






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


__a   :							Union[str, Any]						= logging.get_logger(__name__)


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					["pixel_values"]




     def __init__(					self							:      Any			,						UpperCamelCase_							:      bool = True			,						UpperCamelCase_							:      Dict[str, int] = None			,						UpperCamelCase_							:      PILImageResampling = PILImageResampling.BICUBIC			,						UpperCamelCase_							:      bool = True			,						UpperCamelCase_							:      Union[int, float] = 1 / 255			,						UpperCamelCase_							:      bool = True			,						UpperCamelCase_							:      Optional[Union[float, List[float]]] = None			,						UpperCamelCase_							:      Optional[Union[float, List[float]]] = None			,						UpperCamelCase_							:      bool = True			,						**UpperCamelCase_							:      str			,						):


         """simple docstring"""



         super().__init__(**UpperCamelCase_					)
         __A       =						size if size is not None else {"""height""": 384, """width""": 384}
         __A       =						get_size_dict(UpperCamelCase_			,						default_to_square=UpperCamelCase_					)

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




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      np.ndarray			,						UpperCamelCase_							:      Dict[str, int]			,						UpperCamelCase_							:      PILImageResampling = PILImageResampling.BICUBIC			,						UpperCamelCase_							:      Optional[Union[str, ChannelDimension]] = None			,						**UpperCamelCase_							:      Optional[int]			,						):


         """simple docstring"""



         __A       =						get_size_dict(UpperCamelCase_			,						default_to_square=UpperCamelCase_					)
         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()}"					)
         __A       =						(size["""height"""], size["""width"""])
         return resize(UpperCamelCase_			,						size=UpperCamelCase_			,						resample=UpperCamelCase_			,						data_format=UpperCamelCase_			,						**UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      np.ndarray			,						UpperCamelCase_							:      Union[int, float]			,						UpperCamelCase_							:      Optional[Union[str, ChannelDimension]] = None			,						**UpperCamelCase_							:      Any			,						):


         """simple docstring"""



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




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]			,						UpperCamelCase_							:      np.ndarray			,						UpperCamelCase_							:      Union[float, List[float]]			,						UpperCamelCase_							:      Union[float, List[float]]			,						UpperCamelCase_							:      Optional[Union[str, ChannelDimension]] = None			,						**UpperCamelCase_							:      Optional[Any]			,						):


         """simple docstring"""



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





     def 		lowerCAmelCase_  (					self							:      Union[str, Any]			,						UpperCamelCase_							:      ImageInput			,						UpperCamelCase_							:      Optional[bool] = None			,						UpperCamelCase_							:      Optional[Dict[str, int]] = None			,						UpperCamelCase_							:      PILImageResampling = None			,						UpperCamelCase_							:      Optional[bool] = None			,						UpperCamelCase_							:      Optional[float] = None			,						UpperCamelCase_							:      Optional[bool] = None			,						UpperCamelCase_							:      Optional[Union[float, List[float]]] = None			,						UpperCamelCase_							:      Optional[Union[float, List[float]]] = None			,						UpperCamelCase_							:      Optional[Union[str, TensorType]] = None			,						UpperCamelCase_							:      bool = None			,						UpperCamelCase_							:      ChannelDimension = ChannelDimension.FIRST			,						**UpperCamelCase_							:      Dict			,						):


         """simple docstring"""



         __A       =						do_resize if do_resize is not None else self.do_resize
         __A       =						resample if resample is not None else self.resample
         __A       =						do_rescale if do_rescale is not None else self.do_rescale
         __A       =						rescale_factor if rescale_factor is not None else self.rescale_factor
         __A       =						do_normalize if do_normalize is not None else self.do_normalize
         __A       =						image_mean if image_mean is not None else self.image_mean
         __A       =						image_std if image_std is not None else self.image_std
         __A       =						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         __A       =						size if size is not None else self.size
         __A       =						get_size_dict(UpperCamelCase_			,						default_to_square=UpperCamelCase_					)

         __A       =						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_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:
             __A       =						[convert_to_rgb(UpperCamelCase_					) for image in images]

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

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

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

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

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

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

         return encoded_outputs












from __future__ import annotations



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      list[int]      ,					__lowercase :      int     )    ->   bool:





    """simple docstring"""

    if len(__lowercase     ) == 0:
        return False
    __A       =						len(__lowercase     ) // 2
    if a_list[midpoint] == item:
        return True
    if item < a_list[midpoint]:
        return binary_search(a_list[:midpoint]      ,					__lowercase     )
    else:
        return binary_search(a_list[midpoint + 1 :]      ,					__lowercase     )


if __name__ == "__main__":
  __a   :							Tuple						= input("Enter numbers separated by comma:\n").strip()
  __a   :							Any						= [int(item.strip()) for item in user_input.split(",")]
  __a   :							List[Any]						= int(input("Enter the number to be found in the list:\n").strip())
  __a   :							Optional[int]						= "" if binary_search(sequence, target) else "not "
  print(f"""{target} was {not_str}found in {sequence}""")




