


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(UpperCamelCase_	)
class A    (  UpperCamelCase_	):






      def __init__(   self		:	Tuple     ,  *lowercase_		:	List[str]     ,  **lowercase_		:	int					)   ->		List[str]:
             """simple docstring"""




             super().__init__(*lowercase_     ,  **lowercase_					)

             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		:	List[str]     ,  **lowercase_		:	Union[str, Any]					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            ={}
             if "threshold" in kwargs:
                    _lowerCamelCase			:						Any            =kwargs['threshold']
             return {}, {}, postprocess_kwargs






      def __call__(   self		:	Any     ,  *lowercase_		:	Optional[Any]     ,  **lowercase_		:	Dict					)   ->		Union[Predictions, List[Prediction]]:
             """simple docstring"""




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






      def 			lowerCamelCase			(   self		:	Any     ,  lowercase_		:	Union[str, Any]					)   ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =load_image(lowercase_					)
             _lowerCamelCase			:						Tuple            =torch.IntTensor([[image.height, image.width]]					)
             _lowerCamelCase			:						Optional[Any]            =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			:						Any            =target_size
             return inputs






      def 			lowerCamelCase			(   self		:	List[str]     ,  lowercase_		:	Any					)   ->		Any:
             """simple docstring"""




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






      def 			lowerCamelCase			(   self		:	Any     ,  lowercase_		:	List[Any]     ,  lowercase_		:	Dict=0.9					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Dict            =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			:						Union[str, Any]            =target_size[0].tolist()

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

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

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

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

             return annotation






      def 			lowerCamelCase			(   self		:	int     ,  lowercase_		:	"torch.Tensor"					)   ->		Dict[str, int]:
             """simple docstring"""




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






def       a_ (       SCREAMING_SNAKE_CASE__   : bytes	):
       '''simple docstring'''





       return "".join([hex(SCREAMING_SNAKE_CASE__	)[2:].zfill(2	).upper() for byte in list(SCREAMING_SNAKE_CASE__	)]	)




def       a_ (       SCREAMING_SNAKE_CASE__   : str	):
       '''simple docstring'''





       if (len(SCREAMING_SNAKE_CASE__	) % 2) != 0:
              raise ValueError(
                  'Base16 encoded data is invalid:\nData does not have an even number of hex digits.'	)
       # Check the character set - the standard base16 alphabet
       # is uppercase according to RFC3548 section 6
       if not set(SCREAMING_SNAKE_CASE__	) <= set('0123456789ABCDEF'	):
              raise ValueError(
                  'Base16 encoded data is invalid:\nData is not uppercase hex or it contains invalid characters.'	)
       # For every two hexadecimal digits (= a byte), turn it into an integer.
       # Then, string the result together into bytes, and return it.
       return bytes(int(data[i] + data[i + 1]						,     16	) for i in range(0						,     len(SCREAMING_SNAKE_CASE__	)						,     2	)	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


