
"""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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
					import PIL


UpperCAmelCase          =							logging.get_logger(__name__)



class  lowercase				(   lowerCamelCase_		):
			lowercase			     =    ["""pixel_values"""]






			def __init__(self     :  Optional[Any]			,SCREAMING_SNAKE_CASE_     :  List[str] = True			,SCREAMING_SNAKE_CASE_     :  Dict = None			,SCREAMING_SNAKE_CASE_     :  List[Any] = True			,SCREAMING_SNAKE_CASE_     :  Tuple = None			,SCREAMING_SNAKE_CASE_     :  List[str] = PILImageResampling.BICUBIC			,SCREAMING_SNAKE_CASE_     :  Optional[Any] = True			,SCREAMING_SNAKE_CASE_     :  Union[str, Any] = 1 / 255			,SCREAMING_SNAKE_CASE_     :  List[Any] = True			,SCREAMING_SNAKE_CASE_     :  Union[str, Any] = None			,SCREAMING_SNAKE_CASE_     :  List[Any] = None			,**SCREAMING_SNAKE_CASE_     :  Optional[int]			,)					->  Union[str, Any]:





				"""simple docstring"""


				super().__init__(**SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase				       =     crop_size if crop_size is not None else {"height": 256, "width": 256}
				lowerCAmelCase				       =     get_size_dict(SCREAMING_SNAKE_CASE_			,param_name='''crop_size''' )
				lowerCAmelCase				       =     size if size is not None else {"height": 224, "width": 224}
				lowerCAmelCase				       =     get_size_dict(SCREAMING_SNAKE_CASE_ )

				lowerCAmelCase				       =     do_center_crop
				lowerCAmelCase				       =     crop_size
				lowerCAmelCase				       =     do_resize
				lowerCAmelCase				       =     size
				lowerCAmelCase				       =     resample
				lowerCAmelCase				       =     do_rescale
				lowerCAmelCase				       =     rescale_factor
				lowerCAmelCase				       =     do_normalize
				lowerCAmelCase				       =     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
				lowerCAmelCase				       =     image_std if image_std is not None else IMAGENET_DEFAULT_STD






			def 				UpperCAmelCase       (self     :  str			,SCREAMING_SNAKE_CASE_     :  str			,SCREAMING_SNAKE_CASE_     :  int			,SCREAMING_SNAKE_CASE_     :  Tuple = None			,SCREAMING_SNAKE_CASE_     :  Optional[Any] = None			,**SCREAMING_SNAKE_CASE_     :  Optional[int]			,)					->  Any:





				"""simple docstring"""


				lowerCAmelCase				       =     self.size if size is None else size
				lowerCAmelCase				       =     get_size_dict(SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase				       =     get_size_dict(SCREAMING_SNAKE_CASE_			,param_name='''crop_size''' )

				lowerCAmelCase				       =     get_image_size(SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase				       =     min(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase				       =     (size["height"] / crop_size["height"]) * min_dim
				lowerCAmelCase				       =     (size["width"] / crop_size["width"]) * min_dim
				return center_crop(SCREAMING_SNAKE_CASE_			,size=(cropped_height, cropped_width)			,data_format=SCREAMING_SNAKE_CASE_			,**SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  str			,SCREAMING_SNAKE_CASE_     :  List[str]			,SCREAMING_SNAKE_CASE_     :  Union[str, Any]			,SCREAMING_SNAKE_CASE_     :  List[str] = PIL.Image.BICUBIC			,SCREAMING_SNAKE_CASE_     :  str = None			,**SCREAMING_SNAKE_CASE_     :  Dict			,)					->  List[Any]:





				"""simple docstring"""


				lowerCAmelCase				       =     get_size_dict(SCREAMING_SNAKE_CASE_ )
				if "height" not in size or "width" not in size:
					raise ValueError(F"""The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}""" )
				return resize(
				    SCREAMING_SNAKE_CASE_			,size=(size['''height'''], size['''width'''])			,resample=SCREAMING_SNAKE_CASE_			,data_format=SCREAMING_SNAKE_CASE_			,**SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  Tuple			,SCREAMING_SNAKE_CASE_     :  Any			,SCREAMING_SNAKE_CASE_     :  List[str]			,SCREAMING_SNAKE_CASE_     :  List[Any] = None			,**SCREAMING_SNAKE_CASE_     :  str			,)					->  Tuple:





				"""simple docstring"""


				return rescale(SCREAMING_SNAKE_CASE_			,scale=SCREAMING_SNAKE_CASE_			,data_format=SCREAMING_SNAKE_CASE_			,**SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  Optional[Any]			,SCREAMING_SNAKE_CASE_     :  str			,SCREAMING_SNAKE_CASE_     :  Any			,SCREAMING_SNAKE_CASE_     :  Union[str, Any]			,SCREAMING_SNAKE_CASE_     :  str = None			,**SCREAMING_SNAKE_CASE_     :  int			,)					->  Any:





				"""simple docstring"""


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






			def 				UpperCAmelCase       (self     :  Optional[int]			,SCREAMING_SNAKE_CASE_     :  List[str]			,SCREAMING_SNAKE_CASE_     :  List[str] = None			,SCREAMING_SNAKE_CASE_     :  Optional[Any] = None			,SCREAMING_SNAKE_CASE_     :  str = None			,SCREAMING_SNAKE_CASE_     :  List[str] = None			,SCREAMING_SNAKE_CASE_     :  Optional[Any] = None			,SCREAMING_SNAKE_CASE_     :  Union[str, Any] = None			,SCREAMING_SNAKE_CASE_     :  Union[str, Any] = None			,SCREAMING_SNAKE_CASE_     :  Any = None			,SCREAMING_SNAKE_CASE_     :  List[Any] = None			,SCREAMING_SNAKE_CASE_     :  List[Any] = None			,SCREAMING_SNAKE_CASE_     :  Union[str, Any] = None			,SCREAMING_SNAKE_CASE_     :  int = ChannelDimension.FIRST			,**SCREAMING_SNAKE_CASE_     :  str			,)					->  List[str]:





				"""simple docstring"""


				lowerCAmelCase				       =     do_center_crop if do_center_crop is not None else self.do_center_crop
				lowerCAmelCase				       =     crop_size if crop_size is not None else self.crop_size
				lowerCAmelCase				       =     get_size_dict(SCREAMING_SNAKE_CASE_			,param_name='''crop_size''' )
				lowerCAmelCase				       =     do_resize if do_resize is not None else self.do_resize
				lowerCAmelCase				       =     size if size is not None else self.size
				lowerCAmelCase				       =     get_size_dict(SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase				       =     resample if resample is not None else self.resample
				lowerCAmelCase				       =     do_rescale if do_rescale is not None else self.do_rescale
				lowerCAmelCase				       =     rescale_factor if rescale_factor is not None else self.rescale_factor
				lowerCAmelCase				       =     do_normalize if do_normalize is not None else self.do_normalize
				lowerCAmelCase				       =     image_mean if image_mean is not None else self.image_mean
				lowerCAmelCase				       =     image_std if image_std is not None else self.image_std

				lowerCAmelCase				       =     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_center_crop and crop_size is None:
					raise ValueError('''If `do_center_crop` is set to `True`, `crop_size` must be provided.''' )

				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_normalize and (image_mean is None or image_std is None):
					raise ValueError('''Image mean and image standard deviation must be specified if do_normalize is True.''' )

				# All transformations expect numpy arrays.
				lowerCAmelCase				       =     [to_numpy_array(SCREAMING_SNAKE_CASE_ ) for image in images]

				if do_center_crop:
					lowerCAmelCase				       =     [self.center_crop(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,size=SCREAMING_SNAKE_CASE_ ) for image in images]

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

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

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

				lowerCAmelCase				       =     [to_channel_dimension_format(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ ) for image in images]

				lowerCAmelCase				       =     {"pixel_values": images}
				return BatchFeature(data=SCREAMING_SNAKE_CASE_			,tensor_type=SCREAMING_SNAKE_CASE_ )




"""simple docstring"""






import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class  lowercase				(   lowercase__		):
			lowercase			     =    (KDPMaAncestralDiscreteScheduler,)
			lowercase			     =    10






			def 				UpperCAmelCase       (self     :  Optional[int]			,**SCREAMING_SNAKE_CASE_     :  Optional[int] )					->  Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase				       =     {
				    '''num_train_timesteps''': 1_100,
				    '''beta_start''': 0.00_01,
				    '''beta_end''': 0.02,
				    '''beta_schedule''': '''linear''',
				}

				config.update(**SCREAMING_SNAKE_CASE_ )
				return config






			def 				UpperCAmelCase       (self     :  Any )					->  str:





				"""simple docstring"""


				for timesteps in [10, 50, 100, 1_000]:
					self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  Optional[Any] )					->  int:





				"""simple docstring"""


				for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]			,[0.00_02, 0.0_02, 0.02] ):
					self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_			,beta_end=SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  Optional[Any] )					->  Optional[int]:





				"""simple docstring"""


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






			def 				UpperCAmelCase       (self     :  Union[str, Any] )					->  Dict:





				"""simple docstring"""


				if torch_device == "mps":
					return
				lowerCAmelCase				       =     self.scheduler_classes[0]
				lowerCAmelCase				       =     self.get_scheduler_config()
				lowerCAmelCase				       =     scheduler_class(**SCREAMING_SNAKE_CASE_ )

				scheduler.set_timesteps(self.num_inference_steps )

				lowerCAmelCase				       =     torch.manual_seed(0 )

				lowerCAmelCase				       =     self.dummy_model()
				lowerCAmelCase				       =     self.dummy_sample_deter * scheduler.init_noise_sigma
				lowerCAmelCase				       =     sample.to(SCREAMING_SNAKE_CASE_ )

				for i, t in enumerate(scheduler.timesteps ):
					lowerCAmelCase				       =     scheduler.scale_model_input(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     model(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     scheduler.step(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,generator=SCREAMING_SNAKE_CASE_ )
					lowerCAmelCase				       =     output.prev_sample

				lowerCAmelCase				       =     torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
				lowerCAmelCase				       =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )

				assert abs(result_sum.item() - 1_38_49.38_77 ) < 1e-2
				assert abs(result_mean.item() - 18.03_31 ) < 5e-3






			def 				UpperCAmelCase       (self     :  str )					->  List[Any]:





				"""simple docstring"""


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






			def 				UpperCAmelCase       (self     :  List[Any] )					->  List[str]:





				"""simple docstring"""


				if torch_device == "mps":
					return
				lowerCAmelCase				       =     self.scheduler_classes[0]
				lowerCAmelCase				       =     self.get_scheduler_config(prediction_type='''v_prediction''' )
				lowerCAmelCase				       =     scheduler_class(**SCREAMING_SNAKE_CASE_ )

				scheduler.set_timesteps(self.num_inference_steps )

				lowerCAmelCase				       =     self.dummy_model()
				lowerCAmelCase				       =     self.dummy_sample_deter * scheduler.init_noise_sigma
				lowerCAmelCase				       =     sample.to(SCREAMING_SNAKE_CASE_ )

				lowerCAmelCase				       =     torch.manual_seed(0 )

				for i, t in enumerate(scheduler.timesteps ):
					lowerCAmelCase				       =     scheduler.scale_model_input(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     model(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     scheduler.step(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,generator=SCREAMING_SNAKE_CASE_ )
					lowerCAmelCase				       =     output.prev_sample

				lowerCAmelCase				       =     torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
				lowerCAmelCase				       =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )

				assert abs(result_sum.item() - 3_28.99_70 ) < 1e-2
				assert abs(result_mean.item() - 0.42_84 ) < 1e-3






			def 				UpperCAmelCase       (self     :  Dict )					->  Union[str, Any]:





				"""simple docstring"""


				if torch_device == "mps":
					return
				lowerCAmelCase				       =     self.scheduler_classes[0]
				lowerCAmelCase				       =     self.get_scheduler_config()
				lowerCAmelCase				       =     scheduler_class(**SCREAMING_SNAKE_CASE_ )

				scheduler.set_timesteps(self.num_inference_steps			,device=SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase				       =     torch.manual_seed(0 )

				lowerCAmelCase				       =     self.dummy_model()
				lowerCAmelCase				       =     self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_ ) * scheduler.init_noise_sigma

				for t in scheduler.timesteps:
					lowerCAmelCase				       =     scheduler.scale_model_input(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     model(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					lowerCAmelCase				       =     scheduler.step(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,generator=SCREAMING_SNAKE_CASE_ )
					lowerCAmelCase				       =     output.prev_sample

				lowerCAmelCase				       =     torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
				lowerCAmelCase				       =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )

				assert abs(result_sum.item() - 1_38_49.38_18 ) < 1e-1
				assert abs(result_mean.item() - 18.03_31 ) < 1e-3


