


# 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        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Any						=		(
				    '''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.'''
				)
				SCREAMING_SNAKE_CASE__				:			Dict						=		'''CIDAS/clipseg-rd64-refined'''
				SCREAMING_SNAKE_CASE__				:			Optional[int]						=		'''image_segmenter'''
				SCREAMING_SNAKE_CASE__				:			Tuple						=		CLIPSegForImageSegmentation

				SCREAMING_SNAKE_CASE__				:			List[Any]						=		['''image''', '''text''']
				SCREAMING_SNAKE_CASE__				:			str						=		['''image''']


				def __init__(       self			:Dict			,			*lowerCAmelCase__			:str			,			**lowerCAmelCase__			:str	)		->			Tuple:
										requires_backends(self			,			['''vision''']	)
										super().__init__(*lowerCAmelCase__			,			**lowerCAmelCase__	)


				def 		__magic_name__(       self			:Union[str, Any]			,			lowerCAmelCase__			:"Image"			,			lowerCAmelCase__			:str	)		->			Dict:
										return self.pre_processor(text=[label]			,			images=[image]			,			padding=lowerCAmelCase__			,			return_tensors='''pt'''	)


				def 		__magic_name__(       self			:List[Any]			,			lowerCAmelCase__			:Tuple	)		->			Optional[int]:
										with torch.no_grad():
																__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.model(**lowerCAmelCase__	).logits
										return logits



				def 		__magic_name__(       self			:Optional[Any]			,			lowerCAmelCase__			:Optional[int]	)		->			Any:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		outputs.cpu().detach().numpy()
										__SCREAMING_SNAKE_CASE						:     Any					=		0
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		1
										return Image.fromarray((array * 255).astype(np.uinta	)	)




from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

__lowerCAmelCase	: List[Any]  				=datasets.load_iris()

__lowerCAmelCase	: Tuple  				=np.array(data['data'])
__lowerCAmelCase	: Dict  				=np.array(data['target'])
__lowerCAmelCase	: List[str]  				=data['target_names']

__lowerCAmelCase				,__lowerCAmelCase				,__lowerCAmelCase				,__lowerCAmelCase	: str  				=train_test_split(X, y)


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						return np.linalg.norm(np.array(lowercase__      ) - np.array(lowercase__      )      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__=5      ):
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		zip(lowercase__    ,       lowercase__      )
						# List of distances of all points from the point to be classified
						__SCREAMING_SNAKE_CASE						:     Dict					=		[]
						for data_point in data:
												__SCREAMING_SNAKE_CASE						:     Tuple					=		euclidean_distance(data_point[0]    ,       lowercase__      )
												distances.append((distance, data_point[1])      )
						# Choosing 'k' points with the least distances.
						__SCREAMING_SNAKE_CASE						:     int					=		[i[1] for i in sorted(lowercase__      )[:k]]
						# Most commonly occurring class among them
						# is the class into which the point is classified
						__SCREAMING_SNAKE_CASE						:     Any					=		Counter(lowercase__      ).most_common(1      )[0][0]
						return classes[result]


if __name__ == "__main__":
						print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
