



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, ChunkPipeline


if is_vision_available():
						from PIL import Image

						from ..image_utils import load_image

if is_torch_available():
						import torch

						from transformers.modeling_outputs import BaseModelOutput

						from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

lowerCAmelCase__							:      List[Any]      =  logging.get_logger(__name__)






@add_end_docstrings(_UpperCAmelCase     )
class      UpperCamelCase    (						_UpperCAmelCase     ):



	def __init__(       self			,	**UpperCAmelCase__		):
							super().__init__(**UpperCAmelCase__		)

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

							requires_backends(self			,	"vision"		)
							self.check_model_type(UpperCAmelCase__		)



	def __call__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = None			,	**UpperCAmelCase__			,	):
							if "text_queries" in kwargs:
													A__							=       kwargs.pop("text_queries"		)

							if isinstance(UpperCAmelCase__			,	(str, Image.Image)		):
													A__							=       {"image": image, "candidate_labels": candidate_labels}
							else:
													A__							=       image
							A__							=       super().__call__(UpperCAmelCase__			,	**UpperCAmelCase__		)
							return results



	def 			__A							(       self			,	**UpperCAmelCase__		):
							A__							=       {}
							if "threshold" in kwargs:
													A__							=       kwargs["threshold"]
							if "top_k" in kwargs:
													A__							=       kwargs["top_k"]
							return {}, {}, postprocess_params



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       load_image(inputs["image"]		)
							A__							=       inputs["candidate_labels"]
							if isinstance(UpperCAmelCase__			,	UpperCAmelCase__		):
													A__							=       candidate_labels.split(","		)

							A__							=       torch.tensor([[image.height, image.width]]			,	dtype=torch.intaa		)
							for i, candidate_label in enumerate(UpperCAmelCase__		):
													A__							=       self.tokenizer(UpperCAmelCase__			,	return_tensors=self.framework		)
													A__							=       self.image_processor(UpperCAmelCase__			,	return_tensors=self.framework		)
													yield {
													    "is_last": i == len(UpperCAmelCase__		) - 1,
													    "target_size": target_size,
													    "candidate_label": candidate_label,
													    **text_inputs,
													    **image_features,
													}



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       model_inputs.pop("target_size"		)
							A__							=       model_inputs.pop("candidate_label"		)
							A__							=       model_inputs.pop("is_last"		)

							A__							=       self.model(**UpperCAmelCase__		)

							A__							=       {"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
							return model_outputs



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__=0.1			,	UpperCAmelCase__=None		):
							A__							=       []
							for model_output in model_outputs:
													A__							=       model_output["candidate_label"]
													A__							=       BaseModelOutput(UpperCAmelCase__		)
													A__							=       self.image_processor.post_process_object_detection(
													    outputs=UpperCAmelCase__			,	threshold=UpperCAmelCase__			,	target_sizes=model_output["target_size"]		)[0]

													for index in outputs["scores"].nonzero():
																			A__							=       outputs["scores"][index].item()
																			A__							=       self._get_bounding_box(outputs["boxes"][index][0]		)

																			A__							=       {"score": score, "label": label, "box": box}
																			results.append(UpperCAmelCase__		)

							A__							=       sorted(UpperCAmelCase__			,	key=lambda UpperCAmelCase__		: x["score"]			,	reverse=UpperCAmelCase__		)
							if top_k:
													A__							=       results[:top_k]

							return results



	def 			__A							(       self			,	UpperCAmelCase__		):
							if self.framework != "pt":
													raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch."		)
							A__						,       A__						,       A__						,       A__							=       box.int().tolist()
							A__							=       {
							    "xmin": xmin,
							    "ymin": ymin,
							    "xmax": xmax,
							    "ymax": ymax,
							}
							return bbox










from __future__ import annotations

from random import random






class      UpperCamelCase    :



	def __init__(       self			,	UpperCAmelCase__ = None		):
							A__							=       value
							A__							=       random()
							A__							=       None
							A__							=       None



	def __repr__(       self		):
							from pprint import pformat

							if self.left is None and self.right is None:
													return F"""'{self.value}: {self.prior:.5}'"""
							else:
													return pformat(
													    {F"""{self.value}: {self.prior:.5}""": (self.left, self.right)}			,	indent=1		)



	def __str__(       self		):
							A__							=       str(self.value		) + " "
							A__							=       str(self.left or ""		)
							A__							=       str(self.right or ""		)
							return value + left + right





def  UpperCamelCase						(       _A			:				Node | None			,							_A			:				int    )->       tuple[Node | None, Node | None]:





						"""simple docstring"""





						if root is None:  # None tree is split into 2 Nones
												return None, None
						elif root.value is None:
												return None, None
						else:
												if value < root.value:
																		A__						,       A__							=       split(root.left			,							_A    )
																		return left, root
												else:
																		A__						,       A__							=       split(root.right			,							_A    )
																		return root, right
def  UpperCamelCase						(       _A			:				Node | None			,							_A			:				Node | None    )->       Node | None:





						"""simple docstring"""





						if (not left) or (not right):  # If one node is None, return the other
												return left or right
						elif left.prior < right.prior:
												A__							=       merge(left.right			,							_A    )
												return left
						else:
												A__							=       merge(_A			,							right.left    )
												return right
def  UpperCamelCase						(       _A			:				Node | None			,							_A			:				int    )->       Node | None:





						"""simple docstring"""





						A__							=       Node(_A    )
						A__						,       A__							=       split(_A			,							_A    )
						return merge(merge(_A			,							_A    )			,							_A    )
def  UpperCamelCase						(       _A			:				Node | None			,							_A			:				int    )->       Node | None:





						"""simple docstring"""





						A__						,       A__							=       split(_A			,							value - 1    )
						A__						,       A__							=       split(_A			,							_A    )
						return merge(_A			,							_A    )
def  UpperCamelCase						(       _A			:				Node | None    )->       None:





						"""simple docstring"""





						if not root:  # None
												return
						else:
												inorder(root.left    )
												print(root.value			,							end=","    )
												inorder(root.right    )
def  UpperCamelCase						(       _A			:				Node | None			,							_A			:				str    )->       Node | None:





						"""simple docstring"""





						for arg in args.split():
												if arg[0] == "+":
																		A__							=       insert(_A			,							int(arg[1:]    )    )

												elif arg[0] == "-":
																		A__							=       erase(_A			,							int(arg[1:]    )    )

												else:
																		print("Unknown command"    )

						return root
def  UpperCamelCase						(       )->       None:





						"""simple docstring"""





						A__							=       None
						print(
						    "enter numbers to create a tree, + value to add value into treap, "
						    "- value to erase all nodes with value. 'q' to quit. "    )

						A__							=       input()
						while args != "q":
												A__							=       interact_treap(_A			,							_A    )
												print(_A    )
												A__							=       input()

						print("good by!"    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()





