





"""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 (
    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, logging


lowercase__					:Optional[int]					 =							logging.get_logger(__name__)




class 				snake_case				(      __a			):

      '''simple docstring'''



      _A  :							Dict								=							['pixel_values']

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


        '''simple docstring'''




        super().__init__(**lowercase_			)
        __UpperCAmelCase     :    Dict							=    size if size is not None else {'''shortest_edge''': 256}
        __UpperCAmelCase     :    Tuple							=    get_size_dict(lowercase_    ,					default_to_square=lowercase_			)
        __UpperCAmelCase     :    str							=    crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
        __UpperCAmelCase     :    Optional[int]							=    get_size_dict(lowercase_			)
        __UpperCAmelCase     :    Optional[Any]							=    do_resize
        __UpperCAmelCase     :    int							=    size
        __UpperCAmelCase     :    Union[str, Any]							=    resample
        __UpperCAmelCase     :    Optional[int]							=    do_center_crop
        __UpperCAmelCase     :    Tuple							=    crop_size
        __UpperCAmelCase     :    List[Any]							=    do_rescale
        __UpperCAmelCase     :    Tuple							=    rescale_factor
        __UpperCAmelCase     :    Optional[int]							=    do_normalize
        __UpperCAmelCase     :    Tuple							=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        __UpperCAmelCase     :    List[Any]							=    image_std if image_std is not None else IMAGENET_STANDARD_STD

      def    A_       (				self							:   List[str]    ,					__lowercase							:   np.ndarray    ,					__lowercase							:   Dict[str, int]    ,					__lowercase							:   PILImageResampling = PILImageResampling.BICUBIC    ,					__lowercase							:   Optional[Union[str, ChannelDimension]] = None    ,					**__lowercase							:   Any    ,					):


        '''simple docstring'''




        __UpperCAmelCase     :    Optional[Any]							=    get_size_dict(lowercase_    ,					default_to_square=lowercase_			)
        if "shortest_edge" not in size:
          raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''			)
        __UpperCAmelCase     :    int							=    get_resize_output_image_size(lowercase_    ,					size=size['''shortest_edge''']    ,					default_to_square=lowercase_			)
        return resize(lowercase_    ,					size=lowercase_    ,					resample=lowercase_    ,					data_format=lowercase_    ,					**lowercase_			)

      def    A_       (				self							:   Union[str, Any]    ,					__lowercase							:   np.ndarray    ,					__lowercase							:   Dict[str, int]    ,					__lowercase							:   Optional[Union[str, ChannelDimension]] = None    ,					**__lowercase							:   Any    ,					):


        '''simple docstring'''




        __UpperCAmelCase     :    Union[str, Any]							=    get_size_dict(lowercase_			)
        return center_crop(lowercase_    ,					size=(size['''height'''], size['''width'''])    ,					data_format=lowercase_    ,					**lowercase_			)

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


        '''simple docstring'''




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

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


        '''simple docstring'''




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







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


        '''simple docstring'''




        __UpperCAmelCase     :    str							=    do_resize if do_resize is not None else self.do_resize
        __UpperCAmelCase     :    Union[str, Any]							=    size if size is not None else self.size
        __UpperCAmelCase     :    Dict							=    get_size_dict(lowercase_    ,					default_to_square=lowercase_			)
        __UpperCAmelCase     :    List[Any]							=    resample if resample is not None else self.resample
        __UpperCAmelCase     :    Tuple							=    do_center_crop if do_center_crop is not None else self.do_center_crop
        __UpperCAmelCase     :    Dict							=    crop_size if crop_size is not None else self.crop_size
        __UpperCAmelCase     :    Any							=    get_size_dict(lowercase_			)
        __UpperCAmelCase     :    str							=    do_rescale if do_rescale is not None else self.do_rescale
        __UpperCAmelCase     :    Tuple							=    rescale_factor if rescale_factor is not None else self.rescale_factor
        __UpperCAmelCase     :    Union[str, Any]							=    do_normalize if do_normalize is not None else self.do_normalize
        __UpperCAmelCase     :    Any							=    image_mean if image_mean is not None else self.image_mean
        __UpperCAmelCase     :    int							=    image_std if image_std is not None else self.image_std

        __UpperCAmelCase     :    str							=    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:
          raise ValueError('''Size must be specified if do_resize is True.'''			)

        if do_center_crop and crop_size is None:
          raise ValueError('''Crop size must be specified if do_center_crop 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.'''			)

        # All transformations expect numpy arrays.
        __UpperCAmelCase     :    str							=    [to_numpy_array(lowercase_			) for image in images]

        if do_resize:
          __UpperCAmelCase     :    Optional[Any]							=    [self.resize(image=lowercase_    ,					size=lowercase_    ,					resample=lowercase_			) for image in images]

        if do_center_crop:
          __UpperCAmelCase     :    Optional[Any]							=    [self.center_crop(image=lowercase_    ,					size=lowercase_			) for image in images]

        if do_rescale:
          __UpperCAmelCase     :    List[Any]							=    [self.rescale(image=lowercase_    ,					scale=lowercase_			) for image in images]

        if do_normalize:
          __UpperCAmelCase     :    str							=    [self.normalize(image=lowercase_    ,					mean=lowercase_    ,					std=lowercase_			) for image in images]

        __UpperCAmelCase     :    str							=    [to_channel_dimension_format(lowercase_    ,					lowercase_			) for image in images]

        __UpperCAmelCase     :    List[str]							=    {'''pixel_values''': images}
        return BatchFeature(data=lowercase_    ,					tensor_type=lowercase_			)
'''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.
__snake_case						  =		10

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

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->int:
  lowercase_     =       0
  lowercase_     =       len(SCREAMING_SNAKE_CASE_						)
  while left <= right:
    if right - left < precision:
      return lin_search(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

    lowercase_     =       (left + right) // 3 + 1
    lowercase_     =       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]:
      lowercase_     =       one_third - 1
    elif array[two_third] < target:
      lowercase_     =       two_third + 1

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

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->int:
  if left < right:
    if right - left < precision:
      return lin_search(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    lowercase_     =       (left + right) // 3 + 1
    lowercase_     =       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(SCREAMING_SNAKE_CASE_ ,	one_third - 1 ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    elif array[two_third] < target:
      return rec_ternary_search(two_third + 1 ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    else:
      return rec_ternary_search(one_third + 1 ,	two_third - 1 ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
  else:
    return -1


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       __snake_case						  =		input("""Enter numbers separated by comma:\n""").strip()
       __snake_case						  =		[int(item.strip()) for item in user_input.split(""",""")]
       assert collection == sorted(collection), f"List must be ordered.\n{collection}."
       __snake_case						  =		int(input("""Enter the number to be found in the list:\n""").strip())
       __snake_case						  =		ite_ternary_search(collection, target)
       __snake_case						  =		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""")

