

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    :List[str]      =			logging.get_logger(__name__)

@add_end_docstrings(snake_case_				)
class   _a					(			snake_case_				):




      """simple docstring"""

      def __init__(   self							:				Any						,			**UpperCAmelCase							:				List[str]	):
            super().__init__(**UpperCAmelCase	)

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

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





      def __call__(   self							:				Optional[int]						,			UpperCAmelCase							:				Union[str, "Image.Image", List[Dict[str, Any]]]						,			UpperCAmelCase							:				Union[str, List[str]] = None						,			**UpperCAmelCase							:				List[Any]						,			):
            if "text_queries" in kwargs:
                  A_						  =				kwargs.pop("text_queries"	)

            if isinstance(UpperCAmelCase						,			(str, Image.Image)	):
                  A_						  =				{"image": image, "candidate_labels": candidate_labels}
            else:
                  A_						  =				image
            A_						  =				super().__call__(UpperCAmelCase						,			**UpperCAmelCase	)
            return results





      def 		__A	(   self							:				int						,			**UpperCAmelCase							:				Tuple	):
            A_						  =				{}
            if "threshold" in kwargs:
                  A_						  =				kwargs["threshold"]
            if "top_k" in kwargs:
                  A_						  =				kwargs["top_k"]
            return {}, {}, postprocess_params





      def 		__A	(   self							:				List[str]						,			UpperCAmelCase							:				Dict	):
            A_						  =				load_image(inputs["image"]	)
            A_						  =				inputs["candidate_labels"]
            if isinstance(UpperCAmelCase						,			UpperCAmelCase	):
                  A_						  =				candidate_labels.split(","	)

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





      def 		__A	(   self							:				str						,			UpperCAmelCase							:				int	):
            A_						  =				model_inputs.pop("target_size"	)
            A_						  =				model_inputs.pop("candidate_label"	)
            A_						  =				model_inputs.pop("is_last"	)

            A_						  =				self.model(**UpperCAmelCase	)

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





      def 		__A	(   self							:				Dict						,			UpperCAmelCase							:				Any						,			UpperCAmelCase							:				Optional[int]=0.1						,			UpperCAmelCase							:				Optional[int]=None	):
            A_						  =				[]
            for model_output in model_outputs:
                  A_						  =				model_output["candidate_label"]
                  A_						  =				BaseModelOutput(UpperCAmelCase	)
                  A_						  =				self.image_processor.post_process_object_detection(
                      outputs=UpperCAmelCase						,			threshold=UpperCAmelCase						,			target_sizes=model_output["target_size"]	)[0]

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

                        A_						  =				{"score": score, "label": label, "box": box}
                        results.append(UpperCAmelCase	)

            A_						  =				sorted(UpperCAmelCase						,			key=lambda UpperCAmelCase	: x["score"]						,			reverse=UpperCAmelCase	)
            if top_k:
                  A_						  =				results[:top_k]

            return results







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


"""simple docstring"""

def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						float:
 if digit_amount > 0:
  return round(number - int(lowercase__   )					,					lowercase__   )
 return number - int(lowercase__   )


if __name__ == "__main__":
    print(decimal_isolate(1.53, 0))
    print(decimal_isolate(35.345, 1))
    print(decimal_isolate(35.345, 2))
    print(decimal_isolate(35.345, 3))
    print(decimal_isolate(-14.789, 3))
    print(decimal_isolate(0, 2))
    print(decimal_isolate(-14.123, 1))
    print(decimal_isolate(-14.123, 2))
    print(decimal_isolate(-14.123, 3))






