'''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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


_lowercase		:					List[Any]													=		logging.get_logger(__name__)


if is_vision_available():
						import PIL





class        __magic_name__		(       _lowerCAmelCase):
			UpperCamelCase__							=			['''pixel_values''']







			def __init__(      self							:    int							,     lowercase_							:    Optional[int] = True							,     lowercase_							:    Union[str, Any] = None							,     lowercase_							:    str = PILImageResampling.BILINEAR							,     lowercase_							:    Dict = True							,     lowercase_							:    Optional[Any] = False							,     lowercase_							:    str = True							,     lowercase_							:    Tuple = True							,     lowercase_							:    str = 1 / 255							,     lowercase_							:    Tuple = True							,     lowercase_							:    Union[str, Any] = None							,     lowercase_							:    Optional[Any] = None							,     **lowercase_							:    List[str]							,     ):
										super().__init__(**_UpperCAmelCase       )

										lowercase_   :     List[str]									=			size if size is not None else {'height': 2560, 'width': 1920}
										if isinstance(_UpperCAmelCase							,     (tuple, list)       ):
																	# The previous feature extractor size parameter was in (width, height) format
																	lowercase_   :     Union[str, Any]									=			size[::-1]
										lowercase_   :     Any									=			get_size_dict(_UpperCAmelCase       )

										lowercase_   :     List[str]									=			do_resize
										lowercase_   :     List[Any]									=			size
										lowercase_   :     Any									=			resample
										lowercase_   :     List[str]									=			do_thumbnail
										lowercase_   :     Tuple									=			do_align_long_axis
										lowercase_   :     List[str]									=			do_pad
										lowercase_   :     Union[str, Any]									=			do_rescale
										lowercase_   :     Union[str, Any]									=			rescale_factor
										lowercase_   :     str									=			do_normalize
										lowercase_   :     Any									=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										lowercase_   :     Any									=			image_std if image_std is not None else IMAGENET_STANDARD_STD







			def       SCREAMING_SNAKE_CASE_	(      self							:    int							,     lowercase_							:    List[Any]							,     lowercase_							:    Tuple							,     lowercase_							:    Union[str, Any] = None       ):
										lowercase_   :     List[Any]									=			get_image_size(_UpperCAmelCase       )
										lowercase_   :     Tuple									=			size['height'], size['width']

										if (output_width < output_height and input_width > input_height) or (
										    output_width > output_height and input_width < input_height
										):
																	lowercase_   :     Any									=			np.rotaa(_UpperCAmelCase							,     3       )

										if data_format is not None:
																	lowercase_   :     Optional[Any]									=			to_channel_dimension_format(_UpperCAmelCase							,     _UpperCAmelCase       )

										return image







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]							,     lowercase_							:    Optional[Any]							,     lowercase_							:    Union[str, Any]							,     lowercase_							:    Tuple = False							,     lowercase_							:    Optional[int] = None							,     ):
										lowercase_   :     Dict									=			size['height'], size['width']
										lowercase_   :     str									=			get_image_size(_UpperCAmelCase       )

										lowercase_   :     Optional[Any]									=			output_width - input_width
										lowercase_   :     Optional[int]									=			output_height - input_height

										if random_padding:
																	lowercase_   :     Optional[Any]									=			np.random.randint(low=0							,     high=delta_height + 1       )
																	lowercase_   :     Optional[int]									=			np.random.randint(low=0							,     high=delta_width + 1       )
										else:
																	lowercase_   :     Dict									=			delta_height // 2
																	lowercase_   :     List[Any]									=			delta_width // 2

										lowercase_   :     Optional[Any]									=			delta_height - pad_top
										lowercase_   :     Any									=			delta_width - pad_left

										lowercase_   :     int									=			((pad_top, pad_bottom), (pad_left, pad_right))
										return pad(_UpperCAmelCase							,     _UpperCAmelCase							,     data_format=_UpperCAmelCase       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple							,     *lowercase_							:    List[Any]							,     **lowercase_							:    str       ):
										logger.info("""pad is deprecated and will be removed in version 4.27. Please use pad_image instead."""       )
										return self.pad_image(*_UpperCAmelCase							,     **_UpperCAmelCase       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]							,     lowercase_							:    Optional[Any]							,     lowercase_							:    List[Any]							,     lowercase_							:    List[Any] = PILImageResampling.BICUBIC							,     lowercase_							:    Dict = None							,     **lowercase_							:    Tuple							,     ):
										lowercase_   :     Tuple									=			get_image_size(_UpperCAmelCase       )
										lowercase_   :     Optional[int]									=			size['height'], size['width']

										# We always resize to the smallest of either the input or output size.
										lowercase_   :     List[Any]									=			min(_UpperCAmelCase							,     _UpperCAmelCase       )
										lowercase_   :     Optional[Any]									=			min(_UpperCAmelCase							,     _UpperCAmelCase       )

										if height == input_height and width == input_width:
																	return image

										if input_height > input_width:
																	lowercase_   :     Optional[Any]									=			int(input_width * height / input_height       )
										elif input_width > input_height:
																	lowercase_   :     List[str]									=			int(input_height * width / input_width       )

										return resize(
										    _UpperCAmelCase							,     size=(height, width)							,     resample=_UpperCAmelCase							,     reducing_gap=2.0							,     data_format=_UpperCAmelCase							,     **_UpperCAmelCase       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    str							,     lowercase_							:    str							,     lowercase_							:    int							,     lowercase_							:    Union[str, Any] = PILImageResampling.BICUBIC							,     lowercase_							:    Optional[int] = None							,     **lowercase_							:    Optional[int]							,     ):
										lowercase_   :     List[str]									=			get_size_dict(_UpperCAmelCase       )
										lowercase_   :     List[str]									=			min(size["""height"""]							,     size["""width"""]       )
										lowercase_   :     List[str]									=			get_resize_output_image_size(_UpperCAmelCase							,     size=_UpperCAmelCase							,     default_to_square=_UpperCAmelCase       )
										lowercase_   :     Optional[Any]									=			resize(_UpperCAmelCase							,     size=_UpperCAmelCase							,     resample=_UpperCAmelCase							,     data_format=_UpperCAmelCase							,     **_UpperCAmelCase       )
										return resized_image







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]							,     lowercase_							:    int							,     lowercase_							:    List[str]							,     lowercase_							:    List[str] = None							,     **lowercase_							:    Tuple							,     ):
										return rescale(_UpperCAmelCase							,     scale=_UpperCAmelCase							,     data_format=_UpperCAmelCase							,     **_UpperCAmelCase       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    str							,     lowercase_							:    Tuple							,     lowercase_							:    Optional[int]							,     lowercase_							:    Optional[int]							,     lowercase_							:    int = None							,     **lowercase_							:    int							,     ):
										return normalize(_UpperCAmelCase							,     mean=_UpperCAmelCase							,     std=_UpperCAmelCase							,     data_format=_UpperCAmelCase							,     **_UpperCAmelCase       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    int							,     lowercase_							:    Any							,     lowercase_							:    Tuple = None							,     lowercase_							:    Dict = None							,     lowercase_							:    int = None							,     lowercase_							:    Any = None							,     lowercase_							:    List[str] = None							,     lowercase_							:    Tuple = None							,     lowercase_							:    Any = False							,     lowercase_							:    int = None							,     lowercase_							:    Union[str, Any] = None							,     lowercase_							:    Union[str, Any] = None							,     lowercase_							:    Dict = None							,     lowercase_							:    List[Any] = None							,     lowercase_							:    Tuple = None							,     lowercase_							:    List[str] = ChannelDimension.FIRST							,     **lowercase_							:    Union[str, Any]							,     ):
										lowercase_   :     str									=			do_resize if do_resize is not None else self.do_resize
										lowercase_   :     str									=			size if size is not None else self.size
										if isinstance(_UpperCAmelCase							,     (tuple, list)       ):
																	# Previous feature extractor had size in (width, height) format
																	lowercase_   :     Union[str, Any]									=			size[::-1]
										lowercase_   :     Dict									=			get_size_dict(_UpperCAmelCase       )
										lowercase_   :     Optional[Any]									=			resample if resample is not None else self.resample
										lowercase_   :     List[str]									=			do_thumbnail if do_thumbnail is not None else self.do_thumbnail
										lowercase_   :     Optional[int]									=			do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
										lowercase_   :     Optional[int]									=			do_pad if do_pad is not None else self.do_pad
										lowercase_   :     List[Any]									=			do_rescale if do_rescale is not None else self.do_rescale
										lowercase_   :     Any									=			rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase_   :     str									=			do_normalize if do_normalize is not None else self.do_normalize
										lowercase_   :     Dict									=			image_mean if image_mean is not None else self.image_mean
										lowercase_   :     Dict									=			image_std if image_std is not None else self.image_std

										lowercase_   :     Optional[int]									=			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:
																	raise ValueError("""Size 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_pad and size is None:
																	raise ValueError("""Size must be specified if do_pad 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.
										lowercase_   :     List[str]									=			[to_numpy_array(_UpperCAmelCase       ) for image in images]

										if do_align_long_axis:
																	lowercase_   :     Dict									=			[self.align_long_axis(_UpperCAmelCase							,     size=_UpperCAmelCase       ) for image in images]

										if do_resize:
																	lowercase_   :     List[str]									=			[self.resize(image=_UpperCAmelCase							,     size=_UpperCAmelCase							,     resample=_UpperCAmelCase       ) for image in images]

										if do_thumbnail:
																	lowercase_   :     Tuple									=			[self.thumbnail(image=_UpperCAmelCase							,     size=_UpperCAmelCase       ) for image in images]

										if do_pad:
																	lowercase_   :     str									=			[self.pad_image(image=_UpperCAmelCase							,     size=_UpperCAmelCase							,     random_padding=_UpperCAmelCase       ) for image in images]

										if do_rescale:
																	lowercase_   :     Optional[int]									=			[self.rescale(image=_UpperCAmelCase							,     scale=_UpperCAmelCase       ) for image in images]

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

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

										lowercase_   :     List[str]									=			{'pixel_values': images}
										return BatchFeature(data=_UpperCAmelCase							,     tensor_type=_UpperCAmelCase       )








def  _snake_case		(snake_case_					:     int = 100    ,       snake_case_					:     int = 100  )							->		int:
  return max(
      sum(int(snake_case_  ) for x in str(base**power  )  )
      for base in range(snake_case_  )
      for power in range(snake_case_  )  )


# Tests
if __name__ == "__main__":
      import doctest

      doctest.testmod()





