"""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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


a							=logging.get_logger(__name__)


if is_vision_available():
	import PIL






class    __UpperCAmelCase						(	__lowerCAmelCase	):
				A__      :   Union[str, Any]  							=       ['''pixel_values''']

				def __init__(      self	, _lowerCamelCase = True	, _lowerCamelCase = None	, _lowerCamelCase = PILImageResampling.BICUBIC	, _lowerCamelCase = True	, _lowerCamelCase = None	, _lowerCamelCase = True	, _lowerCamelCase = 1 / 255	, _lowerCamelCase = True	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = True	, **_lowerCamelCase	, ):
										super().__init__(**_lowerCamelCase      )
										lowerCamelCase__					=size if size is not None else {"shortest_edge": 224}
										lowerCamelCase__					=get_size_dict(_lowerCamelCase	, default_to_square=_lowerCamelCase      )
										lowerCamelCase__					=crop_size if crop_size is not None else {"height": 224, "width": 224}
										lowerCamelCase__					=get_size_dict(_lowerCamelCase      )

										lowerCamelCase__					=do_resize
										lowerCamelCase__					=size
										lowerCamelCase__					=resample
										lowerCamelCase__					=do_center_crop
										lowerCamelCase__					=crop_size
										lowerCamelCase__					=do_rescale
										lowerCamelCase__					=rescale_factor
										lowerCamelCase__					=do_normalize
										lowerCamelCase__					=image_mean if image_mean is not None else OPENAI_CLIP_MEAN
										lowerCamelCase__					=image_std if image_std is not None else OPENAI_CLIP_STD
										lowerCamelCase__					=do_convert_rgb

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase = PILImageResampling.BICUBIC	, _lowerCamelCase = None	, **_lowerCamelCase	, ):
										lowerCamelCase__					=get_size_dict(_lowerCamelCase	, default_to_square=_lowerCamelCase      )
										lowerCamelCase__					=get_resize_output_image_size(
										    _lowerCamelCase	, size=(size["height"], size["width"])	, default_to_square=_lowerCamelCase      )
										return resize(_lowerCamelCase	, size=_lowerCamelCase	, resample=_lowerCamelCase	, data_format=_lowerCamelCase	, **_lowerCamelCase      )

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase = None	, **_lowerCamelCase	, ):
										lowerCamelCase__					=get_size_dict(_lowerCamelCase      )
										return center_crop(_lowerCamelCase	, size=(size["height"], size["width"])	, data_format=_lowerCamelCase	, **_lowerCamelCase      )

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase = None	, **_lowerCamelCase	, ):
										return rescale(_lowerCamelCase	, scale=_lowerCamelCase	, data_format=_lowerCamelCase	, **_lowerCamelCase      )

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase = None	, **_lowerCamelCase	, ):
										return normalize(_lowerCamelCase	, mean=_lowerCamelCase	, std=_lowerCamelCase	, data_format=_lowerCamelCase	, **_lowerCamelCase      )






				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = ChannelDimension.FIRST	, **_lowerCamelCase	, ):
										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(_lowerCamelCase	, default_to_square=_lowerCamelCase      )
										lowerCamelCase__					=resample if resample is not None else self.resample
										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(_lowerCamelCase      )
										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__					=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										lowerCamelCase__					=make_list_of_images(_lowerCamelCase      )

										if not valid_images(_lowerCamelCase      ):
																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."      )

										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."      )

										# PIL RGBA images are converted to RGB
										if do_convert_rgb:
																lowerCamelCase__					=[convert_to_rgb(_lowerCamelCase      ) for image in images]

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

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

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

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

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

										lowerCamelCase__					=[to_channel_dimension_format(_lowerCamelCase	, _lowerCamelCase      ) for image in images]

										lowerCamelCase__					={"pixel_values": images}
										return BatchFeature(data=_lowerCamelCase	, tensor_type=_lowerCamelCase      )








'''simple docstring'''
import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Any    =					(HeunDiscreteScheduler,)
       snake_case_ :     Any    =					10

       def     snake_case__			(    self					,		**lowerCamelCase__     )    ->					int:

           """simple docstring"""



           __lowercase							=						{
               """num_train_timesteps""": 1100,
               """beta_start""": 0.00_01,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
           }

           config.update(**lowerCamelCase__     )
           return config

       def     snake_case__			(    self     )    ->					Optional[Any]:

           """simple docstring"""



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

       def     snake_case__			(    self     )    ->					Dict:

           """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     snake_case__			(    self     )    ->					Any:

           """simple docstring"""



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

       def     snake_case__			(    self     )    ->					int:

           """simple docstring"""



           for clip_sample_range in [1.0, 2.0, 3.0]:
               self.check_over_configs(clip_sample_range=lowerCamelCase__					,		clip_sample=lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					Optional[int]:

           """simple docstring"""



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

       def     snake_case__			(    self     )    ->					str:

           """simple docstring"""



           __lowercase							=						self.scheduler_classes[0]
           __lowercase							=						self.get_scheduler_config()
           __lowercase							=						scheduler_class(**lowerCamelCase__     )

           scheduler.set_timesteps(self.num_inference_steps     )

           __lowercase							=						self.dummy_model()
           __lowercase							=						self.dummy_sample_deter * scheduler.init_noise_sigma
           __lowercase							=						sample.to(lowerCamelCase__     )

           for i, t in enumerate(scheduler.timesteps     ):
               __lowercase							=						scheduler.scale_model_input(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						model(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						scheduler.step(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )
               __lowercase							=						output.prev_sample

           __lowercase							=						torch.sum(torch.abs(lowerCamelCase__     )     )
           __lowercase							=						torch.mean(torch.abs(lowerCamelCase__     )     )

           if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 0.12_33     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3
           else:
               # CUDA
               assert abs(result_sum.item() - 0.12_33     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3

       def     snake_case__			(    self     )    ->					str:

           """simple docstring"""



           __lowercase							=						self.scheduler_classes[0]
           __lowercase							=						self.get_scheduler_config(prediction_type="""v_prediction"""     )
           __lowercase							=						scheduler_class(**lowerCamelCase__     )

           scheduler.set_timesteps(self.num_inference_steps     )

           __lowercase							=						self.dummy_model()
           __lowercase							=						self.dummy_sample_deter * scheduler.init_noise_sigma
           __lowercase							=						sample.to(lowerCamelCase__     )

           for i, t in enumerate(scheduler.timesteps     ):
               __lowercase							=						scheduler.scale_model_input(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						model(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						scheduler.step(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )
               __lowercase							=						output.prev_sample

           __lowercase							=						torch.sum(torch.abs(lowerCamelCase__     )     )
           __lowercase							=						torch.mean(torch.abs(lowerCamelCase__     )     )

           if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 4.6_9_3_4E-0_7     ) < 1E-2
               assert abs(result_mean.item() - 6.1_1_1_2E-1_0     ) < 1E-3
           else:
               # CUDA
               assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2E-0_7     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3

       def     snake_case__			(    self     )    ->					Dict:

           """simple docstring"""



           __lowercase							=						self.scheduler_classes[0]
           __lowercase							=						self.get_scheduler_config()
           __lowercase							=						scheduler_class(**lowerCamelCase__     )

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

           __lowercase							=						self.dummy_model()
           __lowercase							=						self.dummy_sample_deter.to(lowerCamelCase__     ) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
               __lowercase							=						scheduler.scale_model_input(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						model(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						scheduler.step(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )
               __lowercase							=						output.prev_sample

           __lowercase							=						torch.sum(torch.abs(lowerCamelCase__     )     )
           __lowercase							=						torch.mean(torch.abs(lowerCamelCase__     )     )

           if str(lowerCamelCase__     ).startswith("""cpu"""     ):
               # The following sum varies between 148 and 156 on mps. Why?
               assert abs(result_sum.item() - 0.12_33     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3
           elif str(lowerCamelCase__     ).startswith("""mps"""     ):
               # Larger tolerance on mps
               assert abs(result_mean.item() - 0.00_02     ) < 1E-2
           else:
               # CUDA
               assert abs(result_sum.item() - 0.12_33     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3




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

           """simple docstring"""



           __lowercase							=						self.scheduler_classes[0]
           __lowercase							=						self.get_scheduler_config()
           __lowercase							=						scheduler_class(**lowerCamelCase__					,		use_karras_sigmas=lowerCamelCase__     )

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

           __lowercase							=						self.dummy_model()
           __lowercase							=						self.dummy_sample_deter.to(lowerCamelCase__     ) * scheduler.init_noise_sigma
           __lowercase							=						sample.to(lowerCamelCase__     )

           for t in scheduler.timesteps:
               __lowercase							=						scheduler.scale_model_input(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						model(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						scheduler.step(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )
               __lowercase							=						output.prev_sample

           __lowercase							=						torch.sum(torch.abs(lowerCamelCase__     )     )
           __lowercase							=						torch.mean(torch.abs(lowerCamelCase__     )     )

           assert abs(result_sum.item() - 0.0_00_15     ) < 1E-2
           assert abs(result_mean.item() - 1.9_8_6_9_5_5_4_5_3_5_0_3_4_6_9_5E-0_7     ) < 1E-2




