
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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    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


__lowerCamelCase							=       logging.get_logger(__name__)


if is_vision_available():
   import PIL






class   UpperCAmelCase		(						A_ ):
   A__					:		str						     =     ["pixel_values"]



   def __init__(self		:					List[str]    ,						snake_case__		:					bool = True    ,						snake_case__		:					Dict[str, int] = None    ,						snake_case__		:					PILImageResampling = PILImageResampling.BICUBIC    ,						snake_case__		:					bool = True    ,						snake_case__		:					Dict[str, int] = None    ,						snake_case__		:					bool = True    ,						snake_case__		:					Union[int, float] = 1 / 2_55    ,						snake_case__		:					bool = True    ,						snake_case__		:					Optional[Union[float, List[float]]] = None    ,						snake_case__		:					Optional[Union[float, List[float]]] = None    ,						snake_case__		:					bool = True    ,						**snake_case__		:					List[Any]    ,						)		->	None:


          '''simple docstring'''


          super().__init__(**snake_case__ )
          snake_case       :   Any							=      size if size is not None else {"shortest_edge": 2_24}
          snake_case       :   str							=      get_size_dict(snake_case__    ,						default_to_square=snake_case__ )
          snake_case       :   Dict							=      crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
          snake_case       :   Optional[int]							=      get_size_dict(snake_case__    ,						default_to_square=snake_case__    ,						param_name="crop_size" )

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					Dict[str, int]    ,						snake_case__		:					PILImageResampling = PILImageResampling.BICUBIC    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None    ,						**snake_case__		:					Optional[int]    ,						)		->	np.ndarray:


          '''simple docstring'''


          snake_case       :   List[str]							=      get_size_dict(snake_case__    ,						default_to_square=snake_case__ )
          if "shortest_edge" not in size:
                 raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
          snake_case       :   Dict							=      get_resize_output_image_size(snake_case__    ,						size=size["shortest_edge"]    ,						default_to_square=snake_case__ )
          return resize(snake_case__    ,						size=snake_case__    ,						resample=snake_case__    ,						data_format=snake_case__    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					Dict[str, int]    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None    ,						**snake_case__		:					Optional[Any]    ,						)		->	np.ndarray:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      get_size_dict(snake_case__ )
          if "height" not in size or "width" not in size:
                 raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""" )
          return center_crop(snake_case__    ,						size=(size["height"], size["width"])    ,						data_format=snake_case__    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					Union[int, float]    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None    ,						**snake_case__		:					Optional[int]    ,						)		->	Any:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					Union[float, List[float]]    ,						snake_case__		:					Union[float, List[float]]    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None    ,						**snake_case__		:					Optional[int]    ,						)		->	np.ndarray:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					ImageInput    ,						snake_case__		:					bool = None    ,						snake_case__		:					Dict[str, int] = None    ,						snake_case__		:					PILImageResampling = None    ,						snake_case__		:					bool = None    ,						snake_case__		:					int = None    ,						snake_case__		:					bool = None    ,						snake_case__		:					float = None    ,						snake_case__		:					bool = None    ,						snake_case__		:					Optional[Union[float, List[float]]] = None    ,						snake_case__		:					Optional[Union[float, List[float]]] = None    ,						snake_case__		:					bool = None    ,						snake_case__		:					Optional[Union[str, TensorType]] = None    ,						snake_case__		:					Optional[ChannelDimension] = ChannelDimension.FIRST    ,						**snake_case__		:					Any    ,						)		->	PIL.Image.Image:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      do_resize if do_resize is not None else self.do_resize
          snake_case       :   Union[str, Any]							=      size if size is not None else self.size
          snake_case       :   List[str]							=      get_size_dict(snake_case__    ,						param_name="size"    ,						default_to_square=snake_case__ )
          snake_case       :   Any							=      resample if resample is not None else self.resample
          snake_case       :   Dict							=      do_center_crop if do_center_crop is not None else self.do_center_crop
          snake_case       :   List[Any]							=      crop_size if crop_size is not None else self.crop_size
          snake_case       :   Optional[int]							=      get_size_dict(snake_case__    ,						param_name="crop_size"    ,						default_to_square=snake_case__ )
          snake_case       :   Any							=      do_rescale if do_rescale is not None else self.do_rescale
          snake_case       :   str							=      rescale_factor if rescale_factor is not None else self.rescale_factor
          snake_case       :   List[str]							=      do_normalize if do_normalize is not None else self.do_normalize
          snake_case       :   Union[str, Any]							=      image_mean if image_mean is not None else self.image_mean
          snake_case       :   str							=      image_std if image_std is not None else self.image_std
          snake_case       :   Union[str, Any]							=      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

          snake_case       :   int							=      make_list_of_images(snake_case__ )

          if not valid_images(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_center_crop and crop_size is None:
                 raise ValueError("Crop size must be specified if do_center_crop 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:
                 snake_case       :   Union[str, Any]							=      [convert_to_rgb(snake_case__ ) for image in images]

          # All transformations expect numpy arrays.
          snake_case       :   Optional[int]							=      [to_numpy_array(snake_case__ ) for image in images]

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

          if do_center_crop:
                 snake_case       :   Tuple							=      [self.center_crop(image=snake_case__    ,						size=snake_case__ ) for image in images]

          if do_rescale:
                 snake_case       :   Optional[Any]							=      [self.rescale(image=snake_case__    ,						scale=snake_case__ ) for image in images]

          if do_normalize:
                 snake_case       :   int							=      [self.normalize(image=snake_case__    ,						mean=snake_case__    ,						std=snake_case__ ) for image in images]

          snake_case       :   str							=      [to_channel_dimension_format(snake_case__    ,						snake_case__ ) for image in images]

          snake_case       :   Optional[Any]							=      {"pixel_values": images}
          return BatchFeature(data=snake_case__    ,						tensor_type=snake_case__ )






"""simple docstring"""
import math







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->bool:

  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5		,     int(math.sqrt(_SCREAMING_SNAKE_CASE   ) + 1   )		,     6   ):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True







def 						__a   (	_SCREAMING_SNAKE_CASE = 0.1   )	->int:
  a__:     str										=	3
  a__:     Optional[Any]										=	3

  while primes / (2 * j - 1) >= ratio:
    for i in range(j * j + j + 1		,     (j + 2) * (j + 2)		,     j + 1   ):
      primes += is_prime(_SCREAMING_SNAKE_CASE   )
    j += 2
  return j


if __name__ == "__main__":
 import doctest

 doctest.testmod()


