

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__												=						logging.get_logger(__name__)

@add_end_docstrings(__lowercase  )
class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							def __init__(     self			,				**_a			)       ->       List[str]:
												super().__init__(**_a			)

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

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


							def __call__(     self			,				_a			,				_a = None			,				**_a			,				)       ->       List[str]:
												if "text_queries" in kwargs:
																	_a      :       Optional[Any]   =						kwargs.pop('''text_queries'''			)

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


							def 							__lowercase							(     self			,				**_a			)       ->       Union[str, Any]:
												_a      :       Union[str, Any]   =						{}
												if "threshold" in kwargs:
																	_a      :       Dict   =						kwargs['''threshold''']
												if "top_k" in kwargs:
																	_a      :       Tuple   =						kwargs['''top_k''']
												return {}, {}, postprocess_params


							def 							__lowercase							(     self			,				_a			)       ->       List[str]:
												_a      :       int   =						load_image(inputs['''image''']			)
												_a      :       Union[str, Any]   =						inputs['''candidate_labels''']
												if isinstance(_a			,				_a			):
																	_a      :       Tuple   =						candidate_labels.split(''','''			)

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


							def 							__lowercase							(     self			,				_a			)       ->       str:
												_a      :       Optional[int]   =						model_inputs.pop('''target_size'''			)
												_a      :       int   =						model_inputs.pop('''candidate_label'''			)
												_a      :       str   =						model_inputs.pop('''is_last'''			)

												_a      :       Union[str, Any]   =						self.model(**_a			)

												_a      :       List[str]   =						{'''target_size''': target_size, '''candidate_label''': candidate_label, '''is_last''': is_last, **outputs}
												return model_outputs


							def 							__lowercase							(     self			,				_a			,				_a=0.1			,				_a=None			)       ->       Any:
												_a      :       str   =						[]
												for model_output in model_outputs:
																	_a      :       str   =						model_output['''candidate_label''']
																	_a      :       int   =						BaseModelOutput(_a			)
																	_a      :       Tuple   =						self.image_processor.post_process_object_detection(
																	    outputs=_a			,				threshold=_a			,				target_sizes=model_output['''target_size''']			)[0]

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

																						_a      :       Dict   =						{'''score''': score, '''label''': label, '''box''': box}
																						results.append(_a			)

												_a      :       str   =						sorted(_a			,				key=lambda _a			: x["score"]			,				reverse=_a			)
												if top_k:
																	_a      :       Any   =						results[:top_k]

												return results




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






class 							UpperCAmelCase_						:
							"""simple docstring"""







							def __init__(     self			,				_a			,				_a			,				_a			)       ->       List[str]:
												_a      :       List[Any]   =						name
												_a      :       List[str]   =						value
												_a      :       List[str]   =						weight


							def __repr__(     self			)       ->       Optional[int]:
												return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""


							def 							__lowercase							(     self			)       ->       List[Any]:
												return self.value


							def 							__lowercase							(     self			)       ->       int:
												return self.name


							def 							__lowercase							(     self			)       ->       Optional[int]:
												return self.weight




							def 							__lowercase							(     self			)       ->       Optional[Any]:
												return self.value / self.weight



def   __UpperCAmelCase					(      __a :     Optional[int]						,__a :     Tuple						,__a :     List[str]      )     ->	List[str]:

					"""simple docstring"""

					_a      :       Optional[int]   =						[]
					for i in range(len(__a      )      ):
										menu.append(Things(name[i]						,value[i]						,weight[i]      )      )
					return menu



def   __UpperCAmelCase					(      __a :     int						,__a :     Union[str, Any]						,__a :     int      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       Union[str, Any]   =						sorted(__a						,key=__a						,reverse=__a      )
					_a      :       Any   =						[]
					_a ,   _a      :       Optional[int]   =						0.0, 0.0
					for i in range(len(__a      )      ):
										if (total_cost + items_copy[i].get_weight()) <= max_cost:
															result.append(items_copy[i]      )
															total_cost += items_copy[i].get_weight()
															total_value += items_copy[i].get_value()
					return (result, total_value)



def   __UpperCAmelCase					(      )     ->	int:

					"""simple docstring"""



if __name__ == "__main__":
			import doctest

			doctest.testmod()



