


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


lowerCAmelCase__						:Optional[int]         =							logging.get_logger(__name__)

class 	__a   (       UpperCAmelCase    ):
		_a			:						List[str]    			=			['pixel_values']
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE = True    ,					_SCREAMING_SNAKE_CASE = None    ,					_SCREAMING_SNAKE_CASE = PIL.Image.NEAREST    ,					_SCREAMING_SNAKE_CASE = False    ,					_SCREAMING_SNAKE_CASE = None    ,					_SCREAMING_SNAKE_CASE = 1 / 255    ,					_SCREAMING_SNAKE_CASE = False    ,					_SCREAMING_SNAKE_CASE = True    ,					_SCREAMING_SNAKE_CASE = True    ,					_SCREAMING_SNAKE_CASE = None    ,					_SCREAMING_SNAKE_CASE = None    ,					_SCREAMING_SNAKE_CASE = True    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> None:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    size if size is not None else {'height': 346, 'width': 346}
									_UpperCAmelCase					=    get_size_dict(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    crop_size if crop_size is not None else {'height': 289, 'width': 289}
									_UpperCAmelCase					=    get_size_dict(_SCREAMING_SNAKE_CASE    ,					param_name='crop_size'		)

									_UpperCAmelCase					=    do_resize
									_UpperCAmelCase					=    size
									_UpperCAmelCase					=    resample
									_UpperCAmelCase					=    do_center_crop
									_UpperCAmelCase					=    crop_size
									_UpperCAmelCase					=    do_rescale
									_UpperCAmelCase					=    rescale_factor
									_UpperCAmelCase					=    rescale_offset
									_UpperCAmelCase					=    do_normalize
									_UpperCAmelCase					=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									_UpperCAmelCase					=    image_std if image_std is not None else IMAGENET_STANDARD_STD
									_UpperCAmelCase					=    include_top
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE = PIL.Image.NEAREST    ,					_SCREAMING_SNAKE_CASE = None    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> np.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=    get_size_dict(_SCREAMING_SNAKE_CASE		)
									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(
									    _SCREAMING_SNAKE_CASE    ,					size=(size['height'], size['width'])    ,					resample=_SCREAMING_SNAKE_CASE    ,					data_format=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE = None    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> np.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=    get_size_dict(_SCREAMING_SNAKE_CASE		)
									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(_SCREAMING_SNAKE_CASE    ,					size=(size['height'], size['width'])    ,					data_format=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE = True    ,					_SCREAMING_SNAKE_CASE = None    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> List[str]:




									"""simple docstring"""





									if offset:
																_UpperCAmelCase					=    (image - 127.5) * scale
																if data_format is not None:
																							_UpperCAmelCase					=    to_channel_dimension_format(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase					=    rescaled_image.astype(np.floataa		)
									else:
																_UpperCAmelCase					=    rescale(_SCREAMING_SNAKE_CASE    ,					scale=_SCREAMING_SNAKE_CASE    ,					data_format=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)
									return rescaled_image
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE = None    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> np.ndarray:




									"""simple docstring"""





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

		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE = None    ,					_SCREAMING_SNAKE_CASE = None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE = None    ,					_SCREAMING_SNAKE_CASE = None    ,					_SCREAMING_SNAKE_CASE = None    ,					_SCREAMING_SNAKE_CASE = None    ,					_SCREAMING_SNAKE_CASE = None    ,					_SCREAMING_SNAKE_CASE = None    ,					_SCREAMING_SNAKE_CASE = None    ,					_SCREAMING_SNAKE_CASE = None    ,					_SCREAMING_SNAKE_CASE = None    ,					_SCREAMING_SNAKE_CASE = None    ,					_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> PIL.Image.Image:




									"""simple docstring"""





									_UpperCAmelCase					=    do_resize if do_resize is not None else self.do_resize
									_UpperCAmelCase					=    resample if resample is not None else self.resample
									_UpperCAmelCase					=    do_center_crop if do_center_crop is not None else self.do_center_crop
									_UpperCAmelCase					=    do_rescale if do_rescale is not None else self.do_rescale
									_UpperCAmelCase					=    rescale_factor if rescale_factor is not None else self.rescale_factor
									_UpperCAmelCase					=    rescale_offset if rescale_offset is not None else self.rescale_offset
									_UpperCAmelCase					=    do_normalize if do_normalize is not None else self.do_normalize
									_UpperCAmelCase					=    image_mean if image_mean is not None else self.image_mean
									_UpperCAmelCase					=    image_std if image_std is not None else self.image_std
									_UpperCAmelCase					=    include_top if include_top is not None else self.include_top

									_UpperCAmelCase					=    size if size is not None else self.size
									_UpperCAmelCase					=    get_size_dict(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    crop_size if crop_size is not None else self.crop_size
									_UpperCAmelCase					=    get_size_dict(_SCREAMING_SNAKE_CASE    ,					param_name='crop_size'		)

									_UpperCAmelCase					=    make_list_of_images(_SCREAMING_SNAKE_CASE		)

									if not valid_images(_SCREAMING_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 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.
									_UpperCAmelCase					=    [to_numpy_array(_SCREAMING_SNAKE_CASE		) for image in images]

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

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

									if do_rescale:
																_UpperCAmelCase					=    [self.rescale(image=_SCREAMING_SNAKE_CASE    ,					scale=_SCREAMING_SNAKE_CASE    ,					offset=_SCREAMING_SNAKE_CASE		) for image in images]

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

									if include_top:
																_UpperCAmelCase					=    [self.normalize(image=_SCREAMING_SNAKE_CASE    ,					mean=[0, 0, 0]    ,					std=_SCREAMING_SNAKE_CASE		) for image in images]

									_UpperCAmelCase					=    [to_channel_dimension_format(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		) for image in images]

									_UpperCAmelCase					=    {'pixel_values': images}
									return BatchFeature(data=_SCREAMING_SNAKE_CASE    ,					tensor_type=_SCREAMING_SNAKE_CASE		)




from __future__ import annotations

def  lowerCAmelCase__				(    a__:	list[int]    ,						a__:	int )						->	list[int]:


							'''simple docstring'''





							_UpperCAmelCase					=    {}
							for index, val in enumerate(a__ ):
														_UpperCAmelCase					=    target - val
														if compl in chk_map:
																					return [chk_map[compl], index]
														_UpperCAmelCase					=    index
							return []


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(f'''{two_sum([2, 7, 1_1, 1_5], 9) = }''')
