

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   :     str    							=     logging.get_logger(__name__)


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






@add_end_docstrings(__UpperCamelCase )
class       __magic_name__       (     __UpperCamelCase ):





			def __init__(						self   ,    *__snake_case   ,    **__snake_case   )  ->					Any:




						'''simple docstring'''
						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 						__magic_name__       (						self   ,    **__snake_case   )  ->					str:




						'''simple docstring'''
						__a     ={}
						if "threshold" in kwargs:
									__a     =kwargs['threshold']
						return {}, {}, postprocess_kwargs





			def __call__(						self   ,    *__snake_case   ,    **__snake_case   )  ->					Union[str, Any]:




						'''simple docstring'''
						return super().__call__(*__snake_case   ,    **__snake_case   )





			def 						__magic_name__       (						self   ,    __snake_case   )  ->					List[str]:




						'''simple docstring'''
						__a     =load_image(__snake_case   )
						__a     =torch.IntTensor([[image.height, image.width]]   )
						__a     =self.image_processor(images=[image]   ,    return_tensors='pt'   )
						if self.tokenizer is not None:
									__a     =self.tokenizer(text=inputs['words']   ,    boxes=inputs['boxes']   ,    return_tensors='pt'   )
						__a     =target_size
						return inputs





			def 						__magic_name__       (						self   ,    __snake_case   )  ->					Union[str, Any]:




						'''simple docstring'''
						__a     =model_inputs.pop('target_size'   )
						__a     =self.model(**__snake_case   )
						__a     =outputs.__class__({'target_size': target_size, **outputs}   )
						if self.tokenizer is not None:
									__a     =model_inputs['bbox']
						return model_outputs





			def 						__magic_name__       (						self   ,    __snake_case   ,    __snake_case=0.9   )  ->					List[Any]:




						'''simple docstring'''
						__a     =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.
									__a ,       __a     =target_size[0].tolist()

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

									__a ,       __a     =model_outputs['logits'].squeeze(0   ).softmax(dim=-1   ).max(dim=-1   )
									__a     =[self.model.config.idalabel[prediction] for prediction in classes.tolist()]
									__a     =[unnormalize(__snake_case   ) for bbox in model_outputs['bbox'].squeeze(0   )]
									__a     =['score', 'label', 'box']
									__a     =[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
									__a     =self.image_processor.post_process_object_detection(__snake_case   ,    __snake_case   ,    __snake_case   )
									__a     =raw_annotations[0]
									__a     =raw_annotation['scores']
									__a     =raw_annotation['labels']
									__a     =raw_annotation['boxes']

									__a     =scores.tolist()
									__a     =[self.model.config.idalabel[label.item()] for label in labels]
									__a     =[self._get_bounding_box(__snake_case   ) for box in boxes]

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

						return annotation





			def 						__magic_name__       (						self   ,    __snake_case   )  ->					List[Any]:




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








def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    if any(not isinstance(__lowerCAmelCase     ,		__lowerCAmelCase				) or x < 0 for x in sequence				):
        raise TypeError("Sequence must be list of non-negative integers"				)
    for _ in range(len(__lowerCAmelCase				)				):
        for i, (rod_upper, rod_lower) in enumerate(zip(__lowerCAmelCase     ,		sequence[1:]				)				):
            if rod_upper > rod_lower:
                sequence[i] -= rod_upper - rod_lower
                sequence[i + 1] += rod_upper - rod_lower
    return sequence


if __name__ == "__main__":
     assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
     assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]



