"""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 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Union[str, Any]		 =		(
         "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."
     )
     _UpperCamelCase							:      Optional[int]		 =		"CIDAS/clipseg-rd64-refined"
     _UpperCamelCase							:      Any		 =		"image_segmenter"
     _UpperCamelCase							:      List[str]		 =		CLIPSegForImageSegmentation

     _UpperCamelCase							:      Any		 =		["image", "text"]
     _UpperCamelCase							:      Tuple		 =		["image"]






     def __init__(		self    ,					*a__    ,					**a__  ):
       requires_backends(self    ,					["""vision"""]  )
       super().__init__(*a__    ,					**a__  )






     def    __A  (		self    ,					a__    ,					a__  ):
       return self.pre_processor(text=[label]    ,					images=[image]    ,					padding=a__    ,					return_tensors="""pt"""  )






     def    __A  (		self    ,					a__  ):
       with torch.no_grad():
         _lowerCAmelCase						:	int        =					self.model(**a__  ).logits
       return logits






     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	int        =					outputs.cpu().detach().numpy()
       _lowerCAmelCase						:	str        =					0
       _lowerCAmelCase						:	Optional[Any]        =					1
       return Image.fromarray((array * 255).astype(np.uinta  )  )





"""simple docstring"""




def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str      ,_lowerCamelCase						:	bool = False     )	->				str:

  if not isinstance(_lowerCamelCase      ,_lowerCamelCase     ):
    _lowerCAmelCase						:	Union[str, Any]        =					f"Expected string as input, found {type(_lowerCamelCase     )}"
    raise ValueError(_lowerCamelCase     )
  if not isinstance(_lowerCamelCase      ,_lowerCamelCase     ):
    _lowerCAmelCase						:	Union[str, Any]        =					f"Expected boolean as use_pascal parameter, found {type(_lowerCamelCase     )}"
    raise ValueError(_lowerCamelCase     )

  _lowerCAmelCase						:	str        =					input_str.split("""_"""     )

  _lowerCAmelCase						:	Optional[int]        =					0 if use_pascal else 1

  _lowerCAmelCase						:	List[Any]        =					words[start_index:]

  _lowerCAmelCase						:	Union[str, Any]        =					[word[0].upper() + word[1:] for word in words_to_capitalize]

  _lowerCAmelCase						:	List[Any]        =					"""""" if use_pascal else words[0]

  return "".join([initial_word, *capitalized_words]     )


if __name__ == "__main__":
     from doctest import testmod

     testmod()




