



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


a			:     Tuple     = logging.get_logger(__name__)


if is_vision_available():
	import PIL







class 							_a							(      _lowerCAmelCase				):
			A					=  ['''pixel_values''']

			def __init__(self,      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR,      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = 1 / 255,      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      **SCREAMING_SNAKE_CASE_,      )					->      None:
						super().__init__(**SCREAMING_SNAKE_CASE_				)

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

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

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None				)					->      np.ndarray:
						UpperCAmelCase_			,       UpperCAmelCase_:		int       			=    get_image_size(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_			,       UpperCAmelCase_:		List[Any]       			=    size["""height"""], size["""width"""]

						if (output_width < output_height and input_width > input_height) or (
						    output_width > output_height and input_width < input_height
						):
									UpperCAmelCase_:		Dict       			=    np.rotaa(SCREAMING_SNAKE_CASE_,      3				)

						if data_format is not None:
									UpperCAmelCase_:		str       			=    to_channel_dimension_format(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)

						return image

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = None,      )					->      np.ndarray:
						UpperCAmelCase_			,       UpperCAmelCase_:		List[str]       			=    size["""height"""], size["""width"""]
						UpperCAmelCase_			,       UpperCAmelCase_:		List[Any]       			=    get_image_size(SCREAMING_SNAKE_CASE_				)

						UpperCAmelCase_:		Any       			=    output_width - input_width
						UpperCAmelCase_:		Optional[Any]       			=    output_height - input_height

						if random_padding:
									UpperCAmelCase_:		List[str]       			=    np.random.randint(low=0,      high=delta_height + 1				)
									UpperCAmelCase_:		Tuple       			=    np.random.randint(low=0,      high=delta_width + 1				)
						else:
									UpperCAmelCase_:		Union[str, Any]       			=    delta_height // 2
									UpperCAmelCase_:		List[Any]       			=    delta_width // 2

						UpperCAmelCase_:		str       			=    delta_height - pad_top
						UpperCAmelCase_:		List[str]       			=    delta_width - pad_left

						UpperCAmelCase_:		List[Any]       			=    ((pad_top, pad_bottom), (pad_left, pad_right))
						return pad(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      data_format=SCREAMING_SNAKE_CASE_				)

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

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC,      SCREAMING_SNAKE_CASE_ = None,      **SCREAMING_SNAKE_CASE_,      )					->      np.ndarray:
						UpperCAmelCase_			,       UpperCAmelCase_:		Any       			=    get_image_size(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_			,       UpperCAmelCase_:		str       			=    size["""height"""], size["""width"""]

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

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

						if input_height > input_width:
									UpperCAmelCase_:		Optional[int]       			=    int(input_width * height / input_height				)
						elif input_width > input_height:
									UpperCAmelCase_:		Dict       			=    int(input_height * width / input_width				)

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

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC,      SCREAMING_SNAKE_CASE_ = None,      **SCREAMING_SNAKE_CASE_,      )					->      np.ndarray:
						UpperCAmelCase_:		Union[str, Any]       			=    get_size_dict(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		List[Any]       			=    min(size["""height"""],      size["""width"""]				)
						UpperCAmelCase_:		Tuple       			=    get_resize_output_image_size(SCREAMING_SNAKE_CASE_,      size=SCREAMING_SNAKE_CASE_,      default_to_square=SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		str       			=    resize(SCREAMING_SNAKE_CASE_,      size=SCREAMING_SNAKE_CASE_,      resample=SCREAMING_SNAKE_CASE_,      data_format=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_				)
						return resized_image

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None,      **SCREAMING_SNAKE_CASE_,      )					->      Tuple:
						return rescale(SCREAMING_SNAKE_CASE_,      scale=SCREAMING_SNAKE_CASE_,      data_format=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_				)

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None,      **SCREAMING_SNAKE_CASE_,      )					->      np.ndarray:
						return normalize(SCREAMING_SNAKE_CASE_,      mean=SCREAMING_SNAKE_CASE_,      std=SCREAMING_SNAKE_CASE_,      data_format=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_				)

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

						UpperCAmelCase_:		Optional[Any]       			=    make_list_of_images(SCREAMING_SNAKE_CASE_				)

						if not valid_images(SCREAMING_SNAKE_CASE_				):
									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.
						UpperCAmelCase_:		List[Any]       			=    [to_numpy_array(SCREAMING_SNAKE_CASE_				) for image in images]

						if do_align_long_axis:
									UpperCAmelCase_:		Union[str, Any]       			=    [self.align_long_axis(SCREAMING_SNAKE_CASE_,      size=SCREAMING_SNAKE_CASE_				) for image in images]

						if do_resize:
									UpperCAmelCase_:		List[Any]       			=    [self.resize(image=SCREAMING_SNAKE_CASE_,      size=SCREAMING_SNAKE_CASE_,      resample=SCREAMING_SNAKE_CASE_				) for image in images]

						if do_thumbnail:
									UpperCAmelCase_:		Optional[int]       			=    [self.thumbnail(image=SCREAMING_SNAKE_CASE_,      size=SCREAMING_SNAKE_CASE_				) for image in images]

						if do_pad:
									UpperCAmelCase_:		Optional[int]       			=    [self.pad_image(image=SCREAMING_SNAKE_CASE_,      size=SCREAMING_SNAKE_CASE_,      random_padding=SCREAMING_SNAKE_CASE_				) for image in images]

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

						if do_normalize:
									UpperCAmelCase_:		List[Any]       			=    [self.normalize(image=SCREAMING_SNAKE_CASE_,      mean=SCREAMING_SNAKE_CASE_,      std=SCREAMING_SNAKE_CASE_				) for image in images]

						UpperCAmelCase_:		Any       			=    [to_channel_dimension_format(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				) for image in images]

						UpperCAmelCase_:		Dict       			=    {"""pixel_values""": images}
						return BatchFeature(data=SCREAMING_SNAKE_CASE_,      tensor_type=SCREAMING_SNAKE_CASE_				)











import decimal







def    lowerCAmelCase_  ():





			"""simple docstring"""


			UpperCAmelCase_:		Union[str, Any]       			=    0
			UpperCAmelCase_:		Optional[Any]       			=    decimal.Context(prec=1_0_5     )
			for i in range(2		,	1_0_0     ):
						UpperCAmelCase_:		str       			=    decimal.Decimal(lowerCAmelCase__     )
						UpperCAmelCase_:		Any       			=    number.sqrt(lowerCAmelCase__     )
						if len(str(lowerCAmelCase__     )     ) > 1:
									answer += int(str(lowerCAmelCase__     )[0]     )
									UpperCAmelCase_:		str       			=    str(lowerCAmelCase__     )[2:1_0_1]
									answer += sum(int(lowerCAmelCase__     ) for x in sqrt_number_str     )
			return answer


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(F'''{solution() = }''')






