      {
							    'num_train_timesteps': 11_00,
							    'beta_start': 0.0_001,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							    'noise_sampler_seed': 0,
							}

							config.update(**lowerCamelCase						)
							return config



					def   __a     (		self     :     Tuple						):
							for timesteps in [10, 50, 1_00, 10_00]:
									self.check_over_configs(num_train_timesteps=lowerCamelCase						)



					def   __a     (		self     :     int						):
							for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]       ,      [0.0_002, 0.002, 0.02]						):
									self.check_over_configs(beta_start=lowerCamelCase       ,      beta_end=lowerCamelCase						)



					def   __a     (		self     :     Any						):
							for schedule in ["linear", "scaled_linear"]:
									self.check_over_configs(beta_schedule=lowerCamelCase						)



					def   __a     (		self     :     Optional[int]						):
							for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=lowerCamelCase						)



					def   __a     (		self     :     Optional[int]						):
							lowerCamelCase_	:     int 						=      self.scheduler_classes[0]
							lowerCamelCase_	:     str 						=      self.get_scheduler_config()
							lowerCamelCase_	:     Optional[Any] 						=      scheduler_class(**lowerCamelCase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							lowerCamelCase_	:     Optional[Any] 						=      self.dummy_model()
							lowerCamelCase_	:     Optional[Any] 						=      self.dummy_sample_deter * scheduler.init_noise_sigma
							lowerCamelCase_	:     Union[str, Any] 						=      sample.to(lowerCamelCase						)

							for i, t in enumerate(scheduler.timesteps						):
									lowerCamelCase_	:     str 						=      scheduler.scale_model_input(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Tuple 						=      model(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Optional[int] 						=      scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase						)
									lowerCamelCase_	:     Tuple 						=      output.prev_sample

							lowerCamelCase_	:     List[str] 						=      torch.sum(torch.abs(lowerCamelCase						)						)
							lowerCamelCase_	:     Optional[Any] 						=      torch.mean(torch.abs(lowerCamelCase						)						)

							if torch_device in ["mps"]:
									assert abs(result_sum.item() - 167.47_821_044_921_875						) < 1E-2
									assert abs(result_mean.item() - 0.2_178_705_964_565_277						) < 1E-3
							elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 171.59_352_111_816_406						) < 1E-2
									assert abs(result_mean.item() - 0.22_342_906_892_299_652						) < 1E-3
							else:
									assert abs(result_sum.item() - 162.52_383_422_851_562						) < 1E-2
									assert abs(result_mean.item() - 0.211_619_570_851_326						) < 1E-3



					def   __a     (		self     :     Tuple						):
							lowerCamelCase_	:     List[str] 						=      self.scheduler_classes[0]
							lowerCamelCase_	:     Any 						=      self.get_scheduler_config(prediction_type='v_prediction'						)
							lowerCamelCase_	:     Tuple 						=      scheduler_class(**lowerCamelCase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							lowerCamelCase_	:     Any 						=      self.dummy_model()
							lowerCamelCase_	:     Tuple 						=      self.dummy_sample_deter * scheduler.init_noise_sigma
							lowerCamelCase_	:     List[Any] 						=      sample.to(lowerCamelCase						)

							for i, t in enumerate(scheduler.timesteps						):
									lowerCamelCase_	:     Dict 						=      scheduler.scale_model_input(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Optional[int] 						=      model(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Union[str, Any] 						=      scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase						)
									lowerCamelCase_	:     Dict 						=      output.prev_sample

							lowerCamelCase_	:     Optional[Any] 						=      torch.sum(torch.abs(lowerCamelCase						)						)
							lowerCamelCase_	:     List[Any] 						=      torch.mean(torch.abs(lowerCamelCase						)						)

							if torch_device in ["mps"]:
									assert abs(result_sum.item() - 124.77_149_200_439_453						) < 1E-2
									assert abs(result_mean.item() - 0.16_226_289_014_816_284						) < 1E-3
							elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 128.1_663_360_595_703						) < 1E-2
									assert abs(result_mean.item() - 0.16_688_326_001_167_297						) < 1E-3
							else:
									assert abs(result_sum.item() - 119.8_487_548_828_125						) < 1E-2
									assert abs(result_mean.item() - 0.1_560_530_662_536_621						) < 1E-3



					def   __a     (		self     :     Optional[Any]						):
							lowerCamelCase_	:     Union[str, Any] 						=      self.scheduler_classes[0]
							lowerCamelCase_	:     List[Any] 						=      self.get_scheduler_config()
							lowerCamelCase_	:     List[str] 						=      scheduler_class(**lowerCamelCase						)

							scheduler.set_timesteps(self.num_inference_steps       ,      device=lowerCamelCase						)

							lowerCamelCase_	:     Dict 						=      self.dummy_model()
							lowerCamelCase_	:     Dict 						=      self.dummy_sample_deter.to(lowerCamelCase						) * scheduler.init_noise_sigma

							for t in scheduler.timesteps:
									lowerCamelCase_	:     Tuple 						=      scheduler.scale_model_input(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     List[str] 						=      model(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     int 						=      scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase						)
									lowerCamelCase_	:     Optional[int] 						=      output.prev_sample

							lowerCamelCase_	:     List[Any] 						=      torch.sum(torch.abs(lowerCamelCase						)						)
							lowerCamelCase_	:     str 						=      torch.mean(torch.abs(lowerCamelCase						)						)

							if torch_device in ["mps"]:
									assert abs(result_sum.item() - 167.46_957_397_460_938						) < 1E-2
									assert abs(result_mean.item() - 0.21_805_934_607_982_635						) < 1E-3
							elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 171.59_353_637_695_312						) < 1E-2
									assert abs(result_mean.item() - 0.22_342_908_382_415_771						) < 1E-3
							else:
									assert abs(result_sum.item() - 162.52_383_422_851_562						) < 1E-2
									assert abs(result_mean.item() - 0.211_619_570_851_326						) < 1E-3





					def   __a     (		self     :     Any						):
							lowerCamelCase_	:     Dict 						=      self.scheduler_classes[0]
							lowerCamelCase_	:     List[Any] 						=      self.get_scheduler_config()
							lowerCamelCase_	:     Union[str, Any] 						=      scheduler_class(**lowerCamelCase       ,      use_karras_sigmas=lowerCamelCase						)

							scheduler.set_timesteps(self.num_inference_steps       ,      device=lowerCamelCase						)

							lowerCamelCase_	:     int 						=      self.dummy_model()
							lowerCamelCase_	:     str 						=      self.dummy_sample_deter.to(lowerCamelCase						) * scheduler.init_noise_sigma
							lowerCamelCase_	:     Dict 						=      sample.to(lowerCamelCase						)

							for t in scheduler.timesteps:
									lowerCamelCase_	:     Optional[Any] 						=      scheduler.scale_model_input(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Dict 						=      model(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Dict 						=      scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase						)
									lowerCamelCase_	:     Any 						=      output.prev_sample

							lowerCamelCase_	:     Optional[int] 						=      torch.sum(torch.abs(lowerCamelCase						)						)
							lowerCamelCase_	:     Union[str, Any] 						=      torch.mean(torch.abs(lowerCamelCase						)						)

							if torch_device in ["mps"]:
									assert abs(result_sum.item() - 176.66_974_135_742_188						) < 1E-2
									assert abs(result_mean.item() - 0.23_003_872_730_981_811						) < 1E-2
							elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 177.63_653_564_453_125						) < 1E-2
									assert abs(result_mean.item() - 0.23_003_872_730_981_811						) < 1E-2
							else:
									assert abs(result_sum.item() - 170.3_135_223_388_672						) < 1E-2
									assert abs(result_mean.item() - 0.23_003_872_730_981_811						) < 1E-2
