


"""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

UpperCAmelCase      		=			logging.get_logger(__name__)


UpperCAmelCase      		=			Dict[str, Any]
UpperCAmelCase      		=			List[Prediction]
@add_end_docstrings(__UpperCAmelCase     )
class 					__magic_name__		(			__UpperCAmelCase     ):






       def __init__(   self  :    List[Any]  ,     *snake_case__  :    str  ,     **snake_case__  :    str				):

              '''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  __snake_case						(   self  :    Optional[int]  ,     **snake_case__  :    Tuple				):

              '''simple docstring'''






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






       def __call__(   self  :    Optional[Any]  ,     *snake_case__  :    int  ,     **snake_case__  :    Optional[Any]				):

              '''simple docstring'''






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






       def  __snake_case						(   self  :    List[str]  ,     snake_case__  :    str				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				load_image(snake_case__				)
              lowercase		:Tuple       							=				torch.IntTensor([[image.height, image.width]]				)
              lowercase		:Any       							=				self.image_processor(images=[image]  ,     return_tensors='''pt'''				)
              if self.tokenizer is not None:
                     lowercase		:List[str]       							=				self.tokenizer(text=inputs['''words''']  ,     boxes=inputs['''boxes''']  ,     return_tensors='''pt'''				)
              lowercase		:List[Any]       							=				target_size
              return inputs






       def  __snake_case						(   self  :    Dict  ,     snake_case__  :    Tuple				):

              '''simple docstring'''






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






       def  __snake_case						(   self  :    Optional[Any]  ,     snake_case__  :    List[Any]  ,     snake_case__  :    Optional[Any]=0.9				):

              '''simple docstring'''






              lowercase		: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.
                     lowercase							,  lowercase		:List[str]       							=				target_size[0].tolist()

                     def unnormalize(snake_case__  :    Union[str, Any]				):
                            return self._get_bounding_box(
                                torch.Tensor(
                                    [
                                        (width * bbox[0] / 1_0_0_0),
                                        (height * bbox[1] / 1_0_0_0),
                                        (width * bbox[2] / 1_0_0_0),
                                        (height * bbox[3] / 1_0_0_0),
                                    ]				)				)

                     lowercase							,  lowercase		:int       							=				model_outputs['''logits'''].squeeze(0				).softmax(dim=-1				).max(dim=-1				)
                     lowercase		:Optional[Any]       							=				[self.model.config.idalabel[prediction] for prediction in classes.tolist()]
                     lowercase		:List[str]       							=				[unnormalize(snake_case__				) for bbox in model_outputs['''bbox'''].squeeze(0				)]
                     lowercase		:Optional[int]       							=				['''score''', '''label''', '''box''']
                     lowercase		:Optional[Any]       							=				[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
                     lowercase		:Dict       							=				self.image_processor.post_process_object_detection(snake_case__  ,     snake_case__  ,     snake_case__				)
                     lowercase		:List[Any]       							=				raw_annotations[0]
                     lowercase		:Dict       							=				raw_annotation['''scores''']
                     lowercase		:Optional[int]       							=				raw_annotation['''labels''']
                     lowercase		:int       							=				raw_annotation['''boxes''']

                     lowercase		:Dict       							=				scores.tolist()
                     lowercase		:Any       							=				[self.model.config.idalabel[label.item()] for label in labels]
                     lowercase		:Any       							=				[self._get_bounding_box(snake_case__				) for box in boxes]

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

              return annotation







       def  __snake_case						(   self  :    str  ,     snake_case__  :    "torch.Tensor"				):

              '''simple docstring'''






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





"""simple docstring"""



import math






def       lowerCamelCase					(a_						:int)     ->							bool:
       assert isinstance(a_				,				a_) and (
           number >= 0
       ), "'number' must been an int and positive"

       if 1 < number < 4:
              # 2 and 3 are primes
              return True
       elif number < 2 or not number % 2:
              # Negatives, 0, 1 and all even numbers are not primes
              return False

       lowercase		:Any       							=				range(3				,				int(math.sqrt(a_) + 1)				,				2)
       return not any(not number % i for i in odd_numbers)






def       lowerCamelCase					(a_						:Optional[int]				,				a_						:Optional[int]=1				,				**a_						:List[str])     ->							Any:
       lowercase		:str       							=				factor * value
       lowercase		:int       							=				value

       while not is_prime(a_):
              value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1

       if value == first_value_val:
              return next_prime(value + 1				,				**a_)
       return value

