



"""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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


A:     int										=	logging.get_logger(__name__)



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):
   __lowerCAmelCase						:     Any		=		['pixel_values']

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE = True   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC   ,  _SCREAMING_SNAKE_CASE = True   ,  _SCREAMING_SNAKE_CASE = 1 / 255   ,  _SCREAMING_SNAKE_CASE = True   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = True   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   None:





    '''simple docstring'''




    super().__init__(**_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[Any]			=			size if size is not None else {"""height""": 384, """width""": 384}
    UpperCAmelCase			:      int			=			get_size_dict(_SCREAMING_SNAKE_CASE   ,  default_to_square=_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Tuple			=			do_resize
    UpperCAmelCase			:      int			=			size
    UpperCAmelCase			:      Optional[int]			=			resample
    UpperCAmelCase			:      List[Any]			=			do_rescale
    UpperCAmelCase			:      str			=			rescale_factor
    UpperCAmelCase			:      List[str]			=			do_normalize
    UpperCAmelCase			:      Any			=			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
    UpperCAmelCase			:      Union[str, Any]			=			image_std if image_std is not None else OPENAI_CLIP_STD
    UpperCAmelCase			:      Any			=			do_convert_rgb

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC   ,  _SCREAMING_SNAKE_CASE = None   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   np.ndarray:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			get_size_dict(_SCREAMING_SNAKE_CASE   ,  default_to_square=_SCREAMING_SNAKE_CASE		)
    if "height" not in size or "width" not in size:
     raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"		)
    UpperCAmelCase			:      List[Any]			=			(size["""height"""], size["""width"""])
    return resize(_SCREAMING_SNAKE_CASE   ,  size=_SCREAMING_SNAKE_CASE   ,  resample=_SCREAMING_SNAKE_CASE   ,  data_format=_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   Tuple:





    '''simple docstring'''




    return rescale(_SCREAMING_SNAKE_CASE   ,  scale=_SCREAMING_SNAKE_CASE   ,  data_format=_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   np.ndarray:





    '''simple docstring'''




    return normalize(_SCREAMING_SNAKE_CASE   ,  mean=_SCREAMING_SNAKE_CASE   ,  std=_SCREAMING_SNAKE_CASE   ,  data_format=_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)




   def 			SCREAMING_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 = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   PIL.Image.Image:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			do_resize if do_resize is not None else self.do_resize
    UpperCAmelCase			:      int			=			resample if resample is not None else self.resample
    UpperCAmelCase			:      List[Any]			=			do_rescale if do_rescale is not None else self.do_rescale
    UpperCAmelCase			:      Optional[Any]			=			rescale_factor if rescale_factor is not None else self.rescale_factor
    UpperCAmelCase			:      List[Any]			=			do_normalize if do_normalize is not None else self.do_normalize
    UpperCAmelCase			:      Union[str, Any]			=			image_mean if image_mean is not None else self.image_mean
    UpperCAmelCase			:      Tuple			=			image_std if image_std is not None else self.image_std
    UpperCAmelCase			:      List[Any]			=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

    UpperCAmelCase			:      Union[str, Any]			=			size if size is not None else self.size
    UpperCAmelCase			:      str			=			get_size_dict(_SCREAMING_SNAKE_CASE   ,  default_to_square=_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      List[str]			=			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 or resample is None:
     raise ValueError("""Size and resample 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_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."""		)

    # PIL RGBA images are converted to RGB
    if do_convert_rgb:
     UpperCAmelCase			:      Optional[int]			=			[convert_to_rgb(_SCREAMING_SNAKE_CASE		) for image in images]

    # All transformations expect numpy arrays.
    UpperCAmelCase			:      Union[str, Any]			=			[to_numpy_array(_SCREAMING_SNAKE_CASE		) for image in images]

    if do_resize:
     UpperCAmelCase			:      Optional[Any]			=			[self.resize(image=_SCREAMING_SNAKE_CASE   ,  size=_SCREAMING_SNAKE_CASE   ,  resample=_SCREAMING_SNAKE_CASE		) for image in images]

    if do_rescale:
     UpperCAmelCase			:      Union[str, Any]			=			[self.rescale(image=_SCREAMING_SNAKE_CASE   ,  scale=_SCREAMING_SNAKE_CASE		) for image in images]

    if do_normalize:
     UpperCAmelCase			:      Tuple			=			[self.normalize(image=_SCREAMING_SNAKE_CASE   ,  mean=_SCREAMING_SNAKE_CASE   ,  std=_SCREAMING_SNAKE_CASE		) for image in images]

    UpperCAmelCase			:      Optional[Any]			=			[to_channel_dimension_format(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		) for image in images]

    UpperCAmelCase			:      Optional[Any]			=			BatchFeature(data={"""pixel_values""": images}   ,  tensor_type=_SCREAMING_SNAKE_CASE		)

    return encoded_outputs





"""simple docstring"""
from __future__ import annotations

from collections.abc import Generator

def    _snake_case		(		):
 UpperCAmelCase			:      dict[int, int]			=			{}
 UpperCAmelCase			:      str			=			2
 while True:
  UpperCAmelCase			:      List[str]			=			factor_map.pop(UpperCamelCase    ,	UpperCamelCase		)
  if factor:
   UpperCAmelCase			:      Dict			=			factor + prime
   while x in factor_map:
    x += factor
   UpperCAmelCase			:      Tuple			=			factor
  else:
   UpperCAmelCase			:      Optional[int]			=			prime
   yield prime
  prime += 1

def    _snake_case		(		UpperCamelCase		:	float = 1e1_0		):
 UpperCAmelCase			:      Tuple			=			sieve()

 UpperCAmelCase			:      Optional[Any]			=			1
 while True:
  UpperCAmelCase			:      List[str]			=			next(UpperCamelCase		)
  if (2 * prime * n) > limit:
   return n
  # Ignore the next prime as the reminder will be 2.
  next(UpperCamelCase		)
  n += 2


if __name__ == "__main__":
      print(solution())
