





'''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		)		)







from datetime import datetime

import matplotlib.pyplot as plt
import torch

def        __a			(   __lowerCAmelCase   )     ->     int:
					for param in module.parameters():
										SCREAMING_SNAKE_CASE							:     List[Any]						=				False

def        __a			(   )     ->     List[str]:
					SCREAMING_SNAKE_CASE							:     List[Any]						=				'cuda' if torch.cuda.is_available() else 'cpu'
					if torch.backends.mps.is_available() and torch.backends.mps.is_built():
										SCREAMING_SNAKE_CASE							:     List[str]						=				'mps'
					if device == "mps":
										print(
										    'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
										    ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
										    ' with generations.'   )
					return device

def        __a			(   __lowerCAmelCase   )     ->     List[Any]:
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				plt.imshow(__lowerCAmelCase   )
					fig.axes.get_xaxis().set_visible(__lowerCAmelCase   )
					fig.axes.get_yaxis().set_visible(__lowerCAmelCase   )
					plt.show()



def        __a			(   )     ->     Optional[Any]:
					SCREAMING_SNAKE_CASE							:     str						=				datetime.now()
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				current_time.strftime('%H:%M:%S'   )
					return timestamp