
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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_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


lowercase__				:      Optional[Any]	   = logging.get_logger(__name__)




class       a__	( UpperCamelCase__				):
     a		:      Tuple		 =     ["""pixel_values"""]







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



          '''simple docstring'''



          super().__init__(**A			)
          a     =				size if size is not None else {"shortest_edge": 384}
          a     =				get_size_dict(A  ,		default_to_square=A			)

          a     =				do_resize
          a     =				size
          # Default value set here for backwards compatibility where the value in config is None
          a     =				crop_pct if crop_pct is not None else 224 / 256
          a     =				resample
          a     =				do_rescale
          a     =				rescale_factor
          a     =				do_normalize
          a     =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          a     =				image_std if image_std is not None else IMAGENET_STANDARD_STD







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



          '''simple docstring'''



          a     =				get_size_dict(A  ,		default_to_square=A			)
          if "shortest_edge" not in size:
               raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''			)
          a     =				size["shortest_edge"]

          if shortest_edge < 384:
               # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
               a     =				int(shortest_edge / crop_pct			)
               a     =				get_resize_output_image_size(A  ,		size=A  ,		default_to_square=A			)
               a     =				resize(image=A  ,		size=A  ,		resample=A  ,		data_format=A  ,		**A			)
               # then crop to (shortest_edge, shortest_edge)
               return center_crop(image=A  ,		size=(shortest_edge, shortest_edge)  ,		data_format=A  ,		**A			)
          else:
               # warping (no cropping) when evaluated at 384 or larger
               return resize(
                   A  ,		size=(shortest_edge, shortest_edge)  ,		resample=A  ,		data_format=A  ,		**A			)







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A = None  ,		**A  ,		)  ->   Tuple:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A  ,		A = None  ,		**A  ,		)  ->   np.ndarray:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self  ,		A  ,		A = None  ,		A = None  ,		A = None  ,		A = None  ,		A = None  ,		A = None  ,		A = None  ,		A = None  ,		A = None  ,		A = None  ,		A = ChannelDimension.FIRST  ,		**A  ,		)  ->   PIL.Image.Image:



          '''simple docstring'''



          a     =				do_resize if do_resize is not None else self.do_resize
          a     =				crop_pct if crop_pct is not None else self.crop_pct
          a     =				resample if resample is not None else self.resample
          a     =				do_rescale if do_rescale is not None else self.do_rescale
          a     =				rescale_factor if rescale_factor is not None else self.rescale_factor
          a     =				do_normalize if do_normalize is not None else self.do_normalize
          a     =				image_mean if image_mean is not None else self.image_mean
          a     =				image_std if image_std is not None else self.image_std

          a     =				size if size is not None else self.size
          a     =				get_size_dict(A  ,		default_to_square=A			)

          a     =				make_list_of_images(A			)

          if not valid_images(A			):
               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_resize and size["shortest_edge"] < 384 and crop_pct is None:
               raise ValueError("crop_pct must be specified if size < 384."			)

          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."			)

          # All transformations expect numpy arrays.
          a     =				[to_numpy_array(A			) for image in images]

          if do_resize:
               a     =				[self.resize(image=A  ,		size=A  ,		crop_pct=A  ,		resample=A			) for image in images]

          if do_rescale:
               a     =				[self.rescale(image=A  ,		scale=A			) for image in images]

          if do_normalize:
               a     =				[self.normalize(image=A  ,		mean=A  ,		std=A			) for image in images]

          a     =				[to_channel_dimension_format(A  ,		A			) for image in images]

          a     =				{"pixel_values": images}
          return BatchFeature(data=A  ,		tensor_type=A			)







import math

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					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(__UpperCamelCase) + 1)		, 6):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase = 0.1)      ->					int:
     a     =				3
     a     =				3

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


if __name__ == "__main__":
   import doctest

   doctest.testmod()

