"""simple docstring"""




def 							_UpperCAmelCase		(       lowerCamelCase__			,   lowerCamelCase__     ):





		"""simple docstring"""




		def get_matched_characters(lowerCamelCase__			,   lowerCamelCase__     ) -> str:
				lowerCAmelCase__        =   []
				lowerCAmelCase__        =   min(len(_stra     )			,   len(_stra     )     ) // 2
				for i, l in enumerate(_stra     ):
						lowerCAmelCase__        =   int(max(0			,   i - limit     )     )
						lowerCAmelCase__        =   int(min(i + limit + 1			,   len(_stra     )     )     )
						if l in _stra[left:right]:
								matched.append(lowerCamelCase__     )
								lowerCAmelCase__        =   f"""{_stra[0:_stra.index(lowerCamelCase__     )]} {_stra[_stra.index(lowerCamelCase__     ) + 1:]}"""

				return "".join(lowerCamelCase__     )

		# matching characters
		lowerCAmelCase__        =   get_matched_characters(lowerCamelCase__			,   lowerCamelCase__     )
		lowerCAmelCase__        =   get_matched_characters(lowerCamelCase__			,   lowerCamelCase__     )
		lowerCAmelCase__        =   len(lowerCamelCase__     )

		# transposition
		lowerCAmelCase__        =   (
		    len([(ca, ca) for ca, ca in zip(lowerCamelCase__			,   lowerCamelCase__     ) if ca != ca]     ) // 2
		)

		if not match_count:
				lowerCAmelCase__        =   0.0
		else:
				lowerCAmelCase__        =   (
				    1
				    / 3
				    * (
				        match_count / len(lowerCamelCase__     )
				        + match_count / len(lowerCamelCase__     )
				        + (match_count - transpositions) / match_count
				    )
				)

		# common prefix up to 4 characters
		lowerCAmelCase__        =   0
		for ca, ca in zip(stra[:4]			,   stra[:4]     ):
				if ca == ca:
						prefix_len += 1
				else:
						break

		return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			print(jaro_winkler("hello", "world"))





"""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       a_      (     __UpperCamelCase			):
					UpperCamelCase_ :	List[str]			=    (
					    "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."
					)
					UpperCamelCase_ :	str			=    "CIDAS/clipseg-rd64-refined"
					UpperCamelCase_ :	Any			=    "image_segmenter"
					UpperCamelCase_ :	Optional[Any]			=    CLIPSegForImageSegmentation

					UpperCamelCase_ :	List[str]			=    ["image", "text"]
					UpperCamelCase_ :	int			=    ["image"]




					def __init__(	self						:     Tuple  ,						*snake_case__						:     str  ,						**snake_case__						:     Optional[Any]     ):
							requires_backends(self  ,						["""vision"""]     )
							super().__init__(*snake_case__  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     "Image"  ,						snake_case__						:     str     ):
							return self.pre_processor(text=[label]  ,						images=[image]  ,						padding=snake_case__  ,						return_tensors="""pt"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     Tuple     ):
							with torch.no_grad():
									lowerCAmelCase__        =   self.model(**snake_case__     ).logits
							return logits




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     List[Any]     ):
							lowerCAmelCase__        =   outputs.cpu().detach().numpy()
							lowerCAmelCase__        =   0
							lowerCAmelCase__        =   1
							return Image.fromarray((array * 255).astype(np.uinta     )     )




