



'''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 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    Tuple   				=					(
							    '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.'
							)
							lowerCAmelCase       :    List[str]   				=					'CIDAS/clipseg-rd64-refined'
							lowerCAmelCase       :    Optional[int]   				=					'image_segmenter'
							lowerCAmelCase       :    Union[str, Any]   				=					CLIPSegForImageSegmentation

							lowerCAmelCase       :    str   				=					['image', 'text']
							lowerCAmelCase       :    int   				=					['image']







							def __init__(							self					:       Tuple	,*_UpperCAmelCase					:       Optional[int]	,**_UpperCAmelCase					:       Any		):
									requires_backends(self	,['vision']		)
									super().__init__(*_UpperCAmelCase	,**_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       "Image"	,_UpperCAmelCase					:       str		):
									return self.pre_processor(text=[label]	,images=[image]	,padding=_UpperCAmelCase	,return_tensors='pt'		)







							def 							__lowercase			(							self					:       Optional[int]	,_UpperCAmelCase					:       Optional[Any]		):
									with torch.no_grad():
											_a     :      int        =							self.model(**_UpperCAmelCase		).logits
									return logits







							def 							__lowercase			(							self					:       List[Any]	,_UpperCAmelCase					:       Optional[int]		):
									_a     :      Dict        =							outputs.cpu().detach().numpy()
									_a     :      List[str]        =							0
									_a     :      int        =							1
									return Image.fromarray((array * 255).astype(np.uinta		)		)








'''simple docstring'''







from math import isqrt


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	int  )  ->			bool:
       return all(number % divisor != 0 for divisor in range(2    ,isqrt(_UpperCAmelCase  ) + 1  )  )


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	int = 10**6  )  ->			int:
       _a    : List[Any]     					=0
       _a    : str     					=1
       _a    : Optional[Any]     					=7
       while prime_candidate < max_prime:
              primes_count += is_prime(_UpperCAmelCase  )

              cube_index += 1
              prime_candidate += 6 * cube_index

       return primes_count


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


