


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_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


snake_case__			 =						logging.get_logger(__name__)


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     ['pixel_values']




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






									"""simple docstring"""






									super().__init__(**__A						)
									a__ :Any						  =    size if size is not None else {"height": 256, "width": 256}
									a__ :Optional[Any]						  =    get_size_dict(__A						)
									a__ :Optional[Any]						  =    crop_size if crop_size is not None else {"height": 224, "width": 224}
									a__ :Optional[Any]						  =    get_size_dict(__A   ,     param_name="crop_size"						)

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




						def  _snake_case					(	self						:   Any   ,     __A						:   np.ndarray   ,     __A						:   Dict[str, int]   ,     __A						:   PILImageResampling = PIL.Image.BICUBIC   ,     __A						:   Optional[Union[str, ChannelDimension]] = None   ,     **__A						:   Optional[int]   ,     )      ->np.ndarray:






									"""simple docstring"""






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




						def  _snake_case					(	self						:   Tuple   ,     __A						:   np.ndarray   ,     __A						:   Dict[str, int]   ,     __A						:   Optional[Union[str, ChannelDimension]] = None   ,     **__A						:   Union[str, Any]   ,     )      ->np.ndarray:






									"""simple docstring"""






									a__ :Optional[Any]						  =    get_size_dict(__A						)
									if "height" not in size or "width" not in size:
												raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
									return center_crop(__A   ,     size=(size["height"], size["width"])   ,     data_format=__A   ,     **__A						)




						def  _snake_case					(	self						:   Optional[int]   ,     __A						:   np.ndarray   ,     __A						:   Union[int, float]   ,     __A						:   Optional[Union[str, ChannelDimension]] = None   ,     **__A						:   int   ,     )      ->Union[str, Any]:






									"""simple docstring"""






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




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






									"""simple docstring"""






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






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






									"""simple docstring"""






									a__ :List[Any]						  =    do_resize if do_resize is not None else self.do_resize
									a__ :Any						  =    resample if resample is not None else self.resample
									a__ :int						  =    do_center_crop if do_center_crop is not None else self.do_center_crop
									a__ :str						  =    do_rescale if do_rescale is not None else self.do_rescale
									a__ :Any						  =    rescale_factor if rescale_factor is not None else self.rescale_factor
									a__ :List[Any]						  =    do_normalize if do_normalize is not None else self.do_normalize
									a__ :Any						  =    image_mean if image_mean is not None else self.image_mean
									a__ :List[str]						  =    image_std if image_std is not None else self.image_std

									a__ :Tuple						  =    size if size is not None else self.size
									a__ :Optional[int]						  =    get_size_dict(__A						)
									a__ :str						  =    crop_size if crop_size is not None else self.crop_size
									a__ :List[Any]						  =    get_size_dict(__A   ,     param_name="crop_size"						)

									a__ :Optional[int]						  =    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 or resample is None:
												raise ValueError("Size and resample 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."						)

									# All transformations expect numpy arrays.
									a__ :Optional[Any]						  =    [to_numpy_array(__A						) for image in images]

									if do_resize:
												a__ :Any						  =    [self.resize(image=__A   ,     size=__A   ,     resample=__A						) for image in images]

									if do_center_crop:
												a__ :Dict						  =    [self.center_crop(image=__A   ,     size=__A						) for image in images]

									if do_rescale:
												a__ :Optional[Any]						  =    [self.rescale(image=__A   ,     scale=__A						) for image in images]

									if do_normalize:
												a__ :List[str]						  =    [self.normalize(image=__A   ,     mean=__A   ,     std=__A						) for image in images]

									a__ :str						  =    [to_channel_dimension_format(__A   ,     __A						) for image in images]

									a__ :List[Any]						  =    {"pixel_values": images}
									return BatchFeature(data=__A   ,     tensor_type=__A						)






import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


snake_case__			 =						'''scheduler_config.json'''


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     1
						lowerCamelCase_         =     2
						lowerCamelCase_         =     3
						lowerCamelCase_         =     4
						lowerCamelCase_         =     5
						lowerCamelCase_         =     6
						lowerCamelCase_         =     7
						lowerCamelCase_         =     8
						lowerCamelCase_         =     9
						lowerCamelCase_         =     10
						lowerCamelCase_         =     11
						lowerCamelCase_         =     12
						lowerCamelCase_         =     13
						lowerCamelCase_         =     14






@dataclass
class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     42






class 					lowerCAmelCase_ :
						lowerCamelCase_         =     SCHEDULER_CONFIG_NAME
						lowerCamelCase_         =     []
						lowerCamelCase_         =     True




						@classmethod
						def  _snake_case					(	cls						:   Union[str, Any]   ,     __A						:   Dict[str, Any] = None   ,     __A						:   Optional[str] = None   ,     __A						:   Optional[int]=False   ,     **__A						:   Union[str, Any]   ,     )      ->List[Any]:






									"""simple docstring"""






									a__  ,  a__  ,  a__ :List[Any]						  =    cls.load_config(
									    pretrained_model_name_or_path=__A   ,     subfolder=__A   ,     return_unused_kwargs=__A   ,     return_commit_hash=__A   ,     **__A   ,     )
									return cls.from_config(__A   ,     return_unused_kwargs=__A   ,     **__A						)




						def  _snake_case					(	self						:   str   ,     __A						:   Union[str, os.PathLike]   ,     __A						:   bool = False   ,     **__A						:   Optional[Any]						)      ->str:






									"""simple docstring"""






									self.save_config(save_directory=__A   ,     push_to_hub=__A   ,     **__A						)




						@property
						def  _snake_case					(	self						:   List[Any]						)      ->Dict:






									"""simple docstring"""






									return self._get_compatibles()






						@classmethod
						def  _snake_case					(	cls						:   Dict						)      ->int:






									"""simple docstring"""






									a__ :Optional[int]						  =    list(set([cls.__name__] + cls._compatibles						)						)
									a__ :Union[str, Any]						  =    importlib.import_module(__name__.split("."						)[0]						)
									a__ :Optional[int]						  =    [
									    getattr(__A   ,     __A						) for c in compatible_classes_str if hasattr(__A   ,     __A						)
									]
									return compatible_classes


