



def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ) ->       str:
						if number < 0 or shift_amount < 0:
												raise ValueError("both inputs must be positive integers"    )

						UpperCAmelCase         =     str(bin(lowerCamelCase_    )    )
						binary_number += "0" * shift_amount
						return binary_number

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ) ->       str:
						if number < 0 or shift_amount < 0:
												raise ValueError("both inputs must be positive integers"    )

						UpperCAmelCase         =     str(bin(lowerCamelCase_    )    )[2:]
						if shift_amount >= len(lowerCamelCase_    ):
												return "0b0"
						UpperCAmelCase         =     binary_number[: len(lowerCamelCase_    ) - shift_amount]
						return "0b" + shifted_binary_number

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ) ->       str:
						if number >= 0:  # Get binary representation of positive number
												UpperCAmelCase         =     "0" + str(bin(lowerCamelCase_    )    ).strip("-"    )[2:]
						else:  # Get binary (2's complement) representation of negative number
												UpperCAmelCase         =     len(bin(lowerCamelCase_    )[3:]    )  # Find 2's complement of number
												UpperCAmelCase         =     bin(abs(lowerCamelCase_    ) - (1 << binary_number_length)    )[3:]
												UpperCAmelCase         =     (
												    "1" + "0" * (binary_number_length - len(lowerCamelCase_    )) + binary_number
												)

						if shift_amount >= len(lowerCamelCase_    ):
												return "0b" + binary_number[0] * len(lowerCamelCase_    )
						return (
						    "0b"
						    + binary_number[0] * shift_amount
						    + binary_number[: len(lowerCamelCase_    ) - shift_amount]
						)


if __name__ == "__main__":
		import doctest

		doctest.testmod()








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, is_vision_available, logging


if is_vision_available():
		import PIL


__lowerCamelCase     :							List[Any]  							=				logging.get_logger(__name__)





class  __magic_name__   (							A__						):
		lowercase      :       Tuple          =['''pixel_values''']






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







								super().__init__(**UpperCamelCase__      )
								UpperCAmelCase         =     size if size is not None else {"shortest_edge": 3_84}
								UpperCAmelCase         =     get_size_dict(UpperCamelCase__				,		default_to_square=UpperCamelCase__      )

								UpperCAmelCase         =     do_resize
								UpperCAmelCase         =     size
								# Default value set here for backwards compatibility where the value in config is None
								UpperCAmelCase         =     crop_pct if crop_pct is not None else 2_24 / 2_56
								UpperCAmelCase         =     resample
								UpperCAmelCase         =     do_rescale
								UpperCAmelCase         =     rescale_factor
								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






		def SCREAMING_SNAKE_CASE_			(						self      :     Any				,		UpperCamelCase__      :     np.ndarray				,		UpperCamelCase__      :     Dict[str, int]				,		UpperCamelCase__      :     float				,		UpperCamelCase__      :     PILImageResampling = PILImageResampling.BICUBIC				,		UpperCamelCase__      :     Optional[Union[str, ChannelDimension]] = None				,		**UpperCamelCase__      :     Union[str, Any]				,		)				->					np.ndarray:
								'''simple docstring'''







								UpperCAmelCase         =     get_size_dict(UpperCamelCase__				,		default_to_square=UpperCamelCase__      )
								if "shortest_edge" not in size:
														raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'      )
								UpperCAmelCase         =     size["shortest_edge"]

								if shortest_edge < 3_84:
														# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
														UpperCAmelCase         =     int(shortest_edge / crop_pct      )
														UpperCAmelCase         =     get_resize_output_image_size(UpperCamelCase__				,		size=UpperCamelCase__				,		default_to_square=UpperCamelCase__      )
														UpperCAmelCase         =     resize(image=UpperCamelCase__				,		size=UpperCamelCase__				,		resample=UpperCamelCase__				,		data_format=UpperCamelCase__				,		**UpperCamelCase__      )
														# then crop to (shortest_edge, shortest_edge)
														return center_crop(image=UpperCamelCase__				,		size=(shortest_edge, shortest_edge)				,		data_format=UpperCamelCase__				,		**UpperCamelCase__      )
								else:
														# warping (no cropping) when evaluated at 384 or larger
														return resize(
														    UpperCamelCase__				,		size=(shortest_edge, shortest_edge)				,		resample=UpperCamelCase__				,		data_format=UpperCamelCase__				,		**UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]				,		UpperCamelCase__      :     np.ndarray				,		UpperCamelCase__      :     Union[int, float]				,		UpperCamelCase__      :     Optional[Union[str, ChannelDimension]] = None				,		**UpperCamelCase__      :     Optional[Any]				,		)				->					List[str]:
								'''simple docstring'''







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






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]				,		UpperCamelCase__      :     np.ndarray				,		UpperCamelCase__      :     Union[float, List[float]]				,		UpperCamelCase__      :     Union[float, List[float]]				,		UpperCamelCase__      :     Optional[Union[str, ChannelDimension]] = None				,		**UpperCamelCase__      :     Optional[Any]				,		)				->					np.ndarray:
								'''simple docstring'''







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







		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple				,		UpperCamelCase__      :     ImageInput				,		UpperCamelCase__      :     bool = None				,		UpperCamelCase__      :     Dict[str, int] = None				,		UpperCamelCase__      :     float = None				,		UpperCamelCase__      :     PILImageResampling = None				,		UpperCamelCase__      :     bool = None				,		UpperCamelCase__      :     float = None				,		UpperCamelCase__      :     bool = None				,		UpperCamelCase__      :     Optional[Union[float, List[float]]] = None				,		UpperCamelCase__      :     Optional[Union[float, List[float]]] = None				,		UpperCamelCase__      :     Optional[Union[str, TensorType]] = None				,		UpperCamelCase__      :     ChannelDimension = ChannelDimension.FIRST				,		**UpperCamelCase__      :     Any				,		)				->					PIL.Image.Image:
								'''simple docstring'''







								UpperCAmelCase         =     do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase         =     crop_pct if crop_pct is not None else self.crop_pct
								UpperCAmelCase         =     resample if resample is not None else self.resample
								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         =     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         =     size if size is not None else self.size
								UpperCAmelCase         =     get_size_dict(UpperCamelCase__				,		default_to_square=UpperCamelCase__      )

								UpperCAmelCase         =     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 or resample is None:
														raise ValueError("Size and resample must be specified if do_resize is True."      )

								if do_resize and size["shortest_edge"] < 3_84 and crop_pct is None:
														raise ValueError("crop_pct must be specified if size < 384."      )

								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(UpperCamelCase__      ) for image in images]

								if do_resize:
														UpperCAmelCase         =     [self.resize(image=UpperCamelCase__				,		size=UpperCamelCase__				,		crop_pct=UpperCamelCase__				,		resample=UpperCamelCase__      ) for image in images]

								if do_rescale:
														UpperCAmelCase         =     [self.rescale(image=UpperCamelCase__				,		scale=UpperCamelCase__      ) for image in images]

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

								UpperCAmelCase         =     [to_channel_dimension_format(UpperCamelCase__				,		UpperCamelCase__      ) for image in images]

								UpperCAmelCase         =     {"pixel_values": images}
								return BatchFeature(data=UpperCamelCase__				,		tensor_type=UpperCamelCase__      )



