






from __future__ import annotations

import math
import random
from typing import Any

class 		_snake_case  :






			def __init__(       self)      ->       None:
						SCREAMING_SNAKE_CASE						= []
						SCREAMING_SNAKE_CASE						= 0
						SCREAMING_SNAKE_CASE						= 0






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       bool:
						return self.head == self.tail






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       None:
						self.data.append(a)
						SCREAMING_SNAKE_CASE						= self.tail + 1






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						SCREAMING_SNAKE_CASE						= self.data[self.head]
						SCREAMING_SNAKE_CASE						= self.head + 1
						return ret






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						return self.tail - self.head







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       None:
						print(self.data)
						print('**************')
						print(self.data[self.head : self.tail])



class 		_snake_case  :






			def __init__(       self		,					a)      ->       None:
						SCREAMING_SNAKE_CASE						= data
						SCREAMING_SNAKE_CASE						= None
						SCREAMING_SNAKE_CASE						= None
						SCREAMING_SNAKE_CASE						= 1






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						return self.data






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       MyNode | None:
						return self.left






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       MyNode | None:
						return self.right






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						return self.height






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       None:
						SCREAMING_SNAKE_CASE						= data






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       None:
						SCREAMING_SNAKE_CASE						= node






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       None:
						SCREAMING_SNAKE_CASE						= node







			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       None:
						SCREAMING_SNAKE_CASE						= height






def 				lowerCamelCase__				(_UpperCAmelCase):
			if node is None:
						return 0
			return node.get_height()






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			if a > b:
						return a
			return b






def 				lowerCamelCase__				(_UpperCAmelCase):
			print('left rotation node:'							,       node.get_data())
			SCREAMING_SNAKE_CASE						= node.get_left()
			assert ret is not None
			node.set_left(ret.get_right())
			ret.set_right(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= my_max(get_height(node.get_right())							,       get_height(node.get_left())) + 1
			node.set_height(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= my_max(get_height(ret.get_right())							,       get_height(ret.get_left())) + 1
			ret.set_height(_UpperCAmelCase)
			return ret






def 				lowerCamelCase__				(_UpperCAmelCase):
			print('right rotation node:'							,       node.get_data())
			SCREAMING_SNAKE_CASE						= node.get_right()
			assert ret is not None
			node.set_right(ret.get_left())
			ret.set_left(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= my_max(get_height(node.get_right())							,       get_height(node.get_left())) + 1
			node.set_height(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= my_max(get_height(ret.get_right())							,       get_height(ret.get_left())) + 1
			ret.set_height(_UpperCAmelCase)
			return ret






def 				lowerCamelCase__				(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= node.get_left()
			assert left_child is not None
			node.set_left(left_rotation(_UpperCAmelCase))
			return right_rotation(_UpperCAmelCase)






def 				lowerCamelCase__				(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= node.get_right()
			assert right_child is not None
			node.set_right(right_rotation(_UpperCAmelCase))
			return left_rotation(_UpperCAmelCase)






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			if node is None:
						return MyNode(_UpperCAmelCase)
			if data < node.get_data():
						node.set_left(insert_node(node.get_left()							,       _UpperCAmelCase))
						if (
						    get_height(node.get_left()) - get_height(node.get_right()) == 2
						):  # an unbalance detected
									SCREAMING_SNAKE_CASE						= node.get_left()
									assert left_child is not None
									if (
									    data < left_child.get_data()
									):  # new node is the left child of the left child
												SCREAMING_SNAKE_CASE						= right_rotation(_UpperCAmelCase)
									else:
												SCREAMING_SNAKE_CASE						= lr_rotation(_UpperCAmelCase)
			else:
						node.set_right(insert_node(node.get_right()							,       _UpperCAmelCase))
						if get_height(node.get_right()) - get_height(node.get_left()) == 2:
									SCREAMING_SNAKE_CASE						= node.get_right()
									assert right_child is not None
									if data < right_child.get_data():
												SCREAMING_SNAKE_CASE						= rl_rotation(_UpperCAmelCase)
									else:
												SCREAMING_SNAKE_CASE						= left_rotation(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= my_max(get_height(node.get_right())							,       get_height(node.get_left())) + 1
			node.set_height(_UpperCAmelCase)
			return node






def 				lowerCamelCase__				(_UpperCAmelCase):
			while True:
						SCREAMING_SNAKE_CASE						= root.get_right()
						if right_child is None:
									break
						SCREAMING_SNAKE_CASE						= right_child
			return root.get_data()






def 				lowerCamelCase__				(_UpperCAmelCase):
			while True:
						SCREAMING_SNAKE_CASE						= root.get_left()
						if left_child is None:
									break
						SCREAMING_SNAKE_CASE						= left_child
			return root.get_data()






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= root.get_left()
			SCREAMING_SNAKE_CASE						= root.get_right()
			if root.get_data() == data:
						if left_child is not None and right_child is not None:
									SCREAMING_SNAKE_CASE						= get_left_most(_UpperCAmelCase)
									root.set_data(_UpperCAmelCase)
									root.set_right(del_node(_UpperCAmelCase							,       _UpperCAmelCase))
						elif left_child is not None:
									SCREAMING_SNAKE_CASE						= left_child
						elif right_child is not None:
									SCREAMING_SNAKE_CASE						= right_child
						else:
									return None
			elif root.get_data() > data:
						if left_child is None:
									print('No such data')
									return root
						else:
									root.set_left(del_node(_UpperCAmelCase							,       _UpperCAmelCase))
			else:  # root.get_data() < data
						if right_child is None:
									return root
						else:
									root.set_right(del_node(_UpperCAmelCase							,       _UpperCAmelCase))

			if get_height(_UpperCAmelCase) - get_height(_UpperCAmelCase) == 2:
						assert right_child is not None
						if get_height(right_child.get_right()) > get_height(right_child.get_left()):
									SCREAMING_SNAKE_CASE						= left_rotation(_UpperCAmelCase)
						else:
									SCREAMING_SNAKE_CASE						= rl_rotation(_UpperCAmelCase)
			elif get_height(_UpperCAmelCase) - get_height(_UpperCAmelCase) == -2:
						assert left_child is not None
						if get_height(left_child.get_left()) > get_height(left_child.get_right()):
									SCREAMING_SNAKE_CASE						= right_rotation(_UpperCAmelCase)
						else:
									SCREAMING_SNAKE_CASE						= lr_rotation(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= my_max(get_height(root.get_right())							,       get_height(root.get_left())) + 1
			root.set_height(_UpperCAmelCase)
			return root

class 		_snake_case  :






			def __init__(       self)      ->       None:
						SCREAMING_SNAKE_CASE						= None






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						return get_height(self.root)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       None:
						print('insert:' + str(a))
						SCREAMING_SNAKE_CASE						= insert_node(self.root		,					a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       None:
						print('delete:' + str(a))
						if self.root is None:
									print('Tree is empty!')
									return
						SCREAMING_SNAKE_CASE						= del_node(self.root		,					a)







			def __str__(       self		,					)      ->       str:  # a level traversale, gives a more intuitive look on the tree
						SCREAMING_SNAKE_CASE						= ''
						SCREAMING_SNAKE_CASE						= MyQueue()
						q.push(self.root)
						SCREAMING_SNAKE_CASE						= self.get_height()
						if layer == 0:
									return output
						SCREAMING_SNAKE_CASE						= 0
						while not q.is_empty():
									SCREAMING_SNAKE_CASE						= q.pop()
									SCREAMING_SNAKE_CASE						= ' ' * int(math.pow(2		,					layer - 1))
									output += space
									if node is None:
												output += "*"
												q.push(a)
												q.push(a)
									else:
												output += str(node.get_data())
												q.push(node.get_left())
												q.push(node.get_right())
									output += space
									SCREAMING_SNAKE_CASE						= cnt + 1
									for i in range(100):
												if cnt == math.pow(2		,					a) - 1:
															SCREAMING_SNAKE_CASE						= layer - 1
															if layer == 0:
																		output += "\n*************************************"
																		return output
															output += "\n"
															break
						output += "\n*************************************"
						return output






def 				lowerCamelCase__				():
			import doctest

			doctest.testmod()


if __name__ == "__main__":
				_test()
				a_		:						Dict             =    AVLtree()
				a_		:						Tuple             =    list(range(10))
				random.shuffle(lst)
				for i in lst:
								t.insert(i)
								print(str(t))
				random.shuffle(lst)
				for i in lst:
								t.del_node(i)
								print(str(t))












from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
				from ..image_utils import load_image


if is_torch_available():
				import torch

				from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

a_		:						str             =    logging.get_logger(__name__)


a_		:						List[Any]             =    Dict[str, Any]
a_		:						Optional[int]             =    List[Prediction]

@add_end_docstrings(A__					)
class 		_snake_case  (  A__					):






			def __init__(       self		,					*a		,					**a)      ->       int:
						super().__init__(*a		,					**a)

						if self.framework == "tf":
									raise ValueError(f'''The {self.__class__} is only available in PyTorch.''')

						requires_backends(self		,					'vision')
						self.check_model_type(
						    dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()))






			def    SCREAMING_SNAKE_CASE__    (       self		,					**a)      ->       int:
						SCREAMING_SNAKE_CASE						= {}
						if "threshold" in kwargs:
									SCREAMING_SNAKE_CASE						= kwargs['threshold']
						return {}, {}, postprocess_kwargs






			def __call__(       self		,					*a		,					**a)      ->       Union[Predictions, List[Prediction]]:
						return super().__call__(*a		,					**a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= load_image(a)
						SCREAMING_SNAKE_CASE						= torch.IntTensor([[image.height, image.width]])
						SCREAMING_SNAKE_CASE						= self.image_processor(images=[image]		,					return_tensors='pt')
						if self.tokenizer is not None:
									SCREAMING_SNAKE_CASE						= self.tokenizer(text=inputs['words']		,					boxes=inputs['boxes']		,					return_tensors='pt')
						SCREAMING_SNAKE_CASE						= target_size
						return inputs






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       int:
						SCREAMING_SNAKE_CASE						= model_inputs.pop('target_size')
						SCREAMING_SNAKE_CASE						= self.model(**a)
						SCREAMING_SNAKE_CASE						= outputs.__class__({'target_size': target_size, **outputs})
						if self.tokenizer is not None:
									SCREAMING_SNAKE_CASE						= model_inputs['bbox']
						return model_outputs






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a=0.9)      ->       Dict:
						SCREAMING_SNAKE_CASE						= model_outputs['target_size']
						if self.tokenizer is not None:
									# This is a LayoutLMForTokenClassification variant.
									# The OCR got the boxes and the model classified the words.
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= target_size[0].tolist()

									def unnormalize(a):
												return self._get_bounding_box(
												    torch.Tensor(
												        [
												            (width * bbox[0] / 1000),
												            (height * bbox[1] / 1000),
												            (width * bbox[2] / 1000),
												            (height * bbox[3] / 1000),
												        ]))

									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= model_outputs['logits'].squeeze(0).softmax(dim=-1).max(dim=-1)
									SCREAMING_SNAKE_CASE						= [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
									SCREAMING_SNAKE_CASE						= [unnormalize(a) for bbox in model_outputs['bbox'].squeeze(0)]
									SCREAMING_SNAKE_CASE						= ['score', 'label', 'box']
									SCREAMING_SNAKE_CASE						= [dict(zip(a		,					a)) for vals in zip(scores.tolist()		,					a		,					a) if vals[0] > threshold]
						else:
									# This is a regular ForObjectDetectionModel
									SCREAMING_SNAKE_CASE						= self.image_processor.post_process_object_detection(a		,					a		,					a)
									SCREAMING_SNAKE_CASE						= raw_annotations[0]
									SCREAMING_SNAKE_CASE						= raw_annotation['scores']
									SCREAMING_SNAKE_CASE						= raw_annotation['labels']
									SCREAMING_SNAKE_CASE						= raw_annotation['boxes']

									SCREAMING_SNAKE_CASE						= scores.tolist()
									SCREAMING_SNAKE_CASE						= [self.model.config.idalabel[label.item()] for label in labels]
									SCREAMING_SNAKE_CASE						= [self._get_bounding_box(a) for box in boxes]

									# {"scores": [...], ...} --> [{"score":x, ...}, ...]
									SCREAMING_SNAKE_CASE						= ['score', 'label', 'box']
									SCREAMING_SNAKE_CASE						= [
									    dict(zip(a		,					a))
									    for vals in zip(raw_annotation['scores']		,					raw_annotation['labels']		,					raw_annotation['boxes'])
									]

						return annotation







			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Dict[str, int]:
						if self.framework != "pt":
									raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.')
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= box.int().tolist()
						SCREAMING_SNAKE_CASE						= {
						    'xmin': xmin,
						    'ymin': ymin,
						    'xmax': xmax,
						    'ymax': ymax,
						}
						return bbox




