





# 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 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    int							=   (
          '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.'
      )
      A_						:    Union[str, Any]							=   'CIDAS/clipseg-rd64-refined'
      A_						:    Tuple							=   'image_segmenter'
      A_						:    int							=   CLIPSegForImageSegmentation

      A_						:    Any							=   ['image', 'text']
      A_						:    Any							=   ['image']


      def __init__(self	:				Tuple    ,      *a__	:				str    ,      **a__	:				Any				):





         """simple docstring"""



         requires_backends(self    ,      ['''vision''']				)
         super().__init__(*a__    ,      **a__				)


      def        a			(self	:				Optional[int]    ,      a__	:				"Image"    ,      a__	:				str				):





         """simple docstring"""



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


      def        a			(self	:				List[Any]    ,      a__	:				List[Any]				):





         """simple docstring"""



         with torch.no_grad():
            __snake_case								=       self.model(**a__				).logits
         return logits


      def        a			(self	:				str    ,      a__	:				Optional[int]				):





         """simple docstring"""



         __snake_case								=       outputs.cpu().detach().numpy()
         __snake_case								=       0
         __snake_case								=       1
         return Image.fromarray((array * 255).astype(np.uinta				)				)








from pathlib import Path

import fire






def lowerCamelCase__   (							snake_case_     :  str	,				snake_case_     :  str	,				snake_case_     :  int	)				->    str:
   __snake_case								=       Path(snake_case_	)
   __snake_case								=       Path(snake_case_	)
   dest_dir.mkdir(exist_ok=snake_case_	)
   for path in src_dir.iterdir():
      __snake_case								=       [x.rstrip() for x in list(path.open().readlines()	)][:n]
      __snake_case								=       dest_dir.joinpath(path.name	)
      print(snake_case_	)
      dest_path.open('''w'''	).write('''\n'''.join(snake_case_	)	)


if __name__ == "__main__":
    fire.Fire(minify)

