





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

_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE      	=		Dict[str, Any]
_SCREAMING_SNAKE_CASE      	=		List[Prediction]
@add_end_docstrings(snake_case_					)
class SCREAMING_SNAKE_CASE_						(					snake_case_					):






						def __init__(      self      :      Dict		,   *_A      :      Optional[int]		,   **_A      :      str	)							->    Optional[Any]:







							"""simple docstring"""


							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 UpperCAmelCase_				(      self      :      int		,   **_A      :      Tuple	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			int											=	{}
							if "threshold" in kwargs:
								snake_case_		:			str											=	kwargs['threshold']
							return {}, {}, postprocess_kwargs






						def __call__(      self      :      int		,   *_A      :      int		,   **_A      :      Union[str, Any]	)							->    Union[Predictions, List[Prediction]]:







							"""simple docstring"""


							return super().__call__(*_A		,   **_A	)






						def UpperCAmelCase_				(      self      :      List[str]		,   _A      :      int	)							->    str:







							"""simple docstring"""


							snake_case_		:			Tuple											=	load_image(_A	)
							snake_case_		:			Dict											=	torch.IntTensor([[image.height, image.width]]	)
							snake_case_		:			Optional[Any]											=	self.image_processor(images=[image]		,   return_tensors='pt'	)
							if self.tokenizer is not None:
								snake_case_		:			Any											=	self.tokenizer(text=inputs['words']		,   boxes=inputs['boxes']		,   return_tensors='pt'	)
							snake_case_		:			int											=	target_size
							return inputs






						def UpperCAmelCase_				(      self      :      Dict		,   _A      :      List[str]	)							->    Optional[Any]:







							"""simple docstring"""


							snake_case_		:			Optional[int]											=	model_inputs.pop('target_size'	)
							snake_case_		:			List[Any]											=	self.model(**_A	)
							snake_case_		:			str											=	outputs.__class__({'target_size': target_size, **outputs}	)
							if self.tokenizer is not None:
								snake_case_		:			Optional[int]											=	model_inputs['bbox']
							return model_outputs






						def UpperCAmelCase_				(      self      :      Dict		,   _A      :      Optional[Any]		,   _A      :      Any=0.9	)							->    int:







							"""simple docstring"""


							snake_case_		:			List[str]											=	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.
								snake_case_						,snake_case_		:			int											=	target_size[0].tolist()

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

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

								snake_case_		:			Union[str, Any]											=	scores.tolist()
								snake_case_		:			Optional[Any]											=	[self.model.config.idalabel[label.item()] for label in labels]
								snake_case_		:			Dict											=	[self._get_bounding_box(_A	) for box in boxes]

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

							return annotation






						def UpperCAmelCase_				(      self      :      Any		,   _A      :      "torch.Tensor"	)							->    Dict[str, int]:







							"""simple docstring"""


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








from __future__ import annotations

from dataclasses import dataclass
@dataclass
class SCREAMING_SNAKE_CASE_						:
						__magic_name__:			float
						__magic_name__:			TreeNode | None       =       None
						__magic_name__:			TreeNode | None       =       None


def 							SCREAMING_SNAKE_CASE__						(    __a   ):

	# Validation
	def is_valid_tree(__a   ) -> bool:
		if node is None:
			return True

		if not isinstance(__a   , __a   ):
			return False

		try:
			float(node.data   )
		except (TypeError, ValueError):
			return False

		return is_valid_tree(node.left   ) and is_valid_tree(node.right   )

	if not is_valid_tree(__a   ):
		raise ValueError(
		    'Each node should be type of TreeNode and data should be float.'   )

	def is_binary_search_tree_recursive_check(
	    __a   , __a   , __a   ) -> bool:

		if node is None:
			return True

		return (
		    left_bound < node.data < right_bound
		    and is_binary_search_tree_recursive_check(node.left   , __a   , node.data   )
		    and is_binary_search_tree_recursive_check(
		        node.right   , node.data   , __a   )
		)

	return is_binary_search_tree_recursive_check(__a   , -float('inf'   )   , float('inf'   )   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()

