





from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
							import PIL

UpperCAmelCase__						:			int									=logging.get_logger(__name__)





def      _lowercase					(						_UpperCAmelCase				)						->  List[List[ImageInput]]:
				if isinstance(_UpperCAmelCase			,      (list, tuple)				) and isinstance(videos[0]			,      (list, tuple)				) and is_valid_image(videos[0][0]				):
								return videos

				elif isinstance(_UpperCAmelCase			,      (list, tuple)				) and is_valid_image(videos[0]				):
								return [videos]

				elif is_valid_image(_UpperCAmelCase				):
								return [[videos]]

				raise ValueError(F"""Could not make batched video from {videos}"""				)




class       __A     (						a    ):
				__A      =				["""pixel_values"""]


				def __init__(		self   , UpperCAmelCase_ = True   , UpperCAmelCase_ = None   , UpperCAmelCase_ = PILImageResampling.BILINEAR   , UpperCAmelCase_ = True   , UpperCAmelCase_ = None   , UpperCAmelCase_ = True   , UpperCAmelCase_ = 1 / 255   , UpperCAmelCase_ = True   , UpperCAmelCase_ = True   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , **UpperCAmelCase_   , ):
								super().__init__(**UpperCAmelCase_							)
								lowerCamelCase	 =size if size is not None else {"""shortest_edge""": 256}
								lowerCamelCase	 =get_size_dict(UpperCAmelCase_   , default_to_square=UpperCAmelCase_							)
								lowerCamelCase	 =crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
								lowerCamelCase	 =get_size_dict(UpperCAmelCase_   , param_name="""crop_size"""							)

								lowerCamelCase	 =do_resize
								lowerCamelCase	 =size
								lowerCamelCase	 =do_center_crop
								lowerCamelCase	 =crop_size
								lowerCamelCase	 =resample
								lowerCamelCase	 =do_rescale
								lowerCamelCase	 =rescale_factor
								lowerCamelCase	 =offset
								lowerCamelCase	 =do_normalize
								lowerCamelCase	 =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								lowerCamelCase	 =image_std if image_std is not None else IMAGENET_STANDARD_STD


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_ = PILImageResampling.BILINEAR   , UpperCAmelCase_ = None   , **UpperCAmelCase_   , ):
								lowerCamelCase	 =get_size_dict(UpperCAmelCase_   , default_to_square=UpperCAmelCase_							)
								if "shortest_edge" in size:
												lowerCamelCase	 =get_resize_output_image_size(UpperCAmelCase_   , size["""shortest_edge"""]   , default_to_square=UpperCAmelCase_							)
								elif "height" in size and "width" in size:
												lowerCamelCase	 =(size["""height"""], size["""width"""])
								else:
												raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""							)
								return resize(UpperCAmelCase_   , size=UpperCAmelCase_   , resample=UpperCAmelCase_   , data_format=UpperCAmelCase_   , **UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_ = None   , **UpperCAmelCase_   , ):
								lowerCamelCase	 =get_size_dict(UpperCAmelCase_							)
								if "height" not in size or "width" not in size:
												raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""							)
								return center_crop(UpperCAmelCase_   , size=(size["""height"""], size["""width"""])   , data_format=UpperCAmelCase_   , **UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_ = True   , UpperCAmelCase_ = None   , **UpperCAmelCase_   , ):
								lowerCamelCase	 =image.astype(np.floataa							)
								if offset:
												lowerCamelCase	 =image - (scale / 2)
								return rescale(UpperCAmelCase_   , scale=UpperCAmelCase_   , data_format=UpperCAmelCase_   , **UpperCAmelCase_							)


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


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = ChannelDimension.FIRST   , ):
								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_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."""							)

								if offset and not do_rescale:
												raise ValueError("""For offset, do_rescale must also be set to True."""							)

								# All transformations expect numpy arrays.
								lowerCamelCase	 =to_numpy_array(UpperCAmelCase_							)

								if do_resize:
												lowerCamelCase	 =self.resize(image=UpperCAmelCase_   , size=UpperCAmelCase_   , resample=UpperCAmelCase_							)

								if do_center_crop:
												lowerCamelCase	 =self.center_crop(UpperCAmelCase_   , size=UpperCAmelCase_							)

								if do_rescale:
												lowerCamelCase	 =self.rescale(image=UpperCAmelCase_   , scale=UpperCAmelCase_   , offset=UpperCAmelCase_							)

								if do_normalize:
												lowerCamelCase	 =self.normalize(image=UpperCAmelCase_   , mean=UpperCAmelCase_   , std=UpperCAmelCase_							)

								lowerCamelCase	 =to_channel_dimension_format(UpperCAmelCase_   , UpperCAmelCase_							)
								return image


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , UpperCAmelCase_ = ChannelDimension.FIRST   , **UpperCAmelCase_   , ):
								lowerCamelCase	 =do_resize if do_resize is not None else self.do_resize
								lowerCamelCase	 =resample if resample is not None else self.resample
								lowerCamelCase	 =do_center_crop if do_center_crop is not None else self.do_center_crop
								lowerCamelCase	 =do_rescale if do_rescale is not None else self.do_rescale
								lowerCamelCase	 =rescale_factor if rescale_factor is not None else self.rescale_factor
								lowerCamelCase	 =offset if offset is not None else self.offset
								lowerCamelCase	 =do_normalize if do_normalize is not None else self.do_normalize
								lowerCamelCase	 =image_mean if image_mean is not None else self.image_mean
								lowerCamelCase	 =image_std if image_std is not None else self.image_std

								lowerCamelCase	 =size if size is not None else self.size
								lowerCamelCase	 =get_size_dict(UpperCAmelCase_   , default_to_square=UpperCAmelCase_							)
								lowerCamelCase	 =crop_size if crop_size is not None else self.crop_size
								lowerCamelCase	 =get_size_dict(UpperCAmelCase_   , param_name="""crop_size"""							)

								if not valid_images(UpperCAmelCase_							):
												raise ValueError(
												    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
												    """torch.Tensor, tf.Tensor or jax.ndarray."""							)

								lowerCamelCase	 =make_batched(UpperCAmelCase_							)

								lowerCamelCase	 =[
								    [
								        self._preprocess_image(
								            image=UpperCAmelCase_   , do_resize=UpperCAmelCase_   , size=UpperCAmelCase_   , resample=UpperCAmelCase_   , do_center_crop=UpperCAmelCase_   , crop_size=UpperCAmelCase_   , do_rescale=UpperCAmelCase_   , rescale_factor=UpperCAmelCase_   , offset=UpperCAmelCase_   , do_normalize=UpperCAmelCase_   , image_mean=UpperCAmelCase_   , image_std=UpperCAmelCase_   , data_format=UpperCAmelCase_   , )
								        for img in video
								    ]
								    for video in videos
								]

								lowerCamelCase	 ={"""pixel_values""": videos}
								return BatchFeature(data=UpperCAmelCase_   , tensor_type=UpperCAmelCase_							)












import math
from collections.abc import Iterator
from itertools import takewhile





def      _lowercase					(						_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      _lowercase					(						)						->  Iterator[int]:
				lowerCamelCase	 =2
				while True:
								if is_prime(_UpperCAmelCase				):
												yield num
								num += 1





def      _lowercase					(						_UpperCAmelCase = 2_00_00_00				)						->  int:
				return sum(takewhile(lambda _UpperCAmelCase				: x < n			,      prime_generator()				)				)


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





