
# 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__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_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				,       *UpperCamelCase__				,       **UpperCamelCase__		):
					requires_backends(self				,       ['''vision''']		)
					super().__init__(*UpperCamelCase__				,       **UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__		):
					return self.pre_processor(text=[label]				,       images=[image]				,       padding=UpperCamelCase__				,       return_tensors='''pt'''		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					with torch.no_grad():
									A__  :	Dict         =							self.model(**UpperCamelCase__		).logits
					return logits





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	Union[str, Any]         =							outputs.cpu().detach().numpy()
					A__  :	Dict         =							0
					A__  :	Dict         =							1
					return Image.fromarray((array * 255).astype(np.uinta		)		)

from numpy import exp, pi, sqrt

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Dict							,						__UpperCamelCase :						float = 0.0							,						__UpperCamelCase :						float = 1.0       ) ->       int:

				"""simple docstring"""







				return 1 / sqrt(2 * pi * sigma**2       ) * exp(-((x - mu) ** 2) / (2 * sigma**2)       )


if __name__ == "__main__":
					import doctest

					doctest.testmod()