





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 convert_to_rgb, 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


if is_vision_available():
		import PIL


_lowerCamelCase :    str  							= logging.get_logger(__name__)


class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   ["pixel_values"]






							def __init__( self     :					Tuple,			_UpperCAmelCase     :					bool = True,			_UpperCAmelCase     :					Dict[str, int] = None,			_UpperCAmelCase     :					PILImageResampling = PILImageResampling.BICUBIC,			_UpperCAmelCase     :					bool = True,			_UpperCAmelCase     :					Union[int, float] = 1 / 2_5_5,			_UpperCAmelCase     :					bool = True,			_UpperCAmelCase     :					Optional[Union[float, List[float]]] = None,			_UpperCAmelCase     :					Optional[Union[float, List[float]]] = None,			_UpperCAmelCase     :					bool = True,			**_UpperCAmelCase     :					Dict,			)				->			None:





														"""simple docstring"""
														super().__init__(**_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Any     =						size if size is not None else {"height": 3_8_4, "width": 3_8_4}
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						get_size_dict(_UpperCAmelCase,			default_to_square=_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      List[str]     =						do_resize
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						size
														SCREAMING_SNAKE_CASE__       :      str     =						resample
														SCREAMING_SNAKE_CASE__       :      List[str]     =						do_rescale
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						rescale_factor
														SCREAMING_SNAKE_CASE__       :      Dict     =						do_normalize
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						image_mean if image_mean is not None else OPENAI_CLIP_MEAN
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						image_std if image_std is not None else OPENAI_CLIP_STD
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						do_convert_rgb






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						get_size_dict(_UpperCAmelCase,			default_to_square=_UpperCAmelCase						)
														if "height" not in size or "width" not in size:
																					raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''						)
														SCREAMING_SNAKE_CASE__       :      int     =						(size["height"], size["width"])
														return resize(_UpperCAmelCase,			size=_UpperCAmelCase,			resample=_UpperCAmelCase,			data_format=_UpperCAmelCase,			**_UpperCAmelCase						)






							def  A_     ( self     :					List[str],			_UpperCAmelCase     :					np.ndarray,			_UpperCAmelCase     :					Union[int, float],			_UpperCAmelCase     :					Optional[Union[str, ChannelDimension]] = None,			**_UpperCAmelCase     :					Optional[int],			)				->			Any:





														"""simple docstring"""
														return rescale(_UpperCAmelCase,			scale=_UpperCAmelCase,			data_format=_UpperCAmelCase,			**_UpperCAmelCase						)






							def  A_     ( self     :					List[Any],			_UpperCAmelCase     :					np.ndarray,			_UpperCAmelCase     :					Union[float, List[float]],			_UpperCAmelCase     :					Union[float, List[float]],			_UpperCAmelCase     :					Optional[Union[str, ChannelDimension]] = None,			**_UpperCAmelCase     :					Optional[int],			)				->			np.ndarray:





														"""simple docstring"""
														return normalize(_UpperCAmelCase,			mean=_UpperCAmelCase,			std=_UpperCAmelCase,			data_format=_UpperCAmelCase,			**_UpperCAmelCase						)






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Dict     =						do_resize if do_resize is not None else self.do_resize
														SCREAMING_SNAKE_CASE__       :      Tuple     =						resample if resample is not None else self.resample
														SCREAMING_SNAKE_CASE__       :      List[str]     =						do_rescale if do_rescale is not None else self.do_rescale
														SCREAMING_SNAKE_CASE__       :      Any     =						rescale_factor if rescale_factor is not None else self.rescale_factor
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						do_normalize if do_normalize is not None else self.do_normalize
														SCREAMING_SNAKE_CASE__       :      int     =						image_mean if image_mean is not None else self.image_mean
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						image_std if image_std is not None else self.image_std
														SCREAMING_SNAKE_CASE__       :      Tuple     =						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						size if size is not None else self.size
														SCREAMING_SNAKE_CASE__       :      List[str]     =						get_size_dict(_UpperCAmelCase,			default_to_square=_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Tuple     =						make_list_of_images(_UpperCAmelCase						)

														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."						)

														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_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:
																					SCREAMING_SNAKE_CASE__       :      Any     =						[convert_to_rgb(_UpperCAmelCase						) for image in images]

														# All transformations expect numpy arrays.
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[to_numpy_array(_UpperCAmelCase						) for image in images]

														if do_resize:
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						[self.resize(image=_UpperCAmelCase,			size=_UpperCAmelCase,			resample=_UpperCAmelCase						) for image in images]

														if do_rescale:
																					SCREAMING_SNAKE_CASE__       :      str     =						[self.rescale(image=_UpperCAmelCase,			scale=_UpperCAmelCase						) for image in images]

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

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						[to_channel_dimension_format(_UpperCAmelCase,			_UpperCAmelCase						) for image in images]

														SCREAMING_SNAKE_CASE__       :      List[str]     =						BatchFeature(data={"pixel_values": images},			tensor_type=_UpperCAmelCase						)

														return encoded_outputs













def _a (  SCREAMING_SNAKE_CASE__      :  int = 50_00_00_00       )	->    int:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      str     =						set()
							SCREAMING_SNAKE_CASE__       :      Dict     =						int((limit - 24) ** (1 / 2)       )

							SCREAMING_SNAKE_CASE__       :      Any     =						set(range(3			,				prime_square_limit + 1			,				2       )       )
							primes.add(2       )
							for p in range(3			,				prime_square_limit + 1			,				2       ):
														if p not in primes:
																					continue
														primes.difference_update(set(range(p * p			,				prime_square_limit + 1			,				SCREAMING_SNAKE_CASE__       )       )       )

							for primea in primes:
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						primea * primea
														for primea in primes:
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						primea * primea * primea
																					if square + cube >= limit - 16:
																												break
																					for primea in primes:
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						primea * primea * primea * primea
																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						square + cube + tetr
																												if total >= limit:
																																			break
																												ret.add(SCREAMING_SNAKE_CASE__       )

							return len(SCREAMING_SNAKE_CASE__       )


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






