




'''simple docstring'''




import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 			UpperCamelCase_   (a__   ):







						"""simple docstring"""


						_lowerCAmelCase			  =    (EulerAncestralDiscreteScheduler,)
						_lowerCAmelCase			  =    1_0







						def 					_a			(   self					:				int							,       **_lowerCamelCase					:				List[str]			):





											"""simple docstring"""







											A_       :   List[str]       =							{
											    '''num_train_timesteps''': 1100,
											    '''beta_start''': 0.00_01,
											    '''beta_end''': 0.02,
											    '''beta_schedule''': '''linear''',
											}

											config.update(**_lowerCamelCase			)
											return config







						def 					_a			(   self					:				Union[str, Any]			):





											"""simple docstring"""







											for timesteps in [10, 50, 100, 1000]:
																self.check_over_configs(num_train_timesteps=_lowerCamelCase			)







						def 					_a			(   self					:				List[str]			):





											"""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=_lowerCamelCase							,       beta_end=_lowerCamelCase			)







						def 					_a			(   self					:				Union[str, Any]			):





											"""simple docstring"""







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







						def 					_a			(   self					:				List[Any]			):





											"""simple docstring"""







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







						def 					_a			(   self					:				Tuple			):





											"""simple docstring"""







											A_       :   Optional[int]       =							self.scheduler_classes[0]
											A_       :   Union[str, Any]       =							self.get_scheduler_config()
											A_       :   int       =							scheduler_class(**_lowerCamelCase			)

											scheduler.set_timesteps(self.num_inference_steps			)

											A_       :   Optional[Any]       =							torch.manual_seed(0			)

											A_       :   Tuple       =							self.dummy_model()
											A_       :   List[str]       =							self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											A_       :   List[Any]       =							sample.to(_lowerCamelCase			)

											for i, t in enumerate(scheduler.timesteps			):
																A_       :   Dict       =							scheduler.scale_model_input(_lowerCamelCase							,       _lowerCamelCase			)

																A_       :   Tuple       =							model(_lowerCamelCase							,       _lowerCamelCase			)

																A_       :   List[Any]       =							scheduler.step(_lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       generator=_lowerCamelCase			)
																A_       :   str       =							output.prev_sample

											A_       :   int       =							torch.sum(torch.abs(_lowerCamelCase			)			)
											A_       :   Dict       =							torch.mean(torch.abs(_lowerCamelCase			)			)

											assert abs(result_sum.item() - 1_52.31_92			) < 1E-2
											assert abs(result_mean.item() - 0.19_83			) < 1E-3







						def 					_a			(   self					:				Any			):





											"""simple docstring"""







											A_       :   Union[str, Any]       =							self.scheduler_classes[0]
											A_       :   Tuple       =							self.get_scheduler_config(prediction_type='''v_prediction'''			)
											A_       :   Union[str, Any]       =							scheduler_class(**_lowerCamelCase			)

											scheduler.set_timesteps(self.num_inference_steps			)

											A_       :   Optional[int]       =							torch.manual_seed(0			)

											A_       :   str       =							self.dummy_model()
											A_       :   Tuple       =							self.dummy_sample_deter * scheduler.init_noise_sigma
											A_       :   Optional[int]       =							sample.to(_lowerCamelCase			)

											for i, t in enumerate(scheduler.timesteps			):
																A_       :   List[Any]       =							scheduler.scale_model_input(_lowerCamelCase							,       _lowerCamelCase			)

																A_       :   Tuple       =							model(_lowerCamelCase							,       _lowerCamelCase			)

																A_       :   Optional[int]       =							scheduler.step(_lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       generator=_lowerCamelCase			)
																A_       :   Union[str, Any]       =							output.prev_sample

											A_       :   Optional[int]       =							torch.sum(torch.abs(_lowerCamelCase			)			)
											A_       :   str       =							torch.mean(torch.abs(_lowerCamelCase			)			)

											assert abs(result_sum.item() - 1_08.44_39			) < 1E-2
											assert abs(result_mean.item() - 0.14_12			) < 1E-3







						def 					_a			(   self					:				Any			):





											"""simple docstring"""







											A_       :   List[Any]       =							self.scheduler_classes[0]
											A_       :   str       =							self.get_scheduler_config()
											A_       :   Union[str, Any]       =							scheduler_class(**_lowerCamelCase			)

											scheduler.set_timesteps(self.num_inference_steps							,       device=_lowerCamelCase			)
											A_       :   int       =							torch.manual_seed(0			)

											A_       :   str       =							self.dummy_model()
											A_       :   Optional[int]       =							self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											A_       :   Optional[int]       =							sample.to(_lowerCamelCase			)

											for t in scheduler.timesteps:
																A_       :   str       =							scheduler.scale_model_input(_lowerCamelCase							,       _lowerCamelCase			)

																A_       :   Tuple       =							model(_lowerCamelCase							,       _lowerCamelCase			)

																A_       :   Tuple       =							scheduler.step(_lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       generator=_lowerCamelCase			)
																A_       :   str       =							output.prev_sample

											A_       :   List[Any]       =							torch.sum(torch.abs(_lowerCamelCase			)			)
											A_       :   Optional[int]       =							torch.mean(torch.abs(_lowerCamelCase			)			)

											assert abs(result_sum.item() - 1_52.31_92			) < 1E-2
											assert abs(result_mean.item() - 0.19_83			) < 1E-3











"""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 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, logging


_UpperCamelCase     			=  logging.get_logger(__name__)







class 					lowerCamelCase__					(       snake_case ):
    SCREAMING_SNAKE_CASE		=			['''pixel_values''']






    def __init__( self	,A = True	,A = None	,A = PILImageResampling.BILINEAR	,A = True	,A = 1 / 255	,A = True	,A = None	,A = None	,**A	,):
          super().__init__(**A					)
          UpperCAmelCase							=       size if size is not None else {"""height""": 224, """width""": 224}
          UpperCAmelCase							=       get_size_dict(A					)
          UpperCAmelCase							=       do_resize
          UpperCAmelCase							=       do_rescale
          UpperCAmelCase							=       do_normalize
          UpperCAmelCase							=       size
          UpperCAmelCase							=       resample
          UpperCAmelCase							=       rescale_factor
          UpperCAmelCase							=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          UpperCAmelCase							=       image_std if image_std is not None else IMAGENET_STANDARD_STD






    def 				_UpperCamelCase							( self	,A	,A	,A = PILImageResampling.BILINEAR	,A = None	,**A	,):
          UpperCAmelCase							=       get_size_dict(A					)
          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(
              A	,size=(size["""height"""], size["""width"""])	,resample=A	,data_format=A	,**A					)






    def 				_UpperCamelCase							( self	,A	,A	,A = None	,**A					):
          return rescale(A	,scale=A	,data_format=A	,**A					)






    def 				_UpperCamelCase							( self	,A	,A	,A	,A = None	,**A	,):
          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 = ChannelDimension.FIRST	,**A	,):
          UpperCAmelCase							=       do_resize if do_resize is not None else self.do_resize
          UpperCAmelCase							=       do_rescale if do_rescale is not None else self.do_rescale
          UpperCAmelCase							=       do_normalize if do_normalize is not None else self.do_normalize
          UpperCAmelCase							=       resample if resample is not None else self.resample
          UpperCAmelCase							=       rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCAmelCase							=       image_mean if image_mean is not None else self.image_mean
          UpperCAmelCase							=       image_std if image_std is not None else self.image_std

          UpperCAmelCase							=       size if size is not None else self.size
          UpperCAmelCase							=       get_size_dict(A					)

          UpperCAmelCase							=       make_list_of_images(A					)

          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_rescale and rescale_factor is None:
                raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

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

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

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

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

          UpperCAmelCase							=       [to_channel_dimension_format(A	,A					) for image in images]

          UpperCAmelCase							=       {"""pixel_values""": images}
          return BatchFeature(data=A	,tensor_type=A					)



