





"""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 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      (       _lowercase ):
						UpperCAmelCase_ =			["pixel_values"]


						def __init__(self   ,							__a = True   ,							__a = None   ,							__a = PILImageResampling.BICUBIC   ,							__a = True   ,							__a = 1 / 2_55   ,							__a = True   ,							__a = None   ,							__a = None   ,							__a = True   ,							**__a   ,							) ->   None:
								super().__init__(**__a		)
								UpperCamelCase   =	size if size is not None else {"height": 3_84, "width": 3_84}
								UpperCamelCase   =	get_size_dict(__a   ,							default_to_square=__a		)

								UpperCamelCase   =	do_resize
								UpperCamelCase   =	size
								UpperCamelCase   =	resample
								UpperCamelCase   =	do_rescale
								UpperCamelCase   =	rescale_factor
								UpperCamelCase   =	do_normalize
								UpperCamelCase   =	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								UpperCamelCase   =	image_std if image_std is not None else OPENAI_CLIP_STD
								UpperCamelCase   =	do_convert_rgb


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a = PILImageResampling.BICUBIC   ,							__a = None   ,							**__a   ,							) ->   np.ndarray:
								UpperCamelCase   =	get_size_dict(__a   ,							default_to_square=__a		)
								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()}"		)
								UpperCamelCase   =	(size["height"], size["width"])
								return resize(__a   ,							size=__a   ,							resample=__a   ,							data_format=__a   ,							**__a		)


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a = None   ,							**__a   ,							) ->   List[Any]:
								return rescale(__a   ,							scale=__a   ,							data_format=__a   ,							**__a		)


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a   ,							__a = None   ,							**__a   ,							) ->   np.ndarray:
								return normalize(__a   ,							mean=__a   ,							std=__a   ,							data_format=__a   ,							**__a		)




						def     snake_case_   (self   ,							__a   ,							__a = None   ,							__a = None   ,							__a = None   ,							__a = None   ,							__a = None   ,							__a = None   ,							__a = None   ,							__a = None   ,							__a = None   ,							__a = None   ,							__a = ChannelDimension.FIRST   ,							**__a   ,							) ->   PIL.Image.Image:
								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_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   =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								UpperCamelCase   =	size if size is not None else self.size
								UpperCamelCase   =	get_size_dict(__a   ,							default_to_square=__a		)

								UpperCamelCase   =	make_list_of_images(__a		)

								if not valid_images(__a		):
										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:
										UpperCamelCase   =	[convert_to_rgb(__a		) for image in images]

								# All transformations expect numpy arrays.
								UpperCamelCase   =	[to_numpy_array(__a		) for image in images]

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

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

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

								UpperCamelCase   =	[to_channel_dimension_format(__a   ,							__a		) for image in images]

								UpperCamelCase   =	BatchFeature(data={"pixel_values": images}   ,							tensor_type=__a		)

								return encoded_outputs













"""simple docstring"""





def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	0
		for ch in input_str:
				UpperCamelCase   =	ord(_SCREAMING_SNAKE_CASE    )
				UpperCamelCase   =	pow(2  ,	_SCREAMING_SNAKE_CASE    )

				# If we already turned on bit for current character's unicode
				if bitmap >> ch_unicode & 1 == 1:
						return False
				bitmap |= ch_bit_index_on
		return True


if __name__ == "__main__":
						import doctest

						doctest.testmod()






