



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

UpperCamelCase__ =						logging.get_logger(__name__)
@add_end_docstrings(_snake_case			)
class 			_lowerCAmelCase   ( _snake_case			):






			"""simple docstring"""





			def __init__(				self	:       Any			,     **lowercase	:       Any   )						->					Tuple:
									"""simple docstring"""







									super().__init__(**lowercase   )

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

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





			def __call__(				self	:       List[str]			,     lowercase	:       Union[str, "Image.Image", List[Dict[str, Any]]]			,     lowercase	:       Union[str, List[str]] = None			,     **lowercase	:       Optional[Any]			,     )						->					Any:
									"""simple docstring"""







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

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





			def 		snake_case__			(				self	:       Any			,     **lowercase	:       Any   )						->					List[Any]:
									"""simple docstring"""







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





			def 		snake_case__			(				self	:       Tuple			,     lowercase	:       List[Any]   )						->					List[Any]:
									"""simple docstring"""







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

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





			def 		snake_case__			(				self	:       Optional[Any]			,     lowercase	:       Union[str, Any]   )						->					Tuple:
									"""simple docstring"""







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

									__lowercase					=				self.model(**lowercase   )

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





			def 		snake_case__			(				self	:       Any			,     lowercase	:       Tuple			,     lowercase	:       Any=0.1			,     lowercase	:       Optional[int]=None   )						->					List[Any]:
									"""simple docstring"""







									__lowercase					=				[]
									for model_output in model_outputs:
															__lowercase					=				model_output["""candidate_label"""]
															__lowercase					=				BaseModelOutput(lowercase   )
															__lowercase					=				self.image_processor.post_process_object_detection(
															    outputs=lowercase			,     threshold=lowercase			,     target_sizes=model_output["""target_size"""]   )[0]

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

																					__lowercase					=				{"""score""": score, """label""": label, """box""": box}
																					results.append(lowercase   )

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

									return results







			def 		snake_case__			(				self	:       Dict			,     lowercase	:       "torch.Tensor"   )						->					str:
									"""simple docstring"""







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










def  UpperCAmelCase__    (    lowercase__ = 100   ) ->      int:
						__lowercase					=				n * (n + 1) * (2 * n + 1) / 6
						__lowercase					=				(n * (n + 1) / 2) ** 2
						return int(square_of_sum - sum_of_squares   )


if __name__ == "__main__":
			print(F"""{solution() = }""")





