




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

__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)






@add_end_docstrings(__UpperCAmelCase)
class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				def __init__(							self:   Tuple		,					**_lowerCamelCase:   Any		):
											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[Any]		,					):
											if "text_queries" in kwargs:
																		SCREAMING_SNAKE_CASE_   	=    kwargs.pop('''text_queries'''		)

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


				def     _A							(							self:   List[Any]		,					**_lowerCamelCase:   Dict		):
											SCREAMING_SNAKE_CASE_   	=    {}
											if "threshold" in kwargs:
																		SCREAMING_SNAKE_CASE_   	=    kwargs['''threshold''']
											if "top_k" in kwargs:
																		SCREAMING_SNAKE_CASE_   	=    kwargs['''top_k''']
											return {}, {}, postprocess_params


				def     _A							(							self:   str		,					_lowerCamelCase:   List[str]		):
											SCREAMING_SNAKE_CASE_   	=    load_image(inputs['''image''']		)
											SCREAMING_SNAKE_CASE_   	=    inputs['''candidate_labels''']
											if isinstance(_lowerCamelCase		,					_lowerCamelCase		):
																		SCREAMING_SNAKE_CASE_   	=    candidate_labels.split(''','''		)

											SCREAMING_SNAKE_CASE_   	=    torch.tensor([[image.height, image.width]]		,					dtype=torch.intaa		)
											for i, candidate_label in enumerate(_lowerCamelCase		):
																		SCREAMING_SNAKE_CASE_   	=    self.tokenizer(_lowerCamelCase		,					return_tensors=self.framework		)
																		SCREAMING_SNAKE_CASE_   	=    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     _A							(							self:   str		,					_lowerCamelCase:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    model_inputs.pop('''target_size'''		)
											SCREAMING_SNAKE_CASE_   	=    model_inputs.pop('''candidate_label'''		)
											SCREAMING_SNAKE_CASE_   	=    model_inputs.pop('''is_last'''		)

											SCREAMING_SNAKE_CASE_   	=    self.model(**_lowerCamelCase		)

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


				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   Dict		,					_lowerCamelCase:   Dict=0.1		,					_lowerCamelCase:   List[Any]=None		):
											SCREAMING_SNAKE_CASE_   	=    []
											for model_output in model_outputs:
																		SCREAMING_SNAKE_CASE_   	=    model_output['''candidate_label''']
																		SCREAMING_SNAKE_CASE_   	=    BaseModelOutput(_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    self.image_processor.post_process_object_detection(
																		    outputs=_lowerCamelCase		,					threshold=_lowerCamelCase		,					target_sizes=model_output['''target_size''']		)[0]

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

																									SCREAMING_SNAKE_CASE_   	=    {'''score''': score, '''label''': label, '''box''': box}
																									results.append(_lowerCamelCase		)

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

											return results





				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   "torch.Tensor"		):
											if self.framework != "pt":
																		raise ValueError('''The ZeroShotObjectDetectionPipeline is only available in PyTorch.'''		)
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    box.int().tolist()
											SCREAMING_SNAKE_CASE_   	=    {
											    '''xmin''': xmin,
											    '''ymin''': ymin,
											    '''xmax''': xmax,
											    '''ymax''': ymax,
											}
											return bbox








def 			a   (_lowerCAmelCase     ):
							if number > 0:
														raise ValueError('''input must be a negative integer'''     )
							SCREAMING_SNAKE_CASE_   	=    len(bin(_lowerCAmelCase     )[3:]     )
							SCREAMING_SNAKE_CASE_   	=    bin(abs(_lowerCAmelCase     ) - (1 << binary_number_length)     )[3:]
							SCREAMING_SNAKE_CASE_   	=    (
							    (
							        '''1'''
							        + '''0''' * (binary_number_length - len(_lowerCAmelCase     ))
							        + twos_complement_number
							    )
							    if number < 0
							    else '''0'''
							)
							return "0b" + twos_complement_number


if __name__ == "__main__":
			import doctest

			doctest.testmod()


