






'''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


__lowerCAmelCase       		=      logging.get_logger(__name__)





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				["pixel_values"]






			def __init__( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			bool = True  ,						__SCREAMING_SNAKE_CASE      :			int = 32  ,						__SCREAMING_SNAKE_CASE      :			str=PILImageResampling.BILINEAR  ,						__SCREAMING_SNAKE_CASE      :			bool = True  ,						**__SCREAMING_SNAKE_CASE      :			Optional[int]  ,						)			->			None:
							a_		:  Union[str, Any]											=      do_resize
							a_		:  List[Any]											=      do_rescale
							a_		:  Dict											=      size_divisor
							a_		:  List[str]											=      resample
							super().__init__(**__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple  ,						__SCREAMING_SNAKE_CASE      :			np.ndarray  ,						__SCREAMING_SNAKE_CASE      :			int  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			Optional[ChannelDimension] = None  ,						**__SCREAMING_SNAKE_CASE      :			int					)			->			np.ndarray:
							a_				,			a_		:  List[Any]											=      get_image_size(__SCREAMING_SNAKE_CASE					)
							# Rounds the height and width down to the closest multiple of size_divisor
							a_		:  Any											=      height // size_divisor * size_divisor
							a_		:  List[str]											=      width // size_divisor * size_divisor
							a_		:  Any											=      resize(__SCREAMING_SNAKE_CASE  ,						(new_h, new_w)  ,						resample=__SCREAMING_SNAKE_CASE  ,						data_format=__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)
							return image






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			np.ndarray  ,						__SCREAMING_SNAKE_CASE      :			float  ,						__SCREAMING_SNAKE_CASE      :			Optional[ChannelDimension] = None  ,						**__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			np.ndarray:
							return rescale(image=__SCREAMING_SNAKE_CASE  ,						scale=__SCREAMING_SNAKE_CASE  ,						data_format=__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]  ,						__SCREAMING_SNAKE_CASE      :			Optional[bool] = None  ,						__SCREAMING_SNAKE_CASE      :			Optional[int] = None  ,						__SCREAMING_SNAKE_CASE      :			Dict=None  ,						__SCREAMING_SNAKE_CASE      :			Optional[bool] = None  ,						__SCREAMING_SNAKE_CASE      :			Optional[Union[TensorType, str]] = None  ,						__SCREAMING_SNAKE_CASE      :			ChannelDimension = ChannelDimension.FIRST  ,						**__SCREAMING_SNAKE_CASE      :			Optional[Any]  ,						)			->			BatchFeature:
							a_		:  Union[str, Any]											=      do_resize if do_resize is not None else self.do_resize
							a_		:  Optional[int]											=      do_rescale if do_rescale is not None else self.do_rescale
							a_		:  Dict											=      size_divisor if size_divisor is not None else self.size_divisor
							a_		:  Optional[Any]											=      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'''					)

							a_		:  Dict											=      make_list_of_images(__SCREAMING_SNAKE_CASE					)

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

							# All transformations expect numpy arrays.
							a_		:  Optional[int]											=      [to_numpy_array(__SCREAMING_SNAKE_CASE					) for img in images]

							if do_resize:
											a_		:  List[str]											=      [self.resize(__SCREAMING_SNAKE_CASE  ,						size_divisor=__SCREAMING_SNAKE_CASE  ,						resample=__SCREAMING_SNAKE_CASE					) for image in images]

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

							a_		:  str											=      [to_channel_dimension_format(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					) for image in images]

							a_		:  List[Any]											=      {'''pixel_values''': images}
							return BatchFeature(data=__SCREAMING_SNAKE_CASE  ,						tensor_type=__SCREAMING_SNAKE_CASE					)












'''simple docstring'''







from __future__ import annotations





def 		_UpperCAmelCase   (							__A							:   list[int]      ):
				a_		:  int											=      len(__A      ) // 2

				# choose the middle 3 elements
				a_		:  Dict											=      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()




