




"""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


__A         =				logging.get_logger(__name__)


if is_vision_available():
       import PIL


class 					UpperCAmelCase		(lowerCAmelCase_						):



    """simple docstring"""

    _UpperCAmelCase			:Optional[Any]							=				['pixel_values']






    def __init__(     self					,    _UpperCAmelCase = True					,    _UpperCAmelCase = None					,    _UpperCAmelCase = PILImageResampling.BILINEAR					,    _UpperCAmelCase = True					,    _UpperCAmelCase = False					,    _UpperCAmelCase = True					,    _UpperCAmelCase = True					,    _UpperCAmelCase = 1 / 255					,    _UpperCAmelCase = True					,    _UpperCAmelCase = None					,    _UpperCAmelCase = None					,    **_UpperCAmelCase					,    ):
       super().__init__(**lowerCamelCase_							)

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

       lowercase__:     Any      =   do_resize
       lowercase__:     Any      =   size
       lowercase__:     Dict      =   resample
       lowercase__:     Any      =   do_thumbnail
       lowercase__:     Optional[int]      =   do_align_long_axis
       lowercase__:     Dict      =   do_pad
       lowercase__:     int      =   do_rescale
       lowercase__:     Optional[Any]      =   rescale_factor
       lowercase__:     str      =   do_normalize
       lowercase__:     Dict      =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       lowercase__:     Optional[int]      =   image_std if image_std is not None else IMAGENET_STANDARD_STD






    def _snake_case    (     self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase = None							):
       lowercase__,				lowercase__:     Dict      =   get_image_size(lowerCamelCase_							)
       lowercase__,				lowercase__:     List[str]      =   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__:     List[Any]      =   np.rotaa(lowerCamelCase_					,    3							)

       if data_format is not None:
          lowercase__:     List[Any]      =   to_channel_dimension_format(lowerCamelCase_					,    lowerCamelCase_							)

       return image






    def _snake_case    (     self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase = False					,    _UpperCAmelCase = None					,    ):
       lowercase__,				lowercase__:     Tuple      =   size['''height'''], size['''width''']
       lowercase__,				lowercase__:     Tuple      =   get_image_size(lowerCamelCase_							)

       lowercase__:     Tuple      =   output_width - input_width
       lowercase__:     int      =   output_height - input_height

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

       lowercase__:     Tuple      =   delta_height - pad_top
       lowercase__:     Any      =   delta_width - pad_left

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






    def _snake_case    (     self					,    *_UpperCAmelCase					,    **_UpperCAmelCase							):
       logger.info('''pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'''							)
       return self.pad_image(*lowerCamelCase_					,    **lowerCamelCase_							)






    def _snake_case    (     self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase = PILImageResampling.BICUBIC					,    _UpperCAmelCase = None					,    **_UpperCAmelCase					,    ):
       lowercase__,				lowercase__:     List[Any]      =   get_image_size(lowerCamelCase_							)
       lowercase__,				lowercase__:     Tuple      =   size['''height'''], size['''width''']

       # We always resize to the smallest of either the input or output size.
       lowercase__:     Union[str, Any]      =   min(lowerCamelCase_					,    lowerCamelCase_							)
       lowercase__:     Union[str, Any]      =   min(lowerCamelCase_					,    lowerCamelCase_							)

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

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

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






    def _snake_case    (     self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase = PILImageResampling.BICUBIC					,    _UpperCAmelCase = None					,    **_UpperCAmelCase					,    ):
       lowercase__:     Dict      =   get_size_dict(lowerCamelCase_							)
       lowercase__:     List[Any]      =   min(size['''height''']					,    size['''width''']							)
       lowercase__:     List[Any]      =   get_resize_output_image_size(lowerCamelCase_					,    size=lowerCamelCase_					,    default_to_square=lowerCamelCase_							)
       lowercase__:     List[Any]      =   resize(lowerCamelCase_					,    size=lowerCamelCase_					,    resample=lowerCamelCase_					,    data_format=lowerCamelCase_					,    **lowerCamelCase_							)
       return resized_image






    def _snake_case    (     self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase = None					,    **_UpperCAmelCase					,    ):
       return rescale(lowerCamelCase_					,    scale=lowerCamelCase_					,    data_format=lowerCamelCase_					,    **lowerCamelCase_							)






    def _snake_case    (     self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase = None					,    **_UpperCAmelCase					,    ):
       return normalize(lowerCamelCase_					,    mean=lowerCamelCase_					,    std=lowerCamelCase_					,    data_format=lowerCamelCase_					,    **lowerCamelCase_							)






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

       lowercase__:     Dict      =   make_list_of_images(lowerCamelCase_							)

       if not valid_images(lowerCamelCase_							):
          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__:     int      =   [to_numpy_array(lowerCamelCase_							) for image in images]

       if do_align_long_axis:
          lowercase__:     Any      =   [self.align_long_axis(lowerCamelCase_					,    size=lowerCamelCase_							) for image in images]

       if do_resize:
          lowercase__:     Optional[int]      =   [self.resize(image=lowerCamelCase_					,    size=lowerCamelCase_					,    resample=lowerCamelCase_							) for image in images]

       if do_thumbnail:
          lowercase__:     Any      =   [self.thumbnail(image=lowerCamelCase_					,    size=lowerCamelCase_							) for image in images]

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

       if do_rescale:
          lowercase__:     int      =   [self.rescale(image=lowerCamelCase_					,    scale=lowerCamelCase_							) for image in images]

       if do_normalize:
          lowercase__:     Any      =   [self.normalize(image=lowerCamelCase_					,    mean=lowerCamelCase_					,    std=lowerCamelCase_							) for image in images]

       lowercase__:     List[str]      =   [to_channel_dimension_format(lowerCamelCase_					,    lowerCamelCase_							) for image in images]

       lowercase__:     Optional[Any]      =   {'''pixel_values''': images}
       return BatchFeature(data=lowerCamelCase_					,    tensor_type=lowerCamelCase_							)

"""simple docstring"""






def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase = 1_0_0_0				)						->						int:
   lowercase__:     Union[str, Any]      =   0
   lowercase__:     Any      =   0
   while 1:
      num += 3
      if num >= n:
         break
      total += num
      num += 2
      if num >= n:
         break
      total += num
      num += 1
      if num >= n:
         break
      total += num
      num += 3
      if num >= n:
         break
      total += num
      num += 1
      if num >= n:
         break
      total += num
      num += 2
      if num >= n:
         break
      total += num
      num += 3
      if num >= n:
         break
      total += num
   return total


if __name__ == "__main__":
       print(f'''{solution() = }''')
