





'''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,
    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, logging


A   : str             =			logging.get_logger(__name__)







class 			lowerCamelCase					(						__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    ["pixel_values"]


					def __init__(					self	:  Optional[Any]       ,     __snake_case	:  bool = True       ,     __snake_case	:  Optional[Dict[str, int]] = None       ,     __snake_case	:  PILImageResampling = PILImageResampling.BILINEAR       ,     __snake_case	:  bool = True       ,     __snake_case	:  Dict[str, int] = None       ,     __snake_case	:  bool = True       ,     __snake_case	:  Union[int, float] = 1 / 2_55       ,     __snake_case	:  bool = True       ,     __snake_case	:  Optional[Union[float, List[float]]] = None       ,     __snake_case	:  Optional[Union[float, List[float]]] = None       ,     **__snake_case	:  Optional[int]       ,     ):





											'''simple docstring'''





											super().__init__(**__snake_case       )
											_snake_case:   Any  					=		size if size is not None else {'shortest_edge': 2_56}
											_snake_case:   int  					=		get_size_dict(__snake_case       ,     default_to_square=__snake_case       )
											_snake_case:   List[str]  					=		crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
											_snake_case:   int  					=		get_size_dict(__snake_case       )
											_snake_case:   Dict  					=		do_resize
											_snake_case:   Any  					=		size
											_snake_case:   Union[str, Any]  					=		resample
											_snake_case:   List[Any]  					=		do_center_crop
											_snake_case:   Union[str, Any]  					=		crop_size
											_snake_case:   Dict  					=		do_rescale
											_snake_case:   Optional[int]  					=		rescale_factor
											_snake_case:   Optional[Any]  					=		do_normalize
											_snake_case:   Dict  					=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											_snake_case:   Any  					=		image_std if image_std is not None else IMAGENET_STANDARD_STD


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





											'''simple docstring'''





											_snake_case:   List[str]  					=		get_size_dict(__snake_case       ,     default_to_square=__snake_case       )
											if "shortest_edge" not in size:
																	raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''       )
											_snake_case:   List[str]  					=		get_resize_output_image_size(__snake_case       ,     size=size['shortest_edge']       ,     default_to_square=__snake_case       )
											return resize(__snake_case       ,     size=__snake_case       ,     resample=__snake_case       ,     data_format=__snake_case       ,     **__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ,     __snake_case	:  np.ndarray       ,     __snake_case	:  Dict[str, int]       ,     __snake_case	:  Optional[Union[str, ChannelDimension]] = None       ,     **__snake_case	:  List[Any]       ,     ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		get_size_dict(__snake_case       )
											return center_crop(__snake_case       ,     size=(size['height'], size['width'])       ,     data_format=__snake_case       ,     **__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ,     __snake_case	:  np.ndarray       ,     __snake_case	:  float       ,     __snake_case	:  Optional[Union[str, ChannelDimension]] = None       ,     **__snake_case	:  Optional[int]       ):





											'''simple docstring'''





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


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





											'''simple docstring'''





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




					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ,     __snake_case	:  ImageInput       ,     __snake_case	:  Optional[bool] = None       ,     __snake_case	:  Dict[str, int] = None       ,     __snake_case	:  PILImageResampling = None       ,     __snake_case	:  bool = None       ,     __snake_case	:  Dict[str, int] = None       ,     __snake_case	:  Optional[bool] = None       ,     __snake_case	:  Optional[float] = None       ,     __snake_case	:  Optional[bool] = None       ,     __snake_case	:  Optional[Union[float, List[float]]] = None       ,     __snake_case	:  Optional[Union[float, List[float]]] = None       ,     __snake_case	:  Optional[Union[str, TensorType]] = None       ,     __snake_case	:  Union[str, ChannelDimension] = ChannelDimension.FIRST       ,     **__snake_case	:  str       ,     ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		do_resize if do_resize is not None else self.do_resize
											_snake_case:   str  					=		size if size is not None else self.size
											_snake_case:   Optional[int]  					=		get_size_dict(__snake_case       ,     default_to_square=__snake_case       )
											_snake_case:   Optional[int]  					=		resample if resample is not None else self.resample
											_snake_case:   Tuple  					=		do_center_crop if do_center_crop is not None else self.do_center_crop
											_snake_case:   List[Any]  					=		crop_size if crop_size is not None else self.crop_size
											_snake_case:   Optional[Any]  					=		get_size_dict(__snake_case       )
											_snake_case:   str  					=		do_rescale if do_rescale is not None else self.do_rescale
											_snake_case:   Dict  					=		rescale_factor if rescale_factor is not None else self.rescale_factor
											_snake_case:   Optional[int]  					=		do_normalize if do_normalize is not None else self.do_normalize
											_snake_case:   Optional[int]  					=		image_mean if image_mean is not None else self.image_mean
											_snake_case:   Any  					=		image_std if image_std is not None else self.image_std

											_snake_case:   Optional[int]  					=		make_list_of_images(__snake_case       )

											if not valid_images(__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_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.'       )

											# All transformations expect numpy arrays.
											_snake_case:   List[Any]  					=		[to_numpy_array(__snake_case       ) for image in images]

											if do_resize:
																	_snake_case:   List[Any]  					=		[self.resize(image=__snake_case       ,     size=__snake_case       ,     resample=__snake_case       ) for image in images]

											if do_center_crop:
																	_snake_case:   Any  					=		[self.center_crop(image=__snake_case       ,     size=__snake_case       ) for image in images]

											if do_rescale:
																	_snake_case:   List[Any]  					=		[self.rescale(image=__snake_case       ,     scale=__snake_case       ) for image in images]

											if do_normalize:
																	_snake_case:   Any  					=		[self.normalize(image=__snake_case       ,     mean=__snake_case       ,     std=__snake_case       ) for image in images]

											_snake_case:   List[Any]  					=		[to_channel_dimension_format(__snake_case       ,     __snake_case       ) for image in images]

											_snake_case:   str  					=		{'pixel_values': images}
											return BatchFeature(data=__snake_case       ,     tensor_type=__snake_case       )












'''simple docstring'''






A   : List[str]             =			'\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

A   : List[str]             =			[{'type': 'code', 'content': INSTALL_CONTENT}]
A   : Dict             =			{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}





