


"""simple docstring"""







from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
		import PIL

if is_torch_available():
		import torch


a__				:							List[str]						=     logging.get_logger(__name__)




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	snake_case__				:  Union[str, Any]    					=			["pixel_values"]




	def __init__( self			:				Tuple			,			UpperCAmelCase__			:				bool = True			,			UpperCAmelCase__			:				Dict[str, int] = None			,			UpperCAmelCase__			:				PILImageResampling = PILImageResampling.BILINEAR			,			UpperCAmelCase__			:				bool = True			,			UpperCAmelCase__			:				Union[int, float] = 1 / 2_5_5			,			UpperCAmelCase__			:				bool = True			,			UpperCAmelCase__			:				Dict[str, int] = None			,			UpperCAmelCase__			:				bool = True			,			**UpperCAmelCase__			:				int			,			)       ->	None:
						super().__init__(**UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				size if size is not None else {"shortest_edge": 2_2_4}
						__SCREAMING_SNAKE_CASE						   =				get_size_dict(UpperCAmelCase__			,			default_to_square=UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				crop_size if crop_size is not None else {"height": 2_5_6, "width": 2_5_6}
						__SCREAMING_SNAKE_CASE						   =				get_size_dict(UpperCAmelCase__			,			param_name="crop_size"   )

						__SCREAMING_SNAKE_CASE						   =				do_resize
						__SCREAMING_SNAKE_CASE						   =				size
						__SCREAMING_SNAKE_CASE						   =				resample
						__SCREAMING_SNAKE_CASE						   =				do_rescale
						__SCREAMING_SNAKE_CASE						   =				rescale_factor
						__SCREAMING_SNAKE_CASE						   =				do_center_crop
						__SCREAMING_SNAKE_CASE						   =				crop_size
						__SCREAMING_SNAKE_CASE						   =				do_flip_channel_order




	def   UpperCAmelCase_      ( self			:				Optional[int]			,			UpperCAmelCase__			:				np.ndarray			,			UpperCAmelCase__			:				Dict[str, int]			,			UpperCAmelCase__			:				PILImageResampling = PIL.Image.BILINEAR			,			UpperCAmelCase__			:				Optional[Union[str, ChannelDimension]] = None			,			**UpperCAmelCase__			:				str			,			)       ->	np.ndarray:
						__SCREAMING_SNAKE_CASE						   =				get_size_dict(UpperCAmelCase__			,			default_to_square=UpperCAmelCase__   )
						if "shortest_edge" not in size:
											raise ValueError(F"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""   )
						__SCREAMING_SNAKE_CASE						   =				get_resize_output_image_size(UpperCAmelCase__			,			size=size["shortest_edge"]			,			default_to_square=UpperCAmelCase__   )
						return resize(UpperCAmelCase__			,			size=UpperCAmelCase__			,			resample=UpperCAmelCase__			,			data_format=UpperCAmelCase__			,			**UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				List[str]			,			UpperCAmelCase__			:				np.ndarray			,			UpperCAmelCase__			:				Dict[str, int]			,			UpperCAmelCase__			:				Optional[Union[str, ChannelDimension]] = None			,			**UpperCAmelCase__			:				Tuple			,			)       ->	np.ndarray:
						__SCREAMING_SNAKE_CASE						   =				get_size_dict(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()}"""   )
						return center_crop(UpperCAmelCase__			,			size=(size["height"], size["width"])			,			data_format=UpperCAmelCase__			,			**UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Any			,			UpperCAmelCase__			:				np.ndarray			,			UpperCAmelCase__			:				Union[int, float]			,			UpperCAmelCase__			:				Optional[Union[str, ChannelDimension]] = None			,			**UpperCAmelCase__			:				Optional[int]			,			)       ->	Dict:
						return rescale(UpperCAmelCase__			,			scale=UpperCAmelCase__			,			data_format=UpperCAmelCase__			,			**UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Optional[int]			,			UpperCAmelCase__			:				np.ndarray			,			UpperCAmelCase__			:				Optional[Union[str, ChannelDimension]] = None   )       ->	np.ndarray:
						return flip_channel_order(UpperCAmelCase__			,			data_format=UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				str			,			UpperCAmelCase__			:				ImageInput			,			UpperCAmelCase__			:				bool = None			,			UpperCAmelCase__			:				Dict[str, int] = None			,			UpperCAmelCase__			:				PILImageResampling = None			,			UpperCAmelCase__			:				bool = None			,			UpperCAmelCase__			:				float = None			,			UpperCAmelCase__			:				bool = None			,			UpperCAmelCase__			:				Dict[str, int] = None			,			UpperCAmelCase__			:				bool = None			,			UpperCAmelCase__			:				Optional[Union[str, TensorType]] = None			,			UpperCAmelCase__			:				ChannelDimension = ChannelDimension.FIRST			,			**UpperCAmelCase__			:				Union[str, Any]			,			)       ->	PIL.Image.Image:
						__SCREAMING_SNAKE_CASE						   =				do_resize if do_resize is not None else self.do_resize
						__SCREAMING_SNAKE_CASE						   =				resample if resample is not None else self.resample
						__SCREAMING_SNAKE_CASE						   =				do_rescale if do_rescale is not None else self.do_rescale
						__SCREAMING_SNAKE_CASE						   =				rescale_factor if rescale_factor is not None else self.rescale_factor
						__SCREAMING_SNAKE_CASE						   =				do_center_crop if do_center_crop is not None else self.do_center_crop
						__SCREAMING_SNAKE_CASE						   =				(
						    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
						)

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

						__SCREAMING_SNAKE_CASE						   =				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:
											raise ValueError("Size 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_center_crop and crop_size is None:
											raise ValueError("Crop size must be specified if do_center_crop is True."   )

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

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

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

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

						# the pretrained checkpoints assume images are BGR, not RGB
						if do_flip_channel_order:
											__SCREAMING_SNAKE_CASE						   =				[self.flip_channel_order(image=UpperCAmelCase__   ) for image in images]

						__SCREAMING_SNAKE_CASE						   =				[to_channel_dimension_format(UpperCAmelCase__			,			UpperCAmelCase__   ) for image in images]

						__SCREAMING_SNAKE_CASE						   =				{"pixel_values": images}
						return BatchFeature(data=UpperCAmelCase__			,			tensor_type=UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				str			,			UpperCAmelCase__			:				Optional[int]			,			UpperCAmelCase__			:				List[Tuple] = None   )       ->	List[str]:
						__SCREAMING_SNAKE_CASE						   =				outputs.logits

						# Resize logits and compute semantic segmentation maps
						if target_sizes is not None:
											if len(UpperCAmelCase__   ) != len(UpperCAmelCase__   ):
																raise ValueError(
																    "Make sure that you pass in as many target sizes as the batch dimension of the logits"   )

											if is_torch_tensor(UpperCAmelCase__   ):
																__SCREAMING_SNAKE_CASE						   =				target_sizes.numpy()

											__SCREAMING_SNAKE_CASE						   =				[]

											for idx in range(len(UpperCAmelCase__   )   ):
																__SCREAMING_SNAKE_CASE						   =				torch.nn.functional.interpolate(
																    logits[idx].unsqueeze(dim=0   )			,			size=target_sizes[idx]			,			mode="bilinear"			,			align_corners=UpperCAmelCase__   )
																__SCREAMING_SNAKE_CASE						   =				resized_logits[0].argmax(dim=0   )
																semantic_segmentation.append(UpperCAmelCase__   )
						else:
											__SCREAMING_SNAKE_CASE						   =				logits.argmax(dim=1   )
											__SCREAMING_SNAKE_CASE						   =				[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]   )]

						return semantic_segmentation




"""simple docstring"""







def    UpperCAmelCase__       (lowerCAmelCase_ = 100_0000   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				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_   )   )   )

					__SCREAMING_SNAKE_CASE						   =				[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() = }")
