# 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 __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							(
       "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_														=							"CIDAS/clipseg-rd64-refined"
   lowerCAmelCase_														=							"image_segmenter"
   lowerCAmelCase_														=							CLIPSegForImageSegmentation

   lowerCAmelCase_														=							["image", "text"]
   lowerCAmelCase_														=							["image"]




   def __init__(					self    :       List[Any] ,			*_lowercase    :       Dict ,			**_lowercase    :       Any   ):



     """simple docstring"""






     requires_backends(self ,			["""vision"""]   )
     super().__init__(*_lowercase ,			**_lowercase   )




   def 			__a		(					self    :       Dict ,			_lowercase    :       "Image" ,			_lowercase    :       str   ):



     """simple docstring"""






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




   def 			__a		(					self    :       Optional[int] ,			_lowercase    :       Union[str, Any]   ):



     """simple docstring"""






     with torch.no_grad():
       SCREAMING_SNAKE_CASE__    							=   self.model(**_lowercase   ).logits
     return logits




   def 			__a		(					self    :       Tuple ,			_lowercase    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   outputs.cpu().detach().numpy()
     SCREAMING_SNAKE_CASE__    							=   0
     SCREAMING_SNAKE_CASE__    							=   1
     return Image.fromarray((array * 2_55).astype(np.uinta   )   )

import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu







@require_cpu
class __snake_case       (		unittest.TestCase	):




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   torch.nn.Linear(10 ,			10   )
     SCREAMING_SNAKE_CASE__    							=   torch.optim.SGD(model.parameters() ,			0.1   )
     SCREAMING_SNAKE_CASE__    							=   Accelerator()
     SCREAMING_SNAKE_CASE__    							=   accelerator.prepare(_lowercase   )
     try:
       pickle.loads(pickle.dumps(_lowercase   )   )
     except Exception as e:
       self.fail(f"""Accelerated optimizer pickling failed with {e}"""   )
     AcceleratorState._reset_state()
