
"""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

def 				a						(							snake_case__:					int = 100					):


				'''simple docstring'''
				lowercase_       =					(n * (n + 1) // 2) ** 2
				lowercase_       =					n * (n + 1) * (2 * n + 1) // 6
				return sum_cubes - sum_squares


if __name__ == "__main__":
						print(f"{solution() = }")




