




"""simple docstring"""






from typing import Dict, Iterable, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase__  		=		logging.get_logger(__name__)






class lowerCAmelCase__			(  __snake_case		):






			'''simple docstring'''



			lowerCamelCase__   =    ["""pixel_values"""]
			def __init__(						self						,   lowercase = True						,   lowercase = None						,   lowercase = PILImageResampling.BICUBIC						,   lowercase = True						,   lowercase = None						,   lowercase = True						,   lowercase = 1 / 255						,   lowercase = True						,   lowercase = IMAGENET_DEFAULT_MEAN						,   lowercase = IMAGENET_DEFAULT_STD						,   **lowercase						,   ):
							super().__init__(**__UpperCamelCase						)
							_lowerCamelCase  :    Dict					=					size if size is not None else {'shortest_edge': 224}
							_lowerCamelCase  :    List[str]					=					get_size_dict(__UpperCamelCase						,   default_to_square=__UpperCamelCase						)
							_lowerCamelCase  :    Union[str, Any]					=					crop_size if crop_size is not None else {'height': 224, 'width': 224}
							_lowerCamelCase  :    Any					=					get_size_dict(__UpperCamelCase						,   param_name='crop_size'						)

							_lowerCamelCase  :    Optional[Any]					=					do_resize
							_lowerCamelCase  :    Optional[Any]					=					size
							_lowerCamelCase  :    int					=					resample
							_lowerCamelCase  :    Union[str, Any]					=					do_center_crop
							_lowerCamelCase  :    Any					=					crop_size
							_lowerCamelCase  :    List[str]					=					do_rescale
							_lowerCamelCase  :    Tuple					=					rescale_factor
							_lowerCamelCase  :    Optional[int]					=					do_normalize
							_lowerCamelCase  :    Union[str, Any]					=					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							_lowerCamelCase  :    Dict					=					image_std if image_std is not None else IMAGENET_DEFAULT_STD
			def       A_					(						self						,   lowercase						,   lowercase						,   lowercase = PILImageResampling.BICUBIC						,   lowercase = None						,   **lowercase						,   ):
							_lowerCamelCase  :    Union[str, Any]					=					get_size_dict(__UpperCamelCase						,   default_to_square=__UpperCamelCase						)
							# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
							if "shortest_edge" in size:
											_lowerCamelCase  :    Optional[Any]					=					int((256 / 224) * size['shortest_edge']						)
											_lowerCamelCase  :    Optional[Any]					=					get_resize_output_image_size(__UpperCamelCase						,   size=__UpperCamelCase						,   default_to_square=__UpperCamelCase						)
											_lowerCamelCase  :    List[Any]					=					{'height': output_size[0], 'width': output_size[1]}
							if "height" not in size_dict or "width" not in size_dict:
											raise ValueError(
											    F'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''						)
							return resize(
							    __UpperCamelCase						,   size=(size_dict['height'], size_dict['width'])						,   resample=__UpperCamelCase						,   data_format=__UpperCamelCase						,   **__UpperCamelCase						)
			def       A_					(						self						,   lowercase						,   lowercase						,   lowercase = None						,   **lowercase						,   ):
							_lowerCamelCase  :    Dict					=					get_size_dict(__UpperCamelCase						)
							if "height" not in size or "width" not in size:
											raise ValueError(F'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
							return center_crop(__UpperCamelCase						,   size=(size['height'], size['width'])						,   data_format=__UpperCamelCase						,   **__UpperCamelCase						)
			def       A_					(						self						,   lowercase						,   lowercase						,   lowercase = None						,   **lowercase						,   ):
							return rescale(__UpperCamelCase						,   scale=__UpperCamelCase						,   data_format=__UpperCamelCase						,   **__UpperCamelCase						)
			def       A_					(						self						,   lowercase						,   lowercase						,   lowercase						,   lowercase = None						,   **lowercase						,   ):
							return normalize(__UpperCamelCase						,   mean=__UpperCamelCase						,   std=__UpperCamelCase						,   data_format=__UpperCamelCase						,   **__UpperCamelCase						)






			def       A_					(						self						,   lowercase						,   lowercase = None						,   lowercase = None						,   lowercase = None						,   lowercase = None						,   lowercase = None						,   lowercase = None						,   lowercase = None						,   lowercase = None						,   lowercase = None						,   lowercase = None						,   lowercase = None						,   lowercase = ChannelDimension.FIRST						,   **lowercase						,   ):
							_lowerCamelCase  :    Dict					=					do_resize if do_resize is not None else self.do_resize
							_lowerCamelCase  :    Any					=					resample if resample is not None else self.resample
							_lowerCamelCase  :    List[Any]					=					do_center_crop if do_center_crop is not None else self.do_center_crop
							_lowerCamelCase  :    List[str]					=					do_rescale if do_rescale is not None else self.do_rescale
							_lowerCamelCase  :    int					=					rescale_factor if rescale_factor is not None else self.rescale_factor
							_lowerCamelCase  :    Any					=					do_normalize if do_normalize is not None else self.do_normalize
							_lowerCamelCase  :    int					=					image_mean if image_mean is not None else self.image_mean
							_lowerCamelCase  :    Optional[Any]					=					image_std if image_std is not None else self.image_std

							_lowerCamelCase  :    List[Any]					=					size if size is not None else self.size
							_lowerCamelCase  :    Dict					=					get_size_dict(__UpperCamelCase						,   default_to_square=__UpperCamelCase						)
							_lowerCamelCase  :    Union[str, Any]					=					crop_size if crop_size is not None else self.crop_size
							_lowerCamelCase  :    Optional[int]					=					get_size_dict(__UpperCamelCase						,   param_name='crop_size'						)

							_lowerCamelCase  :    Union[str, Any]					=					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:
											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.
							_lowerCamelCase  :    List[Any]					=					[to_numpy_array(__UpperCamelCase						) for image in images]

							if do_resize:
											_lowerCamelCase  :    Any					=					[self.resize(__UpperCamelCase						,   __UpperCamelCase						,   __UpperCamelCase						) for image in images]

							if do_center_crop:
											_lowerCamelCase  :    Union[str, Any]					=					[self.center_crop(__UpperCamelCase						,   __UpperCamelCase						) for image in images]

							if do_rescale:
											_lowerCamelCase  :    Union[str, Any]					=					[self.rescale(__UpperCamelCase						,   __UpperCamelCase						) for image in images]

							if do_normalize:
											_lowerCamelCase  :    Dict					=					[self.normalize(__UpperCamelCase						,   __UpperCamelCase						,   __UpperCamelCase						) for image in images]

							_lowerCamelCase  :    Optional[Any]					=					[to_channel_dimension_format(__UpperCamelCase						,   __UpperCamelCase						) for image in images]

							_lowerCamelCase  :    Tuple					=					{'pixel_values': images}
							return BatchFeature(data=__UpperCamelCase						,   tensor_type=__UpperCamelCase						)





"""simple docstring"""






def   _snake_case    (       lowercase__	):
				return "".join(chr(ord(lowercase__	) - 32	) if 'a' <= char <= 'z' else char for char in word	)


if __name__ == "__main__":
			from doctest import testmod

			testmod()