





'''simple docstring'''







from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
lowerCAmelCase_					:    List[Any]      	=			10






def  _lowerCamelCase				( lowercase						:      int    ,							lowercase						:      int    ,							lowercase						:      list[int]    ,							lowercase						:      int						)	->	int:
				for i in range(lowercase    ,							lowercase						):
								if array[i] == target:
												return i
				return -1






def  _lowerCamelCase				( lowercase						:      list[int]    ,							lowercase						:      int						)	->	int:
				_a								= 0
				_a								= len(lowercase						)
				while left <= right:
								if right - left < precision:
												return lin_search(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)

								_a								= (left + right) // 3 + 1
								_a								= 2 * (left + right) // 3 + 1

								if array[one_third] == target:
												return one_third
								elif array[two_third] == target:
												return two_third

								elif target < array[one_third]:
												_a								= one_third - 1
								elif array[two_third] < target:
												_a								= two_third + 1

								else:
												_a								= one_third + 1
												_a								= two_third - 1
				else:
								return -1






def  _lowerCamelCase				( lowercase						:      int    ,							lowercase						:      int    ,							lowercase						:      list[int]    ,							lowercase						:      int						)	->	int:
				if left < right:
								if right - left < precision:
												return lin_search(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
								_a								= (left + right) // 3 + 1
								_a								= 2 * (left + right) // 3 + 1

								if array[one_third] == target:
												return one_third
								elif array[two_third] == target:
												return two_third

								elif target < array[one_third]:
												return rec_ternary_search(lowercase    ,							one_third - 1    ,							lowercase    ,							lowercase						)
								elif array[two_third] < target:
												return rec_ternary_search(two_third + 1    ,							lowercase    ,							lowercase    ,							lowercase						)
								else:
												return rec_ternary_search(one_third + 1    ,							two_third - 1    ,							lowercase    ,							lowercase						)
				else:
								return -1


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					lowerCAmelCase_					:    int      	=			input('Enter numbers separated by comma:\n').strip()
					lowerCAmelCase_					:    Tuple      	=			[int(item.strip()) for item in user_input.split(',')]
					assert collection == sorted(collection), f"List must be ordered.\n{collection}."
					lowerCAmelCase_					:    str      	=			int(input('Enter the number to be found in the list:\n').strip())
					lowerCAmelCase_					:    Optional[int]      	=			ite_ternary_search(collection, target)
					lowerCAmelCase_					:    Optional[int]      	=			rec_ternary_search(0, len(collection) - 1, collection, target)
					if resulta != -1:
										print(f"""Iterative search: {target} found at positions: {resulta}""")
										print(f"""Recursive search: {target} found at positions: {resulta}""")
					else:
										print('Not found')






from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


UpperCAmelCase_									=	logging.get_logger(__name__)





class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	Tuple    		=   ['pixel_values']
      def __init__(				self: Any    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: Union[int, float] = 1 / 2_55    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: int = 8    ,       **UpperCamelCase_: Tuple    ,       ):
             super().__init__(**UpperCamelCase_   )

             __lowerCamelCase   =						do_rescale
             __lowerCamelCase   =						rescale_factor
             __lowerCamelCase   =						do_pad
             __lowerCamelCase   =						pad_size
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: np.ndarray    ,       UpperCamelCase_: float    ,       UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None    ,       **UpperCamelCase_: Tuple   ):
             return rescale(UpperCamelCase_    ,       scale=UpperCamelCase_    ,       data_format=UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]    ,       UpperCamelCase_: np.ndarray    ,       UpperCamelCase_: int    ,       UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None   ):
             __lowerCamelCase,   __lowerCamelCase   =						get_image_size(UpperCamelCase_   )
             __lowerCamelCase   =						(old_height // size + 1) * size - old_height
             __lowerCamelCase   =						(old_width // size + 1) * size - old_width

             return pad(UpperCamelCase_    ,       ((0, pad_height), (0, pad_width))    ,       mode="""symmetric"""    ,       data_format=UpperCamelCase_   )




      def 			lowerCAmelCase__						(				self: str    ,       UpperCamelCase_: ImageInput    ,       UpperCamelCase_: Optional[bool] = None    ,       UpperCamelCase_: Optional[float] = None    ,       UpperCamelCase_: Optional[bool] = None    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: Optional[Union[str, TensorType]] = None    ,       UpperCamelCase_: Union[str, ChannelDimension] = ChannelDimension.FIRST    ,       **UpperCamelCase_: Any    ,       ):
             __lowerCamelCase   =						do_rescale if do_rescale is not None else self.do_rescale
             __lowerCamelCase   =						rescale_factor if rescale_factor is not None else self.rescale_factor
             __lowerCamelCase   =						do_pad if do_pad is not None else self.do_pad
             __lowerCamelCase   =						pad_size if pad_size is not None else self.pad_size

             __lowerCamelCase   =						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_rescale and rescale_factor is None:
                    raise ValueError("""Rescale factor must be specified if do_rescale is True."""   )

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

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

             if do_pad:
                    __lowerCamelCase   =						[self.pad(UpperCamelCase_    ,       size=UpperCamelCase_   ) for image in images]

             __lowerCamelCase   =						[to_channel_dimension_format(UpperCamelCase_    ,       UpperCamelCase_   ) for image in images]

             __lowerCamelCase   =						{"""pixel_values""": images}
             return BatchFeature(data=UpperCamelCase_    ,       tensor_type=UpperCamelCase_   )

