





# 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     (						a    ):
				__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   , *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():
												lowerCamelCase	 =self.model(**UpperCAmelCase_							).logits
								return logits


				def _snake_case       (		self   , UpperCAmelCase_							):
								lowerCamelCase	 =outputs.cpu().detach().numpy()
								lowerCamelCase	 =0
								lowerCamelCase	 =1
								return Image.fromarray((array * 255).astype(np.uinta							)							)












def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase				)						->  List[str]:
				lowerCamelCase	 =[0 for i in range(r + 1				)]
				# nc0 = 1
				lowerCamelCase	 =1
				for i in range(1			,      n + 1				):
								# to compute current row from previous row.
								lowerCamelCase	 =min(_UpperCAmelCase			,      _UpperCAmelCase				)
								while j > 0:
												c[j] += c[j - 1]
												j -= 1
				return c[r]




print(binomial_coefficient(n=10, r=5))





