





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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_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_    =    logging.get_logger(__name__)



class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					str	      =				['''pixel_values''']






			def __init__(self ,							__magic_name__ = True ,							__magic_name__ = None ,							__magic_name__ = PILImageResampling.BICUBIC ,							__magic_name__ = True ,							__magic_name__ = 1 / 255 ,							__magic_name__ = True ,							__magic_name__ = None ,							__magic_name__ = None ,							__magic_name__ = True ,							**__magic_name__ ,							)					->       None:


							'''simple docstring'''



							super().__init__(**__magic_name__   )
							snake_case_						:   List[str]           =  size if size is not None else {'''height''': 384, '''width''': 384}
							snake_case_						:   List[str]           =  get_size_dict(__magic_name__ ,							default_to_square=__magic_name__   )

							snake_case_						:   Optional[Any]           =  do_resize
							snake_case_						:   str           =  size
							snake_case_						:   str           =  resample
							snake_case_						:   Any           =  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 OPENAI_CLIP_MEAN
							snake_case_						:   Optional[int]           =  image_std if image_std is not None else OPENAI_CLIP_STD
							snake_case_						:   Optional[int]           =  do_convert_rgb






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = PILImageResampling.BICUBIC ,							__magic_name__ = None ,							**__magic_name__ ,							)					->       np.ndarray:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  get_size_dict(__magic_name__ ,							default_to_square=__magic_name__   )
							if "height" not in size or "width" not in size:
											raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''   )
							snake_case_						:   Any           =  (size['''height'''], size['''width'''])
							return resize(__magic_name__ ,							size=__magic_name__ ,							resample=__magic_name__ ,							data_format=__magic_name__ ,							**__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = None ,							**__magic_name__ ,							)					->       int:


							'''simple docstring'''



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






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = None ,							**__magic_name__ ,							)					->       np.ndarray:


							'''simple docstring'''



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






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


							'''simple docstring'''



							snake_case_						:   str           =  do_resize if do_resize is not None else self.do_resize
							snake_case_						:   Dict           =  resample if resample is not None else self.resample
							snake_case_						:   Optional[int]           =  do_rescale if do_rescale is not None else self.do_rescale
							snake_case_						:   Tuple           =  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_						:   List[str]           =  image_mean if image_mean is not None else self.image_mean
							snake_case_						:   Optional[Any]           =  image_std if image_std is not None else self.image_std
							snake_case_						:   int           =  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							snake_case_						:   Optional[int]           =  size if size is not None else self.size
							snake_case_						:   List[str]           =  get_size_dict(__magic_name__ ,							default_to_square=__magic_name__   )

							snake_case_						:   Tuple           =  make_list_of_images(__magic_name__   )

							if not valid_images(__magic_name__   ):
											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_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.'''   )

							# PIL RGBA images are converted to RGB
							if do_convert_rgb:
											snake_case_						:   Optional[int]           =  [convert_to_rgb(__magic_name__   ) for image in images]

							# All transformations expect numpy arrays.
							snake_case_						:   List[str]           =  [to_numpy_array(__magic_name__   ) for image in images]

							if do_resize:
											snake_case_						:   Optional[int]           =  [self.resize(image=__magic_name__ ,							size=__magic_name__ ,							resample=__magic_name__   ) for image in images]

							if do_rescale:
											snake_case_						:   Optional[int]           =  [self.rescale(image=__magic_name__ ,							scale=__magic_name__   ) for image in images]

							if do_normalize:
											snake_case_						:   Optional[Any]           =  [self.normalize(image=__magic_name__ ,							mean=__magic_name__ ,							std=__magic_name__   ) for image in images]

							snake_case_						:   List[Any]           =  [to_channel_dimension_format(__magic_name__ ,							__magic_name__   ) for image in images]

							snake_case_						:   List[str]           =  BatchFeature(data={'''pixel_values''': images} ,							tensor_type=__magic_name__   )

							return encoded_outputs







def 					lowerCamelCase_						(    _UpperCamelCase			)		->						str:






				"""simple docstring"""


				if number > 0:
								raise ValueError('''input must be a negative integer'''			)
				snake_case_						:   List[str]           =  len(bin(_UpperCamelCase			)[3:]			)
				snake_case_						:   str           =  bin(abs(_UpperCamelCase			) - (1 << binary_number_length)			)[3:]
				snake_case_						:   Dict           =  (
				    (
				        '''1'''
				        + '''0''' * (binary_number_length - len(_UpperCamelCase			))
				        + twos_complement_number
				    )
				    if number < 0
				    else '''0'''
				)
				return "0b" + twos_complement_number


if __name__ == "__main__":
	import doctest

	doctest.testmod()
