
"""simple docstring"""







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

lowerCamelCase__   					=		logging.get_logger(__name__)


lowerCamelCase__   					=		Dict[str, Any]
lowerCamelCase__   					=		List[Prediction]




@add_end_docstrings(_lowerCamelCase)
class  A__       (	_lowerCamelCase):

		def __init__(      self       ,		*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE ):
			super().__init__(*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_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 							__lowerCamelCase							(      self       ,		**_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						List[Any]									=      {}
			if "threshold" in kwargs:
				__lowerCAmelCase			:						int									=      kwargs['threshold']
			return {}, {}, postprocess_kwargs

		def __call__(      self       ,		*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE ):
			return super().__call__(*_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						List[str]									=      load_image(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						int									=      torch.IntTensor([[image.height, image.width]] )
			__lowerCAmelCase			:						int									=      self.image_processor(images=[image]       ,		return_tensors='pt' )
			if self.tokenizer is not None:
				__lowerCAmelCase			:						Tuple									=      self.tokenizer(text=inputs['words']       ,		boxes=inputs['boxes']       ,		return_tensors='pt' )
			__lowerCAmelCase			:						str									=      target_size
			return inputs

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						Union[str, Any]									=      model_inputs.pop('target_size' )
			__lowerCAmelCase			:						int									=      self.model(**_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						List[Any]									=      outputs.__class__({'target_size': target_size, **outputs} )
			if self.tokenizer is not None:
				__lowerCAmelCase			:						Dict									=      model_inputs['bbox']
			return model_outputs

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE=0.9 ):
			__lowerCAmelCase			:						Union[str, 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.
				__lowerCAmelCase			,   __lowerCAmelCase			:						int									=      target_size[0].tolist()

				def unnormalize(_SCREAMING_SNAKE_CASE ):
					return self._get_bounding_box(
					    torch.Tensor(
					        [
					            (width * bbox[0] / 10_00),
					            (height * bbox[1] / 10_00),
					            (width * bbox[2] / 10_00),
					            (height * bbox[3] / 10_00),
					        ] ) )

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

				__lowerCAmelCase			:						Any									=      scores.tolist()
				__lowerCAmelCase			:						Any									=      [self.model.config.idalabel[label.item()] for label in labels]
				__lowerCAmelCase			:						Optional[int]									=      [self._get_bounding_box(_SCREAMING_SNAKE_CASE ) for box in boxes]

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

			return annotation







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


from collections.abc import Callable



class   __lowercase				:




			"""simple docstring"""







			def __init__( self  :     Tuple   ,						lowerCAmelCase__  :     Callable | None = None):
									# Stores actual heap items.
									SCREAMING_SNAKE_CASE_: list  		=       []
									# Stores indexes of each item for supporting updates and deletion.
									SCREAMING_SNAKE_CASE_: dict  		=       {}
									# Stores current size of heap.
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       0
									# Stores function used to evaluate the score of an item on which basis ordering
									# will be done.
									SCREAMING_SNAKE_CASE_: Any  		=       key or (lambda lowerCAmelCase__: x)



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     int):
									return int((i - 1) / 2) if i > 0 else None



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       int(2 * i + 1)
									return left if 0 < left < self.size else None



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       int(2 * i + 2)
									return right if 0 < right < self.size else None



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       (
									    self.pos_map[self.arr[j][0]],
									    self.pos_map[self.arr[i][0]],
									)
									# Then swap the items in the list.
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[str]  		=       self.arr[j], self.arr[i]



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int):
									return self.arr[i][1] < self.arr[j][1]



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: Any  		=       self._left(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       self._right(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       i

									if left is not None and not self._cmp(lowerCAmelCase__   ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       left
									if right is not None and not self._cmp(lowerCAmelCase__   ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_: Tuple  		=       right

									return valid_parent



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       self._parent(lowerCAmelCase__)
									while parent is not None and not self._cmp(lowerCAmelCase__   ,						lowerCAmelCase__):
															self._swap(lowerCAmelCase__   ,						lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[str]  		=       parent, self._parent(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self._get_valid_parent(lowerCAmelCase__)
									while valid_parent != index:
															self._swap(lowerCAmelCase__   ,						lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Dict  		=       valid_parent, self._get_valid_parent(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int):
									if item not in self.pos_map:
															return
									SCREAMING_SNAKE_CASE_: Any  		=       self.pos_map[item]
									SCREAMING_SNAKE_CASE_: int  		=       [item, self.key(lowerCAmelCase__)]
									# Make sure heap is right in both up and down direction.
									# Ideally only one of them will make any change.
									self._heapify_up(lowerCAmelCase__)
									self._heapify_down(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     int):
									if item not in self.pos_map:
															return
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.pos_map[item]
									del self.pos_map[item]
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.arr[self.size - 1]
									SCREAMING_SNAKE_CASE_: Tuple  		=       index
									self.size -= 1
									# Make sure heap is right in both up and down direction. Ideally only one
									# of them will make any change- so no performance loss in calling both.
									if self.size > index:
															self._heapify_up(lowerCAmelCase__)
															self._heapify_down(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       len(self.arr)
									if arr_len == self.size:
															self.arr.append([item, self.key(lowerCAmelCase__)])
									else:
															SCREAMING_SNAKE_CASE_: str  		=       [item, self.key(lowerCAmelCase__)]
									SCREAMING_SNAKE_CASE_: List[Any]  		=       self.size
									self.size += 1
									self._heapify_up(self.size - 1)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									return self.arr[0] if self.size else None




			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: Dict  		=       self.get_top()
									if top_item_tuple:
															self.delete_item(top_item_tuple[0])
									return top_item_tuple







def 			A_      (    ):
						pass


if __name__ == "__main__":
						import doctest

						doctest.testmod()






