






from __future__ import annotations

from random import random
from typing import Generic, TypeVar

__A					=	TypeVar("KT")
__A					=	TypeVar("VT")



class 				_A			(     Generic[KT, VT]				):
    """simple docstring"""

    def __init__(				self      :     int ,     __SCREAMING_SNAKE_CASE      :     KT | str = "root" ,     __SCREAMING_SNAKE_CASE      :     VT | None = None      )      ->							List[Any]:
           __UpperCAmelCase     				=key
           __UpperCAmelCase     				=value
           __UpperCAmelCase     				=[]



    def __repr__(				self      :     str      )      ->							str:
           return f'''Node({self.key}: {self.value})'''



    @property
    def _a							(				self      :     str      )      ->							int:
           return len(self.forward      )




class 				_A			(     Generic[KT, VT]				):
    """simple docstring"""

    def __init__(				self      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     float = 0.5 ,     __SCREAMING_SNAKE_CASE      :     int = 16      )      ->							Optional[int]:
           __UpperCAmelCase     				=Node[KT, VT]()
           __UpperCAmelCase     				=0
           __UpperCAmelCase     				=p
           __UpperCAmelCase     				=max_level



    def __str__(				self      :     Optional[int]      )      ->							str:
           __UpperCAmelCase     				=list(self      )

           if len(__SCREAMING_SNAKE_CASE      ) == 0:
                  return f'''SkipList(level={self.level})'''

           __UpperCAmelCase     				=max((len(str(__SCREAMING_SNAKE_CASE      )      ) for item in items) ,     default=4      )
           __UpperCAmelCase     				=max(__SCREAMING_SNAKE_CASE ,     4      ) + 4

           __UpperCAmelCase     				=self.head
           __UpperCAmelCase     				=[]

           __UpperCAmelCase     				=node.forward.copy()
           lines.append(f'''[{node.key}]'''.ljust(__SCREAMING_SNAKE_CASE ,     """-"""      ) + """* """ * len(__SCREAMING_SNAKE_CASE      )      )
           lines.append(""" """ * label_size + """| """ * len(__SCREAMING_SNAKE_CASE      )      )

           while len(node.forward      ) != 0:
                  __UpperCAmelCase     				=node.forward[0]

                  lines.append(
                      f'''[{node.key}]'''.ljust(__SCREAMING_SNAKE_CASE ,     """-"""      )
                      + """ """.join(str(n.key      ) if n.key == node.key else """|""" for n in forwards      )      )
                  lines.append(""" """ * label_size + """| """ * len(__SCREAMING_SNAKE_CASE      )      )
                  __UpperCAmelCase     				=node.forward

           lines.append("""None""".ljust(__SCREAMING_SNAKE_CASE      ) + """* """ * len(__SCREAMING_SNAKE_CASE      )      )
           return f'''SkipList(level={self.level})\n''' + "\n".join(__SCREAMING_SNAKE_CASE      )



    def __iter__(				self      :     Tuple      )      ->							Optional[int]:
           __UpperCAmelCase     				=self.head

           while len(node.forward      ) != 0:
                  yield node.forward[0].key
                  __UpperCAmelCase     				=node.forward[0]



    def _a							(				self      :     Dict      )      ->							int:
           __UpperCAmelCase     				=1
           while random() < self.p and level < self.max_level:
                  level += 1

           return level



    def _a							(				self      :     int ,     __SCREAMING_SNAKE_CASE      :     List[str]      )      ->							tuple[Node[KT, VT] | None, list[Node[KT, VT]]]:
           __UpperCAmelCase     				=[]

           __UpperCAmelCase     				=self.head

           for i in reversed(range(self.level      )      ):
                  # i < node.level - When node level is lesser than `i` decrement `i`.
                  # node.forward[i].key < key - Jumping to node with key value higher
                  #                             or equal to searched key would result
                  #                             in skipping searched key.
                  while i < node.level and node.forward[i].key < key:
                         __UpperCAmelCase     				=node.forward[i]
                  # Each leftmost node (relative to searched node) will potentially have to
                  # be updated.
                  update_vector.append(__SCREAMING_SNAKE_CASE      )

           update_vector.reverse()  # Note that we were inserting values in reverse order.

           # len(node.forward) != 0 - If current node doesn't contain any further
           #                          references then searched key is not present.
           # node.forward[0].key == key - Next node key should be equal to search key
           #                              if key is present.
           if len(node.forward      ) != 0 and node.forward[0].key == key:
                  return node.forward[0], update_vector
           else:
                  return None, update_vector



    def _a							(				self      :     Any ,     __SCREAMING_SNAKE_CASE      :     KT      )      ->							Any:
           __UpperCAmelCase				, __UpperCAmelCase     				=self._locate_node(__SCREAMING_SNAKE_CASE      )

           if node is not None:
                  for i, update_node in enumerate(__SCREAMING_SNAKE_CASE      ):
                         # Remove or replace all references to removed node.
                         if update_node.level > i and update_node.forward[i].key == key:
                                if node.level > i:
                                       __UpperCAmelCase     				=node.forward[i]
                                else:
                                       __UpperCAmelCase     				=update_node.forward[:i]



    def _a							(				self      :     Dict ,     __SCREAMING_SNAKE_CASE      :     KT ,     __SCREAMING_SNAKE_CASE      :     VT      )      ->							Any:
           __UpperCAmelCase				, __UpperCAmelCase     				=self._locate_node(__SCREAMING_SNAKE_CASE      )
           if node is not None:
                  __UpperCAmelCase     				=value
           else:
                  __UpperCAmelCase     				=self.random_level()

                  if level > self.level:
                         # After level increase we have to add additional nodes to head.
                         for _ in range(self.level - 1 ,     __SCREAMING_SNAKE_CASE      ):
                                update_vector.append(self.head      )
                         __UpperCAmelCase     				=level

                  __UpperCAmelCase     				=Node(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )

                  for i, update_node in enumerate(update_vector[:level]      ):
                         # Change references to pass through new node.
                         if update_node.level > i:
                                new_node.forward.append(update_node.forward[i]      )

                         if update_node.level < i + 1:
                                update_node.forward.append(__SCREAMING_SNAKE_CASE      )
                         else:
                                __UpperCAmelCase     				=new_node



    def _a							(				self      :     Optional[Any] ,     __SCREAMING_SNAKE_CASE      :     VT      )      ->							VT | None:
           __UpperCAmelCase				, __UpperCAmelCase     				=self._locate_node(__SCREAMING_SNAKE_CASE      )

           if node is not None:
                  return node.value

           return None







def       lowercase__	(					)	->				Any:







       """simple docstring"""






       __UpperCAmelCase     				=SkipList()
       skip_list.insert("""Key1"""  ,      3		)
       skip_list.insert("""Key2"""  ,      12		)
       skip_list.insert("""Key3"""  ,      41		)
       skip_list.insert("""Key4"""  ,      -19		)

       __UpperCAmelCase     				=skip_list.head
       __UpperCAmelCase     				={}
       while node.level != 0:
              __UpperCAmelCase     				=node.forward[0]
              __UpperCAmelCase     				=node.value

       assert len(A_		) == 4
       assert all_values["Key1"] == 3
       assert all_values["Key2"] == 12
       assert all_values["Key3"] == 41
       assert all_values["Key4"] == -19







def       lowercase__	(					)	->				Optional[Any]:







       """simple docstring"""






       __UpperCAmelCase     				=SkipList()
       skip_list.insert("""Key1"""  ,      10		)
       skip_list.insert("""Key1"""  ,      12		)

       skip_list.insert("""Key5"""  ,      7		)
       skip_list.insert("""Key7"""  ,      10		)
       skip_list.insert("""Key10"""  ,      5		)

       skip_list.insert("""Key7"""  ,      7		)
       skip_list.insert("""Key5"""  ,      5		)
       skip_list.insert("""Key10"""  ,      10		)

       __UpperCAmelCase     				=skip_list.head
       __UpperCAmelCase     				={}
       while node.level != 0:
              __UpperCAmelCase     				=node.forward[0]
              __UpperCAmelCase     				=node.value

       if len(A_		) != 4:
              print()
       assert len(A_		) == 4
       assert all_values["Key1"] == 12
       assert all_values["Key7"] == 7
       assert all_values["Key5"] == 5
       assert all_values["Key10"] == 10







def       lowercase__	(					)	->				Union[str, Any]:







       """simple docstring"""






       __UpperCAmelCase     				=SkipList()
       assert skip_list.find("""Some key"""		) is None







def       lowercase__	(					)	->				int:







       """simple docstring"""






       __UpperCAmelCase     				=SkipList()

       skip_list.insert("""Key2"""  ,      20		)
       assert skip_list.find("""Key2"""		) == 20

       skip_list.insert("""Some Key"""  ,      10		)
       skip_list.insert("""Key2"""  ,      8		)
       skip_list.insert("""V"""  ,      13		)

       assert skip_list.find("""Y"""		) is None
       assert skip_list.find("""Key2"""		) == 8
       assert skip_list.find("""Some Key"""		) == 10
       assert skip_list.find("""V"""		) == 13







def       lowercase__	(					)	->				Tuple:







       """simple docstring"""






       __UpperCAmelCase     				=SkipList()
       skip_list.delete("""Some key"""		)

       assert len(skip_list.head.forward		) == 0







def       lowercase__	(					)	->				Dict:







       """simple docstring"""






       __UpperCAmelCase     				=SkipList()

       skip_list.insert("""Key1"""  ,      12		)
       skip_list.insert("""V"""  ,      13		)
       skip_list.insert("""X"""  ,      14		)
       skip_list.insert("""Key2"""  ,      15		)

       skip_list.delete("""V"""		)
       skip_list.delete("""Key2"""		)

       assert skip_list.find("""V"""		) is None
       assert skip_list.find("""Key2"""		) is None







def       lowercase__	(					)	->				Any:







       """simple docstring"""






       __UpperCAmelCase     				=SkipList()

       skip_list.insert("""Key1"""  ,      12		)
       skip_list.insert("""V"""  ,      13		)
       skip_list.insert("""X"""  ,      14		)
       skip_list.insert("""Key2"""  ,      15		)

       skip_list.delete("""V"""		)
       assert skip_list.find("""V"""		) is None
       assert skip_list.find("""X"""		) == 14
       assert skip_list.find("""Key1"""		) == 12
       assert skip_list.find("""Key2"""		) == 15

       skip_list.delete("""X"""		)
       assert skip_list.find("""V"""		) is None
       assert skip_list.find("""X"""		) is None
       assert skip_list.find("""Key1"""		) == 12
       assert skip_list.find("""Key2"""		) == 15

       skip_list.delete("""Key1"""		)
       assert skip_list.find("""V"""		) is None
       assert skip_list.find("""X"""		) is None
       assert skip_list.find("""Key1"""		) is None
       assert skip_list.find("""Key2"""		) == 15

       skip_list.delete("""Key2"""		)
       assert skip_list.find("""V"""		) is None
       assert skip_list.find("""X"""		) is None
       assert skip_list.find("""Key1"""		) is None
       assert skip_list.find("""Key2"""		) is None







def       lowercase__	(					)	->				Dict:







       """simple docstring"""






       __UpperCAmelCase     				=SkipList()

       skip_list.insert("""Key1"""  ,      12		)
       skip_list.insert("""V"""  ,      13		)
       skip_list.insert("""X"""  ,      142		)
       skip_list.insert("""Key2"""  ,      15		)

       skip_list.delete("""X"""		)

       def traverse_keys(A_:						Dict		):
              yield node.key
              for forward_node in node.forward:
                     yield from traverse_keys(A_		)

       assert len(set(traverse_keys(skip_list.head		)		)		) == 4







def       lowercase__	(					)	->				Optional[Any]:







       """simple docstring"""






       def is_sorted(A_:						Any		):
              return all(next_item >= item for item, next_item in zip(A_  ,      lst[1:]		)		)

       __UpperCAmelCase     				=SkipList()
       for i in range(10		):
              skip_list.insert(A_  ,      A_		)
       assert is_sorted(list(A_		)		)
       skip_list.delete(5		)
       skip_list.delete(8		)
       skip_list.delete(2		)
       assert is_sorted(list(A_		)		)
       skip_list.insert(-12  ,      -12		)
       skip_list.insert(77  ,      77		)
       assert is_sorted(list(A_		)		)







def       lowercase__	(					)	->				Dict:







       """simple docstring"""






       for _ in range(100		):
              # Repeat test 100 times due to the probabilistic nature of skip list
              # random values == random bugs
              test_insert()
              test_insert_overrides_existing_value()

              test_searching_empty_list_returns_none()
              test_search()

              test_deleting_item_from_empty_list_do_nothing()
              test_deleted_items_are_not_founded_by_find_method()
              test_delete_removes_only_given_key()
              test_delete_doesnt_leave_dead_nodes()

              test_iter_always_yields_sorted_values()







def       lowercase__	(					)	->				Optional[int]:







       """simple docstring"""






       __UpperCAmelCase     				=SkipList()
       skip_list.insert(2  ,      """2"""		)
       skip_list.insert(4  ,      """4"""		)
       skip_list.insert(6  ,      """4"""		)
       skip_list.insert(4  ,      """5"""		)
       skip_list.insert(8  ,      """4"""		)
       skip_list.insert(9  ,      """4"""		)

       skip_list.delete(4		)

       print(A_		)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()






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

UpperCamelCase_						:     int	       =							logging.get_logger(__name__)


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




@add_end_docstrings(__snake_case       )
class   __lowercase						(						__snake_case       ):






	def __init__(self				: Dict      ,	*snake_case				: List[Any]      ,	**snake_case				: Tuple    ) ->			Any:
							super().__init__(*snake_case      ,	**snake_case    )

							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       _a(self				: Optional[int]      ,	**snake_case				: Union[str, Any]    ) ->			Any:
							_lowercase       :	List[Any]           =   {}
							if "threshold" in kwargs:
													_lowercase       :	Optional[Any]           =   kwargs["threshold"]
							return {}, {}, postprocess_kwargs






	def __call__(self				: str      ,	*snake_case				: Dict      ,	**snake_case				: Optional[int]    ) ->			Union[Predictions, List[Prediction]]:
							return super().__call__(*snake_case      ,	**snake_case    )






	def       _a(self				: Tuple      ,	snake_case				: str    ) ->			Dict:
							_lowercase       :	List[str]           =   load_image(snake_case    )
							_lowercase       :	str           =   torch.IntTensor([[image.height, image.width]]    )
							_lowercase       :	int           =   self.image_processor(images=[image]      ,	return_tensors="pt"    )
							if self.tokenizer is not None:
													_lowercase       :	List[Any]           =   self.tokenizer(text=inputs["words"]      ,	boxes=inputs["boxes"]      ,	return_tensors="pt"    )
							_lowercase       :	str           =   target_size
							return inputs






	def       _a(self				: List[Any]      ,	snake_case				: Any    ) ->			str:
							_lowercase       :	Dict           =   model_inputs.pop("target_size"    )
							_lowercase       :	str           =   self.model(**snake_case    )
							_lowercase       :	Union[str, Any]           =   outputs.__class__({"target_size": target_size, **outputs}    )
							if self.tokenizer is not None:
													_lowercase       :	str           =   model_inputs["bbox"]
							return model_outputs






	def       _a(self				: str      ,	snake_case				: Tuple      ,	snake_case				: Dict=0.9    ) ->			List[str]:
							_lowercase       :	Optional[Any]           =   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.
													_lowercase      ,    _lowercase       :	str           =   target_size[0].tolist()

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

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

													_lowercase       :	int           =   scores.tolist()
													_lowercase       :	List[str]           =   [self.model.config.idalabel[label.item()] for label in labels]
													_lowercase       :	List[Any]           =   [self._get_bounding_box(snake_case    ) for box in boxes]

													# {"scores": [...], ...} --> [{"score":x, ...}, ...]
													_lowercase       :	Union[str, Any]           =   ["score", "label", "box"]
													_lowercase       :	Tuple           =   [
													    dict(zip(snake_case      ,	snake_case    )    )
													    for vals in zip(raw_annotation["scores"]      ,	raw_annotation["labels"]      ,	raw_annotation["boxes"]    )
													]

							return annotation






	def       _a(self				: int      ,	snake_case				: "torch.Tensor"    ) ->			Dict[str, int]:
							if self.framework != "pt":
													raise ValueError("The ObjectDetectionPipeline is only available in PyTorch."    )
							_lowercase      ,    _lowercase      ,    _lowercase      ,    _lowercase       :	Optional[int]           =   box.int().tolist()
							_lowercase       :	Tuple           =   {
							    "xmin": xmin,
							    "ymin": ymin,
							    "xmax": xmax,
							    "ymax": ymax,
							}
							return bbox




