

"""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, ChunkPipeline


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_torch_available():
  import torch

  from transformers.modeling_outputs import BaseModelOutput

  from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

a					:Union[str, Any]    = logging.get_logger(__name__)

@add_end_docstrings(UpperCamelCase_)
class      __a							(UpperCamelCase_):






     '''simple docstring'''

     def __init__(						self	,    **_a  )  ->      List[str]:


           """simple docstring"""


           super().__init__(**_a  )

           if self.framework == "tf":
                 raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''  )

           requires_backends(self	,    """vision"""  )
           self.check_model_type(_a  )

     def __call__(						self	,    _a	,    _a = None	,    **_a	,    )  ->      Any:


           """simple docstring"""


           if "text_queries" in kwargs:
                 SCREAMING_SNAKE_CASE__   :      Any      		=	kwargs.pop("""text_queries"""  )

           if isinstance(_a	,    (str, Image.Image)  ):
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	{"""image""": image, """candidate_labels""": candidate_labels}
           else:
                 SCREAMING_SNAKE_CASE__   :      int      		=	image
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	super().__call__(_a	,    **_a  )
           return results

     def 					_a			(						self	,    **_a  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	{}
           if "threshold" in kwargs:
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	kwargs["""threshold"""]
           if "top_k" in kwargs:
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	kwargs["""top_k"""]
           return {}, {}, postprocess_params

     def 					_a			(						self	,    _a  )  ->      Optional[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	load_image(inputs["""image"""]  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	inputs["""candidate_labels"""]
           if isinstance(_a	,    _a  ):
                 SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	candidate_labels.split(""","""  )

           SCREAMING_SNAKE_CASE__   :      str      		=	torch.tensor([[image.height, image.width]]	,    dtype=torch.intaa  )
           for i, candidate_label in enumerate(_a  ):
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	self.tokenizer(_a	,    return_tensors=self.framework  )
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	self.image_processor(_a	,    return_tensors=self.framework  )
                 yield {
                     "is_last": i == len(_a  ) - 1,
                     "target_size": target_size,
                     "candidate_label": candidate_label,
                     **text_inputs,
                     **image_features,
                 }

     def 					_a			(						self	,    _a  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	model_inputs.pop("""target_size"""  )
           SCREAMING_SNAKE_CASE__   :      str      		=	model_inputs.pop("""candidate_label"""  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	model_inputs.pop("""is_last"""  )

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.model(**_a  )

           SCREAMING_SNAKE_CASE__   :      int      		=	{"""target_size""": target_size, """candidate_label""": candidate_label, """is_last""": is_last, **outputs}
           return model_outputs

     def 					_a			(						self	,    _a	,    _a=0.1	,    _a=None  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	[]
           for model_output in model_outputs:
                 SCREAMING_SNAKE_CASE__   :      List[str]      		=	model_output["""candidate_label"""]
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	BaseModelOutput(_a  )
                 SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.image_processor.post_process_object_detection(
                     outputs=_a	,    threshold=_a	,    target_sizes=model_output["""target_size"""]  )[0]

                 for index in outputs["scores"].nonzero():
                       SCREAMING_SNAKE_CASE__   :      List[str]      		=	outputs["""scores"""][index].item()
                       SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self._get_bounding_box(outputs["""boxes"""][index][0]  )

                       SCREAMING_SNAKE_CASE__   :      Dict      		=	{"""score""": score, """label""": label, """box""": box}
                       results.append(_a  )

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	sorted(_a	,    key=lambda _a  : x["score"]	,    reverse=_a  )
           if top_k:
                 SCREAMING_SNAKE_CASE__   :      List[str]      		=	results[:top_k]

           return results





     def 					_a			(						self	,    _a  )  ->      Dict[str, int]:


           """simple docstring"""


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







"""simple docstring"""


class      __a							:






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a	,    _a  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	name
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	value
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	weight

     def __repr__(						self  )  ->      List[Any]:


           """simple docstring"""


           return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           return self.value

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           return self.name

     def 					_a			(						self  )  ->      Optional[Any]:


           """simple docstring"""


           return self.weight





     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           return self.value / self.weight





def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Dict:
      SCREAMING_SNAKE_CASE__   :      Any      		=	[]
      for i in range(len(__lowerCAmelCase							)							):
            menu.append(Things(name[i]  ,					value[i]  ,					weight[i]							)							)
      return menu




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Union[str, Any]:
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	sorted(__lowerCAmelCase  ,					key=__lowerCAmelCase  ,					reverse=__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      str      		=	[]
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	0.0, 0.0
      for i in range(len(__lowerCAmelCase							)							):
            if (total_cost + items_copy[i].get_weight()) <= max_cost:
                  result.append(items_copy[i]							)
                  total_cost += items_copy[i].get_weight()
                  total_value += items_copy[i].get_value()
      return (result, total_value)




def  _lowercase					(    )       ->				List[str]:
      pass


if __name__ == "__main__":
  import doctest

  doctest.testmod()




