


'''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

lowerCAmelCase				:Any        =      logging.get_logger(__name__)
@add_end_docstrings(UpperCamelCase__							)
class _lowerCamelCase	(						UpperCamelCase__							):



		'''simple docstring'''







		def __init__( self		:						str  ,					**_A		:						int      )  ->			str:
			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		:						Optional[int]  ,					_A		:						int  ,					_A		:						Dict = None  ,					**_A		:						int  ,					)  ->			Dict:
			if "text_queries" in kwargs:
				__magic_name__	:   int					     =					kwargs.pop('text_queries'      )

			if isinstance(__lowerCamelCase  ,					(str, Image.Image)      ):
				__magic_name__	:   List[Any]					     =					{'image': image, 'candidate_labels': candidate_labels}
			else:
				__magic_name__	:   Any					     =					image
			__magic_name__	:   Tuple					     =					super().__call__(__lowerCamelCase  ,					**__lowerCamelCase      )
			return results







		def        __lowerCAmelCase		( self		:						Optional[Any]  ,					**_A		:						List[Any]      )  ->			List[str]:
			__magic_name__	:   int					     =					{}
			if "threshold" in kwargs:
				__magic_name__	:   str					     =					kwargs['threshold']
			if "top_k" in kwargs:
				__magic_name__	:   List[Any]					     =					kwargs['top_k']
			return {}, {}, postprocess_params







		def        __lowerCAmelCase		( self		:						List[Any]  ,					_A		:						Any      )  ->			Tuple:
			__magic_name__	:   Union[str, Any]					     =					load_image(inputs['image']      )
			__magic_name__	:   List[str]					     =					inputs['candidate_labels']
			if isinstance(__lowerCamelCase  ,					__lowerCamelCase      ):
				__magic_name__	:   Dict					     =					candidate_labels.split(','      )

			__magic_name__	:   Union[str, Any]					     =					torch.tensor([[image.height, image.width]]  ,					dtype=torch.intaa      )
			for i, candidate_label in enumerate(__lowerCamelCase      ):
				__magic_name__	:   List[Any]					     =					self.tokenizer(__lowerCamelCase  ,					return_tensors=self.framework      )
				__magic_name__	:   Union[str, Any]					     =					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        __lowerCAmelCase		( self		:						Dict  ,					_A		:						Union[str, Any]      )  ->			int:
			__magic_name__	:   List[str]					     =					model_inputs.pop('target_size'      )
			__magic_name__	:   List[str]					     =					model_inputs.pop('candidate_label'      )
			__magic_name__	:   str					     =					model_inputs.pop('is_last'      )

			__magic_name__	:   Tuple					     =					self.model(**__lowerCamelCase      )

			__magic_name__	:   Optional[int]					     =					{'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
			return model_outputs







		def        __lowerCAmelCase		( self		:						List[str]  ,					_A		:						int  ,					_A		:						List[str]=0.1  ,					_A		:						Any=None      )  ->			List[Any]:
			__magic_name__	:   Optional[Any]					     =					[]
			for model_output in model_outputs:
				__magic_name__	:   Optional[Any]					     =					model_output['candidate_label']
				__magic_name__	:   Dict					     =					BaseModelOutput(__lowerCamelCase      )
				__magic_name__	:   Dict					     =					self.image_processor.post_process_object_detection(
				    outputs=__lowerCamelCase  ,					threshold=__lowerCamelCase  ,					target_sizes=model_output['target_size']      )[0]

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

					__magic_name__	:   List[str]					     =					{'score': score, 'label': label, 'box': box}
					results.append(__lowerCamelCase      )

			__magic_name__	:   Dict					     =					sorted(__lowerCamelCase  ,					key=lambda _A      : x["score"]  ,					reverse=__lowerCamelCase      )
			if top_k:
				__magic_name__	:   Any					     =					results[:top_k]

			return results







		def        __lowerCAmelCase		( self		:						List[str]  ,					_A		:						Any      )  ->			Any:
			if self.framework != "pt":
				raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.'      )
			__magic_name__     ,    __magic_name__     ,    __magic_name__     ,    __magic_name__	:   List[str]					     =					box.int().tolist()
			__magic_name__	:   Dict					     =					{
			    'xmin': xmin,
			    'ymin': ymin,
			    'xmax': xmax,
			    'ymax': ymax,
			}
			return bbox



'''simple docstring'''

def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	if not isinstance(lowerCAmelCase		,		lowerCAmelCase		):
		__magic_name__	:   int					     =					f'Input value of [number={number}] must be an integer'
		raise TypeError(lowerCAmelCase		)
	if number < 0:
		return False
	__magic_name__	:   Tuple					     =					number * number
	while number > 0:
		if number % 10 != number_square % 10:
			return False
		number //= 10
		number_square //= 10
	return True


if __name__ == "__main__":
							import doctest

							doctest.testmod()