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

UpperCAmelCase_			    =							logging.get_logger(__name__)


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




@add_end_docstrings(__a    )
class  __magic_name__  (					__a    ):





   """simple docstring"""




   def __init__(	self					:  Dict		,  *_lowercase					:  int		,  **_lowercase					:  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					:  int		,  **_lowercase					:  Any							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= {}
    if "threshold" in kwargs:
     _UpperCamelCase:     Dict							= kwargs['''threshold''']
    return {}, {}, postprocess_kwargs



   def __call__(	self					:  Tuple		,  *_lowercase					:  str		,  **_lowercase					:  int							):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  int		,  _lowercase					:  Dict							):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  Union[str, Any]		,  _lowercase					:  Optional[Any]							):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  Tuple		,  _lowercase					:  int		,  _lowercase					:  Dict=0.9							):



    """simple docstring"""



    _UpperCamelCase:     Optional[int]							= 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.
     _UpperCamelCase:     Union[str, Any]							= target_size[0].tolist()

     def unnormalize(_lowercase					:  Any							):
      return self._get_bounding_box(
          torch.Tensor(
              [
                  (width * bbox[0] / 1_000),
                  (height * bbox[1] / 1_000),
                  (width * bbox[2] / 1_000),
                  (height * bbox[3] / 1_000),
              ]							)							)

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

     _UpperCamelCase:     Any							= scores.tolist()
     _UpperCamelCase:     int							= [self.model.config.idalabel[label.item()] for label in labels]
     _UpperCamelCase:     str							= [self._get_bounding_box(_lowercase							) for box in boxes]

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

    return annotation



   def   lowerCAmelCase						(	self					:  Optional[int]		,  _lowercase					:  "torch.Tensor"							):



    """simple docstring"""



    if self.framework != "pt":
     raise ValueError('''The ObjectDetectionPipeline is only available in PyTorch.'''							)
    _UpperCamelCase:     Optional[Any]							= box.int().tolist()
    _UpperCamelCase:     str							= {
        '''xmin''': xmin,
        '''ymin''': ymin,
        '''xmax''': xmax,
        '''ymax''': ymax,
    }
    return bbox
def       lowerCAmelCase_     (		lowercase:							int = 10**9		)    ->				int:







 '''simple docstring'''
 _UpperCamelCase:     List[Any]							= 1
 _UpperCamelCase:     Dict							= 2

 _UpperCamelCase:     Tuple							= 0
 _UpperCamelCase:     int							= 0
 _UpperCamelCase:     Dict							= 0
 while perimeter <= max_perimeter:
  perimeters_sum += perimeter

  prev_value += 2 * value
  value += prev_value

  _UpperCamelCase:     str							= 2 * value + 2 if i % 2 == 0 else 2 * value - 2
  i += 1

 return perimeters_sum


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