


"""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

lowerCAmelCase__				    =       logging.get_logger(__name__)


@add_end_docstrings(lowercase    )
class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       def __init__(						self		,					**snake_case__						):






         """simple docstring"""





         super().__init__(**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(snake_case__						)

       def __call__(						self		,					snake_case__		,					snake_case__ = None		,					**snake_case__		,					):






         """simple docstring"""





         if "text_queries" in kwargs:
           lowerCAmelCase					:					int								=							kwargs.pop("text_queries"						)

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

       def 	lowercase__							(						self		,					**snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Dict								=							{}
         if "threshold" in kwargs:
           lowerCAmelCase					:					List[str]								=							kwargs["threshold"]
         if "top_k" in kwargs:
           lowerCAmelCase					:					List[Any]								=							kwargs["top_k"]
         return {}, {}, postprocess_params

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					str								=							load_image(inputs["image"]						)
         lowerCAmelCase					:					List[Any]								=							inputs["candidate_labels"]
         if isinstance(snake_case__		,					snake_case__						):
           lowerCAmelCase					:					List[str]								=							candidate_labels.split(","						)

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

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Union[str, Any]								=							model_inputs.pop("target_size"						)
         lowerCAmelCase					:					str								=							model_inputs.pop("candidate_label"						)
         lowerCAmelCase					:					str								=							model_inputs.pop("is_last"						)

         lowerCAmelCase					:					Optional[int]								=							self.model(**snake_case__						)

         lowerCAmelCase					:					Optional[int]								=							{"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
         return model_outputs

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__=0.1		,					snake_case__=None						):






         """simple docstring"""





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

           for index in outputs["scores"].nonzero():
             lowerCAmelCase					:					Tuple								=							outputs["scores"][index].item()
             lowerCAmelCase					:					Tuple								=							self._get_bounding_box(outputs["boxes"][index][0]						)

             lowerCAmelCase					:					str								=							{"score": score, "label": label, "box": box}
             results.append(snake_case__						)

         lowerCAmelCase					:					Union[str, Any]								=							sorted(snake_case__		,					key=lambda snake_case__						: x["score"]		,					reverse=snake_case__						)
         if top_k:
           lowerCAmelCase					:					int								=							results[:top_k]

         return results







       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





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









"""simple docstring"""







import baseaa
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ):


  '''simple docstring'''







  return baseaa.baaencode(string.encode("utf-8"    )    )
def  a__      (    SCREAMING_SNAKE_CASE   :   bytes    ):


  '''simple docstring'''







  return baseaa.baadecode(SCREAMING_SNAKE_CASE    ).decode("utf-8"    )


if __name__ == "__main__":
     lowerCAmelCase__				    =       '''Hello World!'''
     lowerCAmelCase__				    =       baseaa_encode(test)
     print(encoded)

     lowerCAmelCase__				    =       baseaa_decode(encoded)
     print(decoded)





