

# 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__		(	A__	):






  """simple docstring"""


  a									= (
      "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									= "CIDAS/clipseg-rd64-refined"
  a									= "image_segmenter"
  a									= CLIPSegForImageSegmentation

  a									= ["image", "text"]
  a									= ["image"]






  def __init__(							self						:		Union[str, Any]     ,       *__lowerCamelCase						:		List[str]     ,       **__lowerCamelCase						:		Optional[int] )		->							Optional[int]:
       requires_backends(self     ,       ['''vision'''] )
       super().__init__(*__lowerCamelCase     ,       **__lowerCamelCase )






  def 				lowercase_		(							self						:		List[Any]     ,       __lowerCamelCase						:		"Image"     ,       __lowerCamelCase						:		str )		->							List[Any]:
       return self.pre_processor(text=[label]     ,       images=[image]     ,       padding=__lowerCamelCase     ,       return_tensors='''pt''' )






  def 				lowercase_		(							self						:		Dict     ,       __lowerCamelCase						:		Any )		->							Optional[Any]:
       with torch.no_grad():
            SCREAMING_SNAKE_CASE__   =   self.model(**__lowerCamelCase ).logits
       return logits







  def 				lowercase_		(							self						:		List[Any]     ,       __lowerCamelCase						:		Optional[int] )		->							Tuple:
       SCREAMING_SNAKE_CASE__   =   outputs.cpu().detach().numpy()
       SCREAMING_SNAKE_CASE__   =   0
       SCREAMING_SNAKE_CASE__   =   1
       return Image.fromarray((array * 255).astype(np.uinta ) )





from argparse import ArgumentParser

from .env import EnvironmentCommand
def     UpperCAmelCase_       (						):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   ArgumentParser('''Diffusers CLI tool'''				,		usage='''diffusers-cli <command> [<args>]'''    )
     SCREAMING_SNAKE_CASE__   =   parser.add_subparsers(help='''diffusers-cli command helpers'''    )

     # Register commands
     EnvironmentCommand.register_subcommand(_A    )

     # Let's go
     SCREAMING_SNAKE_CASE__   =   parser.parse_args()

     if not hasattr(_A				,		'''func'''    ):
          parser.print_help()
          exit(1    )

     # Run
     SCREAMING_SNAKE_CASE__   =   args.func(_A    )
     service.run()


if __name__ == "__main__":
       main()


