




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

lowercase__				:      Any	   = logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase__				)
class       a__	( UpperCamelCase__				):







     def __init__(     self  ,		**A			)  ->   Optional[int]:



          '''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  ,		)  ->   Dict:



          '''simple docstring'''



          if "text_queries" in kwargs:
               a     =				kwargs.pop("text_queries"			)

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







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   Optional[int]:



          '''simple docstring'''



          a     =				{}
          if "threshold" in kwargs:
               a     =				kwargs["threshold"]
          if "top_k" in kwargs:
               a     =				kwargs["top_k"]
          return {}, {}, postprocess_params







     def       lowerCAmelCase_    (     self  ,		A			)  ->   str:



          '''simple docstring'''



          a     =				load_image(inputs["image"]			)
          a     =				inputs["candidate_labels"]
          if isinstance(A  ,		A			):
               a     =				candidate_labels.split(","			)

          a     =				torch.tensor([[image.height, image.width]]  ,		dtype=torch.intaa			)
          for i, candidate_label in enumerate(A			):
               a     =				self.tokenizer(A  ,		return_tensors=self.framework			)
               a     =				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       lowerCAmelCase_    (     self  ,		A			)  ->   Tuple:



          '''simple docstring'''



          a     =				model_inputs.pop("target_size"			)
          a     =				model_inputs.pop("candidate_label"			)
          a     =				model_inputs.pop("is_last"			)

          a     =				self.model(**A			)

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







     def       lowerCAmelCase_    (     self  ,		A  ,		A=0.1  ,		A=None			)  ->   Tuple:



          '''simple docstring'''



          a     =				[]
          for model_output in model_outputs:
               a     =				model_output["candidate_label"]
               a     =				BaseModelOutput(A			)
               a     =				self.image_processor.post_process_object_detection(
                   outputs=A  ,		threshold=A  ,		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(A			)

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

          return results







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



          '''simple docstring'''



          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







import math
import unittest

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					bool:
     assert isinstance(__UpperCamelCase		, __UpperCamelCase) 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 number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5		, int(math.sqrt(__UpperCamelCase) + 1)		, 6):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True




class       a__	( unittest.TestCase				):







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          self.assertTrue(is_prime(2			)			)
          self.assertTrue(is_prime(3			)			)
          self.assertTrue(is_prime(5			)			)
          self.assertTrue(is_prime(7			)			)
          self.assertTrue(is_prime(11			)			)
          self.assertTrue(is_prime(13			)			)
          self.assertTrue(is_prime(17			)			)
          self.assertTrue(is_prime(19			)			)
          self.assertTrue(is_prime(23			)			)
          self.assertTrue(is_prime(29			)			)







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          with self.assertRaises(A			):
               is_prime(-19			)
          self.assertFalse(
              is_prime(0			)  ,		"Zero doesn't have any positive factors, primes must have exactly two."  ,		)
          self.assertFalse(
              is_prime(1			)  ,		"One only has 1 positive factor, primes must have exactly two."  ,		)
          self.assertFalse(is_prime(2 * 2			)			)
          self.assertFalse(is_prime(2 * 3			)			)
          self.assertFalse(is_prime(3 * 3			)			)
          self.assertFalse(is_prime(3 * 5			)			)
          self.assertFalse(is_prime(3 * 5 * 7			)			)


if __name__ == "__main__":
   unittest.main()

