
"""simple docstring"""





def 				lowerCamelCase__		(      __snake_case = 50_00_00_00     )							->		int:


					"""simple docstring"""





					_UpperCamelCase										=  set()
					_UpperCamelCase										=  int((limit - 24) ** (1 / 2)     )

					_UpperCamelCase										=  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,					__snake_case     )     )     )

					for primea in primes:
										_UpperCamelCase										=  primea * primea
										for primea in primes:
															_UpperCamelCase										=  primea * primea * primea
															if square + cube >= limit - 16:
																				break
															for primea in primes:
																				_UpperCamelCase										=  primea * primea * primea * primea
																				_UpperCamelCase										=  square + cube + tetr
																				if total >= limit:
																									break
																				ret.add(__snake_case     )

					return len(__snake_case     )


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








"""simple docstring"""





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
				from PIL import Image






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				(
		    'This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'
		    'It takes two arguments named `image` which should be the original image, and `label` which should be a text '
		    'describing the elements what should be identified in the segmentation mask. The tool returns the mask.'
		)
		lowercase__      =				'CIDAS/clipseg-rd64-refined'
		lowercase__      =				'image_segmenter'
		lowercase__      =				CLIPSegForImageSegmentation

		lowercase__      =				['image', 'text']
		lowercase__      =				['image']



		def __init__( self    ,		*__a    ,		**__a)	->	Any:






							'''simple docstring'''
							requires_backends(self    ,		['''vision'''])
							super().__init__(*__a    ,		**__a)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	Dict:






							'''simple docstring'''
							return self.pre_processor(text=[label]    ,		images=[image]    ,		padding=__a    ,		return_tensors='''pt''')



		def 						UpperCAmelCase							( self    ,		__a)	->	Tuple:






							'''simple docstring'''
							with torch.no_grad():
												_UpperCamelCase										=  self.model(**__a).logits
							return logits




		def 						UpperCAmelCase							( self    ,		__a)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  outputs.cpu().detach().numpy()
							_UpperCamelCase										=  0
							_UpperCamelCase										=  1
							return Image.fromarray((array * 2_55).astype(np.uinta))






