





"""simple docstring"""






from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any




@dataclass
class 			__lowercase     :







					'''simple docstring'''




					__lowerCAmelCase					=					42
					__lowerCAmelCase					=					None
					__lowerCAmelCase					=					None






def       __A (     )						->						Node | None:
				__a	:  Union[str, Any]         =					Node(1)
				__a	:  Optional[Any]         =					Node(2)
				__a	:  Tuple         =					Node(3)
				__a	:  Union[str, Any]         =					Node(4)
				__a	:  int         =					Node(5)
				return tree

def       __A (     a_  :Node | None)						->						list[int]:
				return [root.data, *preorder(root.left), *preorder(root.right)] if root else []

def       __A (     a_  :Node | None)						->						list[int]:
				return postorder(root.left) + postorder(root.right) + [root.data] if root else []

def       __A (     a_  :Node | None)						->						list[int]:
				return [*inorder(root.left), root.data, *inorder(root.right)] if root else []

def       __A (     a_  :Node | None)						->						int:
				return (max(height(root.left)				,    height(root.right)) + 1) if root else 0

def       __A (     a_  :Node | None)						->						Sequence[Node | None]:
				__a	:  list[Any]         =					[]

				if root is None:
								return output

				__a	:  Optional[Any]         =					deque([root])

				while process_queue:
								__a	:  Union[str, Any]         =					process_queue.popleft()
								output.append(node.data)

								if node.left:
												process_queue.append(node.left)
								if node.right:
												process_queue.append(node.right)
				return output

def       __A (     a_  :Node | None				,    a_  :int)						->						Sequence[Node | None]:
				__a	:  list[Any]         =					[]

				def populate_output(a_  :Node | None				,    a_  :int) -> None:
								if not root:
												return
								if level == 1:
												output.append(root.data)
								elif level > 1:
												populate_output(root.left				,    level - 1)
												populate_output(root.right				,    level - 1)

				populate_output(a_				,    a_)
				return output

def       __A (     a_  :Node | None				,    a_  :int)						->						Sequence[Node | None]:
				__a	:  list[Any]         =					[]

				def populate_output(a_  :Node | None				,    a_  :int) -> None:
								if root is None:
												return
								if level == 1:
												output.append(root.data)
								elif level > 1:
												populate_output(root.right				,    level - 1)
												populate_output(root.left				,    level - 1)

				populate_output(a_				,    a_)
				return output

def       __A (     a_  :Node | None)						->						Sequence[Node | None] | list[Any]:
				if root is None:
								return []

				__a	:  list[Sequence[Node | None]]         =					[]

				__a	:  Any         =					0
				__a	:  List[Any]         =					height(a_)

				for h in range(1				,    height_tree + 1):
								if not flag:
												output.append(get_nodes_from_left_to_right(a_				,    a_))
												__a	:  Union[str, Any]         =					1
								else:
												output.append(get_nodes_from_right_to_left(a_				,    a_))
												__a	:  Optional[int]         =					0

				return output

def       __A (     )						->						None:  # Main function for testing.
				__a	:  List[Any]         =					make_tree()
				print(F"""In-order Traversal: {inorder(a_)}""")
				print(F"""Pre-order Traversal: {preorder(a_)}""")
				print(F"""Post-order Traversal: {postorder(a_)}"""				,    '''\n''')

				print(F"""Height of Tree: {height(a_)}"""				,    '''\n''')

				print('''Complete Level Order Traversal: ''')
				print(level_order(a_)				,    '''\n''')

				print('''Level-wise order Traversal: ''')

				for level in range(1				,    height(a_) + 1):
								print(F"""Level {level}:"""				,    get_nodes_from_left_to_right(a_				,    level=a_))

				print('''\nZigZag order Traversal: ''')
				print(zigzag(a_))


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()






"""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 			__lowercase     (     _UpperCamelCase							):







					'''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 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									return self.pre_processor(text=[label]		,     images=[image]		,     padding=_UpperCAmelCase		,     return_tensors='''pt'''       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									with torch.no_grad():
													__a	:  List[str]         =					self.model(**_UpperCAmelCase       ).logits
									return logits







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									__a	:  str         =					outputs.cpu().detach().numpy()
									__a	:  int         =					0
									__a	:  Optional[int]         =					1
									return Image.fromarray((array * 255).astype(np.uinta       )       )