





'''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  lowerCAmelCase_		(		__magic_name__		):
 __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    :				int								=       "CIDAS/clipseg-rd64-refined"
 __lowerCamelCase    :				Union[str, Any]								=       "image_segmenter"
 __lowerCamelCase    :				Dict								=       CLIPSegForImageSegmentation

 __lowerCamelCase    :				Tuple								=       ["image", "text"]
 __lowerCamelCase    :				Optional[int]								=       ["image"]




 def __init__(						self      ,  *_lowerCAmelCase      ,  **_lowerCAmelCase		)     ->				Any:
     requires_backends(self      ,  ["vision"]		)
     super().__init__(*_lowerCAmelCase      ,  **_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				List[str]:
     return self.pre_processor(text=[label]      ,  images=[image]      ,  padding=_lowerCAmelCase      ,  return_tensors="pt"		)




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Dict:
     with torch.no_grad():
         _lowerCAmelCase						      =   self.model(**_lowerCAmelCase		).logits
     return logits





 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				int:
     _lowerCAmelCase						      =   outputs.cpu().detach().numpy()
     _lowerCAmelCase						      =   0
     _lowerCAmelCase						      =   1
     return Image.fromarray((array * 255).astype(np.uinta		)		)









'''simple docstring'''


from __future__ import annotations


def 						__a(SCREAMING_SNAKE_CASE_	:       int | float | str		,							SCREAMING_SNAKE_CASE_	:       int | float | str     ):



    '''simple docstring'''





    if nth_term == "":
        return [""]
    _lowerCAmelCase						      =   int(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   int(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   []
    for temp in range(int(SCREAMING_SNAKE_CASE_     )     ):
        series.append(F'''1 / {pow(temp + 1		,							int(SCREAMING_SNAKE_CASE_     )     )}''' if series else "1"     )
    return series


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       _SCREAMING_SNAKE_CASE									=				int(input("Enter the last number (nth term) of the P-Series"))
       _SCREAMING_SNAKE_CASE									=				int(input("Enter the power for  P-Series"))
       print("Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p")
       print(p_series(nth_term, power))


