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_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


_lowerCamelCase					:   Dict							=		logging.get_logger(__name__)


class 						lowercase    (     a     ):
							lowercase__						:       Union[str, Any]								=							["""pixel_values"""]




							def __init__(						self   :					str	,							_UpperCamelCase   :					bool = True	,							_UpperCamelCase   :					Dict[str, int] = None	,							_UpperCamelCase   :					float = None	,							_UpperCamelCase   :					PILImageResampling = PILImageResampling.BILINEAR	,							_UpperCamelCase   :					bool = True	,							_UpperCamelCase   :					Union[int, float] = 1 / 255	,							_UpperCamelCase   :					bool = True	,							_UpperCamelCase   :					Optional[Union[float, List[float]]] = None	,							_UpperCamelCase   :					Optional[Union[float, List[float]]] = None	,							**_UpperCamelCase   :					Tuple	,							)					->				None:



												'''simple docstring'''

												super().__init__(**_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       size if size is not None else {"shortest_edge": 384}
												SCREAMING_SNAKE_CASE    			=       get_size_dict(_UpperCamelCase	,							default_to_square=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       do_resize
												SCREAMING_SNAKE_CASE    			=       size
												# Default value set here for backwards compatibility where the value in config is None
												SCREAMING_SNAKE_CASE    			=       crop_pct if crop_pct is not None else 224 / 256
												SCREAMING_SNAKE_CASE    			=       resample
												SCREAMING_SNAKE_CASE    			=       do_rescale
												SCREAMING_SNAKE_CASE    			=       rescale_factor
												SCREAMING_SNAKE_CASE    			=       do_normalize
												SCREAMING_SNAKE_CASE    			=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												SCREAMING_SNAKE_CASE    			=       image_std if image_std is not None else IMAGENET_STANDARD_STD




							def 	__snake_case(						self   :					List[Any]	,							_UpperCamelCase   :					np.ndarray	,							_UpperCamelCase   :					Dict[str, int]	,							_UpperCamelCase   :					float	,							_UpperCamelCase   :					PILImageResampling = PILImageResampling.BICUBIC	,							_UpperCamelCase   :					Optional[Union[str, ChannelDimension]] = None	,							**_UpperCamelCase   :					Dict	,							)					->				np.ndarray:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       get_size_dict(_UpperCamelCase	,							default_to_square=_UpperCamelCase      )
												if "shortest_edge" not in size:
																	raise ValueError(F"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"      )
												SCREAMING_SNAKE_CASE    			=       size["shortest_edge"]

												if shortest_edge < 384:
																	# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																	SCREAMING_SNAKE_CASE    			=       int(shortest_edge / crop_pct      )
																	SCREAMING_SNAKE_CASE    			=       get_resize_output_image_size(_UpperCamelCase	,							size=_UpperCamelCase	,							default_to_square=_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       resize(image=_UpperCamelCase	,							size=_UpperCamelCase	,							resample=_UpperCamelCase	,							data_format=_UpperCamelCase	,							**_UpperCamelCase      )
																	# then crop to (shortest_edge, shortest_edge)
																	return center_crop(image=_UpperCamelCase	,							size=(shortest_edge, shortest_edge)	,							data_format=_UpperCamelCase	,							**_UpperCamelCase      )
												else:
																	# warping (no cropping) when evaluated at 384 or larger
																	return resize(
																	    _UpperCamelCase	,							size=(shortest_edge, shortest_edge)	,							resample=_UpperCamelCase	,							data_format=_UpperCamelCase	,							**_UpperCamelCase      )




							def 	__snake_case(						self   :					int	,							_UpperCamelCase   :					np.ndarray	,							_UpperCamelCase   :					Union[int, float]	,							_UpperCamelCase   :					Optional[Union[str, ChannelDimension]] = None	,							**_UpperCamelCase   :					str	,							)					->				List[str]:



												'''simple docstring'''

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




							def 	__snake_case(						self   :					Any	,							_UpperCamelCase   :					np.ndarray	,							_UpperCamelCase   :					Union[float, List[float]]	,							_UpperCamelCase   :					Union[float, List[float]]	,							_UpperCamelCase   :					Optional[Union[str, ChannelDimension]] = None	,							**_UpperCamelCase   :					List[Any]	,							)					->				np.ndarray:



												'''simple docstring'''

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





							def 	__snake_case(						self   :					List[str]	,							_UpperCamelCase   :					ImageInput	,							_UpperCamelCase   :					bool = None	,							_UpperCamelCase   :					Dict[str, int] = None	,							_UpperCamelCase   :					float = None	,							_UpperCamelCase   :					PILImageResampling = None	,							_UpperCamelCase   :					bool = None	,							_UpperCamelCase   :					float = None	,							_UpperCamelCase   :					bool = None	,							_UpperCamelCase   :					Optional[Union[float, List[float]]] = None	,							_UpperCamelCase   :					Optional[Union[float, List[float]]] = None	,							_UpperCamelCase   :					Optional[Union[str, TensorType]] = None	,							_UpperCamelCase   :					ChannelDimension = ChannelDimension.FIRST	,							**_UpperCamelCase   :					List[Any]	,							)					->				PIL.Image.Image:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       do_resize if do_resize is not None else self.do_resize
												SCREAMING_SNAKE_CASE    			=       crop_pct if crop_pct is not None else self.crop_pct
												SCREAMING_SNAKE_CASE    			=       resample if resample is not None else self.resample
												SCREAMING_SNAKE_CASE    			=       do_rescale if do_rescale is not None else self.do_rescale
												SCREAMING_SNAKE_CASE    			=       rescale_factor if rescale_factor is not None else self.rescale_factor
												SCREAMING_SNAKE_CASE    			=       do_normalize if do_normalize is not None else self.do_normalize
												SCREAMING_SNAKE_CASE    			=       image_mean if image_mean is not None else self.image_mean
												SCREAMING_SNAKE_CASE    			=       image_std if image_std is not None else self.image_std

												SCREAMING_SNAKE_CASE    			=       size if size is not None else self.size
												SCREAMING_SNAKE_CASE    			=       get_size_dict(_UpperCamelCase	,							default_to_square=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       make_list_of_images(_UpperCamelCase      )

												if not valid_images(_UpperCamelCase      ):
																	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_resize and size["shortest_edge"] < 384 and crop_pct is None:
																	raise ValueError("crop_pct must be specified if size < 384."      )

												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.
												SCREAMING_SNAKE_CASE    			=       [to_numpy_array(_UpperCamelCase      ) for image in images]

												if do_resize:
																	SCREAMING_SNAKE_CASE    			=       [self.resize(image=_UpperCamelCase	,							size=_UpperCamelCase	,							crop_pct=_UpperCamelCase	,							resample=_UpperCamelCase      ) for image in images]

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

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

												SCREAMING_SNAKE_CASE    			=       [to_channel_dimension_format(_UpperCamelCase	,							_UpperCamelCase      ) for image in images]

												SCREAMING_SNAKE_CASE    			=       {"pixel_values": images}
												return BatchFeature(data=_UpperCamelCase	,							tensor_type=_UpperCamelCase      )





import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 						lowercase    (     a     ):
							lowercase__						:       Tuple								=							(KDPMaDiscreteScheduler,)
							lowercase__						:       Optional[int]								=							10




							def 	__snake_case(						self   :					Optional[Any]	,							**_UpperCamelCase   :					List[Any]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       {
												    "num_train_timesteps": 1_100,
												    "beta_start": 0.0_0_0_1,
												    "beta_end": 0.0_2,
												    "beta_schedule": "linear",
												}

												config.update(**_UpperCamelCase      )
												return config




							def 	__snake_case(						self   :					int      )					->				List[Any]:



												'''simple docstring'''

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




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



												'''simple docstring'''

												for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]	,							[0.0_0_0_2, 0.0_0_2, 0.0_2]      ):
																	self.check_over_configs(beta_start=_UpperCamelCase	,							beta_end=_UpperCamelCase      )




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



												'''simple docstring'''

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




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



												'''simple docstring'''

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




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE    			=       self.get_scheduler_config(prediction_type="v_prediction"      )
												SCREAMING_SNAKE_CASE    			=       scheduler_class(**_UpperCamelCase      )

												scheduler.set_timesteps(self.num_inference_steps      )

												SCREAMING_SNAKE_CASE    			=       self.dummy_model()
												SCREAMING_SNAKE_CASE    			=       self.dummy_sample_deter * scheduler.init_noise_sigma
												SCREAMING_SNAKE_CASE    			=       sample.to(_UpperCamelCase      )

												for i, t in enumerate(scheduler.timesteps      ):
																	SCREAMING_SNAKE_CASE    			=       scheduler.scale_model_input(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       scheduler.step(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       output.prev_sample

												SCREAMING_SNAKE_CASE    			=       torch.sum(torch.abs(_UpperCamelCase      )      )
												SCREAMING_SNAKE_CASE    			=       torch.mean(torch.abs(_UpperCamelCase      )      )

												if torch_device in ["cpu", "mps"]:
																	assert abs(result_sum.item() - 4.6_934e-07      ) < 1e-2
																	assert abs(result_mean.item() - 6.1_112e-10      ) < 1e-3
												else:
																	# CUDA
																	assert abs(result_sum.item() - 4.693_428_650_170_972e-07      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_0_0_2      ) < 1e-3




							def 	__snake_case(						self   :					Dict      )					->				int:



												'''simple docstring'''

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

												scheduler.set_timesteps(self.num_inference_steps      )

												SCREAMING_SNAKE_CASE    			=       self.dummy_model()
												SCREAMING_SNAKE_CASE    			=       self.dummy_sample_deter * scheduler.init_noise_sigma
												SCREAMING_SNAKE_CASE    			=       sample.to(_UpperCamelCase      )

												for i, t in enumerate(scheduler.timesteps      ):
																	SCREAMING_SNAKE_CASE    			=       scheduler.scale_model_input(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       scheduler.step(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       output.prev_sample

												SCREAMING_SNAKE_CASE    			=       torch.sum(torch.abs(_UpperCamelCase      )      )
												SCREAMING_SNAKE_CASE    			=       torch.mean(torch.abs(_UpperCamelCase      )      )

												if torch_device in ["cpu", "mps"]:
																	assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3
												else:
																	# CUDA
																	assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3





							def 	__snake_case(						self   :					Tuple      )					->				str:



												'''simple docstring'''

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

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

												SCREAMING_SNAKE_CASE    			=       self.dummy_model()
												SCREAMING_SNAKE_CASE    			=       self.dummy_sample_deter.to(_UpperCamelCase      ) * scheduler.init_noise_sigma

												for t in scheduler.timesteps:
																	SCREAMING_SNAKE_CASE    			=       scheduler.scale_model_input(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       scheduler.step(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       output.prev_sample

												SCREAMING_SNAKE_CASE    			=       torch.sum(torch.abs(_UpperCamelCase      )      )
												SCREAMING_SNAKE_CASE    			=       torch.mean(torch.abs(_UpperCamelCase      )      )

												if str(_UpperCamelCase      ).startswith("cpu"      ):
																	# The following sum varies between 148 and 156 on mps. Why?
																	assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3
												else:
																	# CUDA
																	assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3




