





'''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 (
    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							:     Dict		     =logging.get_logger(__name__)


if is_vision_available():
  import PIL






class        UpperCAmelCase     (	UpperCamelCase__    ):
 __lowercase       		=    ["""pixel_values"""]
 def __init__(    self				:int						,	lowercase_				:bool = True						,	lowercase_				:Dict[str, int] = None						,	lowercase_				:PILImageResampling = PILImageResampling.BICUBIC						,	lowercase_				:bool = True						,	lowercase_				:Dict[str, int] = None						,	lowercase_				:bool = True						,	lowercase_				:Union[int, float] = 1 / 2_55						,	lowercase_				:bool = True						,	lowercase_				:Optional[Union[float, List[float]]] = None						,	lowercase_				:Optional[Union[float, List[float]]] = None						,	lowercase_				:bool = True						,	**lowercase_				:List[Any]						,	)->							None:
     super().__init__(**lowercase_							)
     A__    =      size if size is not None else {"shortest_edge": 2_24}
     A__    =      get_size_dict(lowercase_						,	default_to_square=lowercase_							)
     A__    =      crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
     A__    =      get_size_dict(lowercase_						,	default_to_square=lowercase_						,	param_name="crop_size"							)

     A__    =      do_resize
     A__    =      size
     A__    =      resample
     A__    =      do_center_crop
     A__    =      crop_size
     A__    =      do_rescale
     A__    =      rescale_factor
     A__    =      do_normalize
     A__    =      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
     A__    =      image_std if image_std is not None else OPENAI_CLIP_STD
     A__    =      do_convert_rgb
 def 					UpperCAmelCase_							(    self				:str						,	lowercase_				:np.ndarray						,	lowercase_				:Dict[str, int]						,	lowercase_				:PILImageResampling = PILImageResampling.BICUBIC						,	lowercase_				:Optional[Union[str, ChannelDimension]] = None						,	**lowercase_				:Optional[Any]						,	)->							np.ndarray:
     A__    =      get_size_dict(lowercase_						,	default_to_square=lowercase_							)
     if "shortest_edge" not in size:
         raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"							)
     A__    =      get_resize_output_image_size(lowercase_						,	size=size["shortest_edge"]						,	default_to_square=lowercase_							)
     return resize(lowercase_						,	size=lowercase_						,	resample=lowercase_						,	data_format=lowercase_						,	**lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]						,	lowercase_				:np.ndarray						,	lowercase_				:Dict[str, int]						,	lowercase_				:Optional[Union[str, ChannelDimension]] = None						,	**lowercase_				:List[Any]						,	)->							np.ndarray:
     A__    =      get_size_dict(lowercase_							)
     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(lowercase_						,	size=(size["height"], size["width"])						,	data_format=lowercase_						,	**lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[int]						,	lowercase_				:np.ndarray						,	lowercase_				:Union[int, float]						,	lowercase_				:Optional[Union[str, ChannelDimension]] = None						,	**lowercase_				:Any						,	)->							Union[str, Any]:
     return rescale(lowercase_						,	scale=lowercase_						,	data_format=lowercase_						,	**lowercase_							)
 def 					UpperCAmelCase_							(    self				:Tuple						,	lowercase_				:np.ndarray						,	lowercase_				:Union[float, List[float]]						,	lowercase_				:Union[float, List[float]]						,	lowercase_				:Optional[Union[str, ChannelDimension]] = None						,	**lowercase_				:str						,	)->							np.ndarray:
     return normalize(lowercase_						,	mean=lowercase_						,	std=lowercase_						,	data_format=lowercase_						,	**lowercase_							)



 def 					UpperCAmelCase_							(    self				:Tuple						,	lowercase_				:ImageInput						,	lowercase_				:bool = None						,	lowercase_				:Dict[str, int] = None						,	lowercase_				:PILImageResampling = None						,	lowercase_				:bool = None						,	lowercase_				:int = None						,	lowercase_				:bool = None						,	lowercase_				:float = None						,	lowercase_				:bool = None						,	lowercase_				:Optional[Union[float, List[float]]] = None						,	lowercase_				:Optional[Union[float, List[float]]] = None						,	lowercase_				:bool = None						,	lowercase_				:Optional[Union[str, TensorType]] = None						,	lowercase_				:Optional[ChannelDimension] = ChannelDimension.FIRST						,	**lowercase_				:str						,	)->							PIL.Image.Image:
     A__    =      do_resize if do_resize is not None else self.do_resize
     A__    =      size if size is not None else self.size
     A__    =      get_size_dict(lowercase_						,	param_name="size"						,	default_to_square=lowercase_							)
     A__    =      resample if resample is not None else self.resample
     A__    =      do_center_crop if do_center_crop is not None else self.do_center_crop
     A__    =      crop_size if crop_size is not None else self.crop_size
     A__    =      get_size_dict(lowercase_						,	param_name="crop_size"						,	default_to_square=lowercase_							)
     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__    =      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     A__    =      make_list_of_images(lowercase_							)

     if not valid_images(lowercase_							):
         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:
         A__    =      [convert_to_rgb(lowercase_							) for image in images]

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

     if do_resize:
         A__    =      [self.resize(image=lowercase_						,	size=lowercase_						,	resample=lowercase_							) for image in images]

     if do_center_crop:
         A__    =      [self.center_crop(image=lowercase_						,	size=lowercase_							) for image in images]

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

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

     A__    =      [to_channel_dimension_format(lowercase_						,	lowercase_							) for image in images]

     A__    =      {"pixel_values": images}
     return BatchFeature(data=lowercase_						,	tensor_type=lowercase_							)










'''simple docstring'''

def 						UpperCamelCase    (							_lowerCamelCase : int = 1_00_00_00	):
    A__    =      set(range(3						,	_lowerCamelCase						,	2	)	)
    primes.add(2	)
    for p in range(3						,	_lowerCamelCase						,	2	):
        if p not in primes:
            continue
        primes.difference_update(set(range(p * p						,	_lowerCamelCase						,	_lowerCamelCase	)	)	)

    A__    =      [float(_lowerCamelCase	) for n in range(limit + 1	)]

    for p in primes:
        for n in range(_lowerCamelCase						,	limit + 1						,	_lowerCamelCase	):
            phi[n] *= 1 - 1 / p

    return int(sum(phi[2:]	)	)


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



