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


__lowercase											=      logging.get_logger(__name__)


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    a__			:		Optional[Any]      			=   ["""pixel_values"""]







    def __init__(			self				,				__lowercase = True				,				__lowercase = None				,				__lowercase = PILImageResampling.BICUBIC				,				__lowercase = True				,				__lowercase = 1 / 255				,				__lowercase = True				,				__lowercase = None				,				__lowercase = None				,				__lowercase = True				,				**__lowercase				,				) ->			None:
        super().__init__(**__lowercase)
        __UpperCamelCase  :Optional[int]    						=	size if size is not None else {'''height''': 384, '''width''': 384}
        __UpperCamelCase  :List[Any]    						=	get_size_dict(__lowercase				,				default_to_square=__lowercase)

        __UpperCamelCase  :Optional[Any]    						=	do_resize
        __UpperCamelCase  :int    						=	size
        __UpperCamelCase  :Dict    						=	resample
        __UpperCamelCase  :List[str]    						=	do_rescale
        __UpperCamelCase  :Optional[Any]    						=	rescale_factor
        __UpperCamelCase  :str    						=	do_normalize
        __UpperCamelCase  :List[str]    						=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        __UpperCamelCase  :List[Any]    						=	image_std if image_std is not None else OPENAI_CLIP_STD
        __UpperCamelCase  :Optional[int]    						=	do_convert_rgb







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase = PILImageResampling.BICUBIC				,				__lowercase = None				,				**__lowercase				,				) ->			np.ndarray:
        __UpperCamelCase  :Any    						=	get_size_dict(__lowercase				,				default_to_square=__lowercase)
        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  :Union[str, Any]    						=	(size['''height'''], size['''width'''])
        return resize(__lowercase				,				size=__lowercase				,				resample=__lowercase				,				data_format=__lowercase				,				**__lowercase)







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase = None				,				**__lowercase				,				) ->			Optional[int]:
        return rescale(__lowercase				,				scale=__lowercase				,				data_format=__lowercase				,				**__lowercase)







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







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase = None				,				__lowercase = None				,				__lowercase = None				,				__lowercase = None				,				__lowercase = None				,				__lowercase = None				,				__lowercase = None				,				__lowercase = None				,				__lowercase = None				,				__lowercase = None				,				__lowercase = ChannelDimension.FIRST				,				**__lowercase				,				) ->			PIL.Image.Image:
        __UpperCamelCase  :int    						=	do_resize if do_resize is not None else self.do_resize
        __UpperCamelCase  :Tuple    						=	resample if resample is not None else self.resample
        __UpperCamelCase  :Tuple    						=	do_rescale if do_rescale is not None else self.do_rescale
        __UpperCamelCase  :List[Any]    						=	rescale_factor if rescale_factor is not None else self.rescale_factor
        __UpperCamelCase  :List[str]    						=	do_normalize if do_normalize is not None else self.do_normalize
        __UpperCamelCase  :Optional[Any]    						=	image_mean if image_mean is not None else self.image_mean
        __UpperCamelCase  :Optional[Any]    						=	image_std if image_std is not None else self.image_std
        __UpperCamelCase  :int    						=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        __UpperCamelCase  :Dict    						=	size if size is not None else self.size
        __UpperCamelCase  :Optional[int]    						=	get_size_dict(__lowercase				,				default_to_square=__lowercase)

        __UpperCamelCase  :Dict    						=	make_list_of_images(__lowercase)

        if not valid_images(__lowercase):
            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  :Dict    						=	[convert_to_rgb(__lowercase) for image in images]

        # All transformations expect numpy arrays.
        __UpperCamelCase  :int    						=	[to_numpy_array(__lowercase) for image in images]

        if do_resize:
            __UpperCamelCase  :Tuple    						=	[self.resize(image=__lowercase				,				size=__lowercase				,				resample=__lowercase) for image in images]

        if do_rescale:
            __UpperCamelCase  :Tuple    						=	[self.rescale(image=__lowercase				,				scale=__lowercase) for image in images]

        if do_normalize:
            __UpperCamelCase  :int    						=	[self.normalize(image=__lowercase				,				mean=__lowercase				,				std=__lowercase) for image in images]

        __UpperCamelCase  :List[str]    						=	[to_channel_dimension_format(__lowercase				,				__lowercase) for image in images]

        __UpperCamelCase  :Optional[Any]    						=	BatchFeature(data={'''pixel_values''': images}				,				tensor_type=__lowercase)

        return encoded_outputs






from __future__ import annotations

import bisect



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = 0						,    SCREAMING_SNAKE_CASE = -1			):
    '''simple docstring'''







    if hi < 0:
        __UpperCamelCase  :str    						=	len(SCREAMING_SNAKE_CASE			)

    while lo < hi:
        __UpperCamelCase  :Optional[int]    						=	lo + (hi - lo) // 2
        if sorted_collection[mid] < item:
            __UpperCamelCase  :List[Any]    						=	mid + 1
        else:
            __UpperCamelCase  :Any    						=	mid

    return lo



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = 0						,    SCREAMING_SNAKE_CASE = -1			):
    '''simple docstring'''







    if hi < 0:
        __UpperCamelCase  :List[str]    						=	len(SCREAMING_SNAKE_CASE			)

    while lo < hi:
        __UpperCamelCase  :Optional[Any]    						=	lo + (hi - lo) // 2
        if sorted_collection[mid] <= item:
            __UpperCamelCase  :Dict    						=	mid + 1
        else:
            __UpperCamelCase  :Optional[Any]    						=	mid

    return lo



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = 0						,    SCREAMING_SNAKE_CASE = -1			):
    '''simple docstring'''







    sorted_collection.insert(bisect_left(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)						,    SCREAMING_SNAKE_CASE			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = 0						,    SCREAMING_SNAKE_CASE = -1			):
    '''simple docstring'''







    sorted_collection.insert(bisect_right(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)						,    SCREAMING_SNAKE_CASE			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Tuple    						=	0
    __UpperCamelCase  :List[str]    						=	len(SCREAMING_SNAKE_CASE			) - 1

    while left <= right:
        __UpperCamelCase  :Optional[int]    						=	left + (right - left) // 2
        __UpperCamelCase  :List[str]    						=	sorted_collection[midpoint]
        if current_item == item:
            return midpoint
        elif item < current_item:
            __UpperCamelCase  :Union[str, Any]    						=	midpoint - 1
        else:
            __UpperCamelCase  :Dict    						=	midpoint + 1
    return None



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	bisect.bisect_left(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    if index != len(SCREAMING_SNAKE_CASE			) and sorted_collection[index] == item:
        return index
    return None



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    if right < left:
        return None

    __UpperCamelCase  :str    						=	left + (right - left) // 2

    if sorted_collection[midpoint] == item:
        return midpoint
    elif sorted_collection[midpoint] > item:
        return binary_search_by_recursion(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    midpoint - 1			)
    else:
        return binary_search_by_recursion(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    midpoint + 1						,    SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
    __lowercase											=      input('''Enter numbers separated by comma:\n''').strip()
    __lowercase											=      sorted(int(item) for item in user_input.split(''','''))
    __lowercase											=      int(input('''Enter a single number to be found in the list:\n'''))
    __lowercase											=      binary_search(collection, target)
    if result is None:
        print(F'{target} was not found in {collection}.')
    else:
        print(F'{target} was found at position {result} in {collection}.')





