



"""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      :							List[str]   =      logging.get_logger(__name__)
@add_end_docstrings(lowerCAmelCase)
class 	_a       (    lowerCAmelCase):
			"""simple docstring"""



			def __init__(						self   :			str ,      **__UpperCamelCase   :			Optional[int]      )->Dict:
							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[Any] ,      __UpperCamelCase   :			Union[str, "Image.Image", List[Dict[str, Any]]] ,      __UpperCamelCase   :			Union[str, List[str]] = None ,      **__UpperCamelCase   :			Dict ,      )->Optional[int]:


							if "text_queries" in kwargs:
											_UpperCAmelCase							=			kwargs.pop('''text_queries'''      )

							if isinstance(__UpperCAmelCase ,      (str, Image.Image)      ):
											_UpperCAmelCase							=			{'''image''': image, '''candidate_labels''': candidate_labels}
							else:
											_UpperCAmelCase							=			image
							_UpperCAmelCase							=			super().__call__(__UpperCAmelCase ,      **__UpperCAmelCase      )
							return results



			def        lowercase__  (						self   :			Union[str, Any] ,      **__UpperCamelCase   :			Optional[int]      )->Dict:
							_UpperCAmelCase							=			{}
							if "threshold" in kwargs:
											_UpperCAmelCase							=			kwargs['''threshold''']
							if "top_k" in kwargs:
											_UpperCAmelCase							=			kwargs['''top_k''']
							return {}, {}, postprocess_params



			def        lowercase__  (						self   :			str ,      __UpperCamelCase   :			int      )->Optional[Any]:
							_UpperCAmelCase							=			load_image(inputs['''image''']      )
							_UpperCAmelCase							=			inputs['''candidate_labels''']
							if isinstance(__UpperCAmelCase ,      __UpperCAmelCase      ):
											_UpperCAmelCase							=			candidate_labels.split(''','''      )

							_UpperCAmelCase							=			torch.tensor([[image.height, image.width]] ,      dtype=torch.intaa      )
							for i, candidate_label in enumerate(__UpperCAmelCase      ):
											_UpperCAmelCase							=			self.tokenizer(__UpperCAmelCase ,      return_tensors=self.framework      )
											_UpperCAmelCase							=			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        lowercase__  (						self   :			Optional[int] ,      __UpperCamelCase   :			int      )->str:
							_UpperCAmelCase							=			model_inputs.pop('''target_size'''      )
							_UpperCAmelCase							=			model_inputs.pop('''candidate_label'''      )
							_UpperCAmelCase							=			model_inputs.pop('''is_last'''      )

							_UpperCAmelCase							=			self.model(**__UpperCAmelCase      )

							_UpperCAmelCase							=			{'''target_size''': target_size, '''candidate_label''': candidate_label, '''is_last''': is_last, **outputs}
							return model_outputs



			def        lowercase__  (						self   :			int ,      __UpperCamelCase   :			str ,      __UpperCamelCase   :			List[str]=0.1 ,      __UpperCamelCase   :			List[str]=None      )->List[Any]:
							_UpperCAmelCase							=			[]
							for model_output in model_outputs:
											_UpperCAmelCase							=			model_output['''candidate_label''']
											_UpperCAmelCase							=			BaseModelOutput(__UpperCAmelCase      )
											_UpperCAmelCase							=			self.image_processor.post_process_object_detection(
											    outputs=__UpperCAmelCase ,      threshold=__UpperCAmelCase ,      target_sizes=model_output['''target_size''']      )[0]

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

															_UpperCAmelCase							=			{'''score''': score, '''label''': label, '''box''': box}
															results.append(__UpperCAmelCase      )

							_UpperCAmelCase							=			sorted(__UpperCAmelCase ,      key=lambda __UpperCamelCase      : x["score"] ,      reverse=__UpperCAmelCase      )
							if top_k:
											_UpperCAmelCase							=			results[:top_k]

							return results



			def        lowercase__  (						self   :			Any ,      __UpperCamelCase   :			"torch.Tensor"      )->Dict[str, int]:


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











def 						_a    (      a :int )							->			bool:
 a		  =      n ** (1 / 3)
 return (val * val * val) == n


if __name__ == "__main__":
     print(perfect_cube(27))
     print(perfect_cube(4))


