


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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_A		:				Tuple									=       logging.get_logger(__name__)



class        a					(			_UpperCAmelCase   ):
     UpperCAmelCase__ :  List[str]						      =	["pixel_values"]





     def __init__(    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				bool = True    ,	SCREAMING_SNAKE_CASE_					:				Optional[Dict[str, int]] = None    ,	SCREAMING_SNAKE_CASE_					:				PILImageResampling = PILImageResampling.BICUBIC    ,	SCREAMING_SNAKE_CASE_					:				bool = True    ,	SCREAMING_SNAKE_CASE_					:				bool = True    ,	SCREAMING_SNAKE_CASE_					:				Union[int, float] = 1 / 255    ,	SCREAMING_SNAKE_CASE_					:				Dict[str, int] = None    ,	SCREAMING_SNAKE_CASE_					:				bool = True    ,	SCREAMING_SNAKE_CASE_					:				Optional[Union[float, List[float]]] = None    ,	SCREAMING_SNAKE_CASE_					:				Optional[Union[float, List[float]]] = None    ,	**SCREAMING_SNAKE_CASE_					:				str    ,	):
        super().__init__(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[int]     =	size if size is not None else {"""height""": 224, """width""": 224}
        __lowerCamelCase:				Dict     =	get_size_dict(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        __lowerCamelCase:				str     =	get_size_dict(SCREAMING_SNAKE_CASE_    ,	default_to_square=SCREAMING_SNAKE_CASE_    ,	param_name="""crop_size"""							)

        __lowerCamelCase:				str     =	do_resize
        __lowerCamelCase:				str     =	do_rescale
        __lowerCamelCase:				Union[str, Any]     =	do_normalize
        __lowerCamelCase:				str     =	do_center_crop
        __lowerCamelCase:				Optional[Any]     =	crop_size
        __lowerCamelCase:				int     =	size
        __lowerCamelCase:				List[Any]     =	resample
        __lowerCamelCase:				int     =	rescale_factor
        __lowerCamelCase:				int     =	image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        __lowerCamelCase:				Any     =	image_std if image_std is not None else IMAGENET_DEFAULT_STD





     def  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				np.ndarray    ,	SCREAMING_SNAKE_CASE_					:				Dict[str, int]    ,	SCREAMING_SNAKE_CASE_					:				PILImageResampling = PILImageResampling.BILINEAR    ,	SCREAMING_SNAKE_CASE_					:				Optional[Union[str, ChannelDimension]] = None    ,	**SCREAMING_SNAKE_CASE_					:				int    ,	):
        __lowerCamelCase:				str     =	get_size_dict(SCREAMING_SNAKE_CASE_							)

        if "shortest_edge" in size:
           __lowerCamelCase:				List[str]     =	get_resize_output_image_size(SCREAMING_SNAKE_CASE_    ,	size=size["""shortest_edge"""]    ,	default_to_square=SCREAMING_SNAKE_CASE_							)
           # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
        elif "height" in size and "width" in size:
           __lowerCamelCase:				List[str]     =	(size["""height"""], size["""width"""])
        else:
           raise ValueError(F'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'							)
        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					:				Any    ,	SCREAMING_SNAKE_CASE_					:				np.ndarray    ,	SCREAMING_SNAKE_CASE_					:				Dict[str, int]    ,	SCREAMING_SNAKE_CASE_					:				Optional[Union[str, ChannelDimension]] = None    ,	**SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	):
        __lowerCamelCase:				Optional[Any]     =	get_size_dict(SCREAMING_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(SCREAMING_SNAKE_CASE_    ,	size=(size["""height"""], size["""width"""])    ,	data_format=SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				np.ndarray    ,	SCREAMING_SNAKE_CASE_					:				float    ,	SCREAMING_SNAKE_CASE_					:				Optional[Union[str, ChannelDimension]] = None    ,	**SCREAMING_SNAKE_CASE_					:				List[Any]							):
        return rescale(SCREAMING_SNAKE_CASE_    ,	scale=SCREAMING_SNAKE_CASE_    ,	data_format=SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				np.ndarray    ,	SCREAMING_SNAKE_CASE_					:				Union[float, List[float]]    ,	SCREAMING_SNAKE_CASE_					:				Union[float, List[float]]    ,	SCREAMING_SNAKE_CASE_					:				Optional[Union[str, ChannelDimension]] = None    ,	**SCREAMING_SNAKE_CASE_					:				List[Any]    ,	):
        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					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				ImageInput    ,	SCREAMING_SNAKE_CASE_					:				Optional[bool] = None    ,	SCREAMING_SNAKE_CASE_					:				Dict[str, int] = None    ,	SCREAMING_SNAKE_CASE_					:				PILImageResampling = None    ,	SCREAMING_SNAKE_CASE_					:				bool = None    ,	SCREAMING_SNAKE_CASE_					:				int = None    ,	SCREAMING_SNAKE_CASE_					:				Optional[bool] = None    ,	SCREAMING_SNAKE_CASE_					:				Optional[float] = None    ,	SCREAMING_SNAKE_CASE_					:				Optional[bool] = None    ,	SCREAMING_SNAKE_CASE_					:				Optional[Union[float, List[float]]] = None    ,	SCREAMING_SNAKE_CASE_					:				Optional[Union[float, List[float]]] = None    ,	SCREAMING_SNAKE_CASE_					:				Optional[Union[str, TensorType]] = None    ,	SCREAMING_SNAKE_CASE_					:				Union[str, ChannelDimension] = ChannelDimension.FIRST    ,	**SCREAMING_SNAKE_CASE_					:				str    ,	):
        __lowerCamelCase:				Optional[Any]     =	do_resize if do_resize is not None else self.do_resize
        __lowerCamelCase:				Dict     =	do_rescale if do_rescale is not None else self.do_rescale
        __lowerCamelCase:				Dict     =	do_normalize if do_normalize is not None else self.do_normalize
        __lowerCamelCase:				Tuple     =	do_center_crop if do_center_crop is not None else self.do_center_crop
        __lowerCamelCase:				Union[str, Any]     =	crop_size if crop_size is not None else self.crop_size
        __lowerCamelCase:				Union[str, Any]     =	get_size_dict(SCREAMING_SNAKE_CASE_    ,	param_name="""crop_size"""    ,	default_to_square=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Tuple     =	resample if resample is not None else self.resample
        __lowerCamelCase:				Any     =	rescale_factor if rescale_factor is not None else self.rescale_factor
        __lowerCamelCase:				Optional[Any]     =	image_mean if image_mean is not None else self.image_mean
        __lowerCamelCase:				Tuple     =	image_std if image_std is not None else self.image_std

        __lowerCamelCase:				Union[str, Any]     =	size if size is not None else self.size
        __lowerCamelCase:				int     =	get_size_dict(SCREAMING_SNAKE_CASE_							)

        if not is_batched(SCREAMING_SNAKE_CASE_							):
           __lowerCamelCase:				str     =	[images]

        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:
           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."""							)

        # All transformations expect numpy arrays.
        __lowerCamelCase:				str     =	[to_numpy_array(SCREAMING_SNAKE_CASE_							) for image in images]

        if do_resize:
           __lowerCamelCase:				List[str]     =	[self.resize(image=SCREAMING_SNAKE_CASE_    ,	size=SCREAMING_SNAKE_CASE_    ,	resample=SCREAMING_SNAKE_CASE_							) for image in images]

        if do_center_crop:
           __lowerCamelCase:				int     =	[self.center_crop(image=SCREAMING_SNAKE_CASE_    ,	size=SCREAMING_SNAKE_CASE_							) for image in images]

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

        if do_normalize:
           __lowerCamelCase:				str     =	[self.normalize(image=SCREAMING_SNAKE_CASE_    ,	mean=SCREAMING_SNAKE_CASE_    ,	std=SCREAMING_SNAKE_CASE_							) for image in images]

        __lowerCamelCase:				str     =	[to_channel_dimension_format(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) for image in images]

        __lowerCamelCase:				Dict     =	{"""pixel_values""": images}
        return BatchFeature(data=SCREAMING_SNAKE_CASE_    ,	tensor_type=SCREAMING_SNAKE_CASE_							)






def     __lowerCAmelCase						(							snake_case	:    int = 1000000 )       ->							int:
   __lowerCamelCase:				Union[str, Any]     =	set(range(3						,      snake_case						,      2 ) )
   primes.add(2 )
   for p in range(3						,      snake_case						,      2 ):
      if p not in primes:
         continue
      primes.difference_update(set(range(p * p						,      snake_case						,      snake_case ) ) )

   __lowerCamelCase:				Any     =	[float(snake_case ) for n in range(limit + 1 )]

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

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


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


