





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


@add_end_docstrings(_UpperCamelCase      )
class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	def __init__(						self:						List[str],					**lowerCamelCase_:						int			):
			super().__init__(**lowerCamelCase_			)

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

			requires_backends(self,					'vision'			)
			self.check_model_type(lowerCamelCase_			)

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

			if isinstance(lowerCamelCase_,					(str, Image.Image)			):
					lowercase__					:      Optional[int]      =		{'image': image, 'candidate_labels': candidate_labels}
			else:
					lowercase__					:      str      =		image
			lowercase__					:      str      =		super().__call__(lowerCamelCase_,					**lowerCamelCase_			)
			return results

	def    snake_case__(						self:						int,					**lowerCamelCase_:						Union[str, Any]			):
			lowercase__					:      Dict      =		{}
			if "threshold" in kwargs:
					lowercase__					:      List[Any]      =		kwargs['threshold']
			if "top_k" in kwargs:
					lowercase__					:      List[str]      =		kwargs['top_k']
			return {}, {}, postprocess_params

	def    snake_case__(						self:						Tuple,					lowerCamelCase_:						Any			):
			lowercase__					:      List[Any]      =		load_image(inputs['image']			)
			lowercase__					:      Optional[int]      =		inputs['candidate_labels']
			if isinstance(lowerCamelCase_,					lowerCamelCase_			):
					lowercase__					:      Tuple      =		candidate_labels.split(','			)

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

	def    snake_case__(						self:						Dict,					lowerCamelCase_:						Union[str, Any]			):
			lowercase__					:      Tuple      =		model_inputs.pop('target_size'			)
			lowercase__					:      Optional[Any]      =		model_inputs.pop('candidate_label'			)
			lowercase__					:      List[Any]      =		model_inputs.pop('is_last'			)

			lowercase__					:      Optional[int]      =		self.model(**lowerCamelCase_			)

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

	def    snake_case__(						self:						Optional[Any],					lowerCamelCase_:						Any,					lowerCamelCase_:						Union[str, Any]=0.1,					lowerCamelCase_:						List[Any]=None			):
			lowercase__					:      Optional[int]      =		[]
			for model_output in model_outputs:
					lowercase__					:      int      =		model_output['candidate_label']
					lowercase__					:      Dict      =		BaseModelOutput(lowerCamelCase_			)
					lowercase__					:      Any      =		self.image_processor.post_process_object_detection(
					    outputs=lowerCamelCase_,					threshold=lowerCamelCase_,					target_sizes=model_output['target_size']			)[0]

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

							lowercase__					:      Tuple      =		{'score': score, 'label': label, 'box': box}
							results.append(lowerCamelCase_			)

			lowercase__					:      Optional[Any]      =		sorted(lowerCamelCase_,					key=lambda lowerCamelCase_			: x["score"],					reverse=lowerCamelCase_			)
			if top_k:
					lowercase__					:      Tuple      =		results[:top_k]

			return results





	def    snake_case__(						self:						Any,					lowerCamelCase_:						"torch.Tensor"			):
			if self.framework != "pt":
					raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.'			)
			lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__					:      Tuple      =		box.int().tolist()
			lowercase__					:      Union[str, Any]      =		{
			    'xmin': xmin,
			    'ymin': ymin,
			    'xmax': xmax,
			    'ymax': ymax,
			}
			return bbox









def       SCREAMING_SNAKE_CASE__  (       _lowercase     : int   ,    _lowercase     : int			)   ->							Optional[int]:




		'''simple docstring'''
		if b == 0:
				return 1
		if (b % 2) == 0:
				return actual_power(_lowercase   ,    int(b / 2			)			) * actual_power(_lowercase   ,    int(b / 2			)			)
		else:
				return a * actual_power(_lowercase   ,    int(b / 2			)			) * actual_power(_lowercase   ,    int(b / 2			)			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : int   ,    _lowercase     : int			)   ->							float:




		'''simple docstring'''
		if b < 0:
				return 1 / actual_power(_lowercase   ,    _lowercase			)
		return actual_power(_lowercase   ,    _lowercase			)


if __name__ == "__main__":
			print(power(-2, -3))


