'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase_     		= logging.get_logger(__name__)
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      Tuple       =							['pixel_values']






		def __init__(   self		,						__A = True		,						__A = 32		,						__A=PILImageResampling.BILINEAR		,						__A = True		,						**__A		,						) ->     None:
								_lowerCAmelCase										=do_resize
								_lowerCAmelCase										=do_rescale
								_lowerCAmelCase										=size_divisor
								_lowerCAmelCase										=resample
								super().__init__(**__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A		,						__A = None		,						**__A				) ->     np.ndarray:
								_lowerCAmelCase						,				_lowerCAmelCase										=get_image_size(__A				)
								# Rounds the height and width down to the closest multiple of size_divisor
								_lowerCAmelCase										=height // size_divisor * size_divisor
								_lowerCAmelCase										=width // size_divisor * size_divisor
								_lowerCAmelCase										=resize(__A		,						(new_h, new_w)		,						resample=__A		,						data_format=__A		,						**__A				)
								return image






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A = None		,						**__A				) ->     np.ndarray:
								return rescale(image=__A		,						scale=__A		,						data_format=__A		,						**__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A = None		,						__A = None		,						__A=None		,						__A = None		,						__A = None		,						__A = ChannelDimension.FIRST		,						**__A		,						) ->     BatchFeature:
								_lowerCAmelCase										=do_resize if do_resize is not None else self.do_resize
								_lowerCAmelCase										=do_rescale if do_rescale is not None else self.do_rescale
								_lowerCAmelCase										=size_divisor if size_divisor is not None else self.size_divisor
								_lowerCAmelCase										=resample if resample is not None else self.resample

								if do_resize and size_divisor is None:
														raise ValueError('size_divisor is required for resizing'				)

								_lowerCAmelCase										=make_list_of_images(__A				)

								if not valid_images(__A				):
														raise ValueError('Invalid image(s)'				)

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

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

								if do_rescale:
														_lowerCAmelCase										=[self.rescale(__A		,						scale=1 / 255				) for image in images]

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

								_lowerCAmelCase										={'pixel_values': images}
								return BatchFeature(data=__A		,						tensor_type=__A				)






'''simple docstring'''

from __future__ import annotations



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=len(a__      ) // 2

						# choose the middle 3 elements
						_lowerCAmelCase										=lst[m - 1 : m + 2]

						# if middle element is peak
						if three[1] > three[0] and three[1] > three[2]:
												return three[1]

						# if increasing, recurse on right
						elif three[0] < three[2]:
												if len(lst[:m]      ) == 2:
																		m -= 1
												return peak(lst[m:]      )

						# decreasing
						else:
												if len(lst[:m]      ) == 2:
																		m += 1
												return peak(lst[:m]      )


if __name__ == "__main__":
							import doctest

							doctest.testmod()





