



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(lowercase__  )
class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					def __init__(			self    ,	**lowerCAmelCase     ):
							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    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	**lowerCAmelCase    ,	):
							if "text_queries" in kwargs:
									UpperCAmelCase_						=				kwargs.pop("text_queries"     )

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



					def    A__		(			self    ,	**lowerCAmelCase     ):
							UpperCAmelCase_						=				{}
							if "threshold" in kwargs:
									UpperCAmelCase_						=				kwargs["threshold"]
							if "top_k" in kwargs:
									UpperCAmelCase_						=				kwargs["top_k"]
							return {}, {}, postprocess_params



					def    A__		(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				load_image(inputs["image"]     )
							UpperCAmelCase_						=				inputs["candidate_labels"]
							if isinstance(lowerCAmelCase    ,	lowerCAmelCase     ):
									UpperCAmelCase_						=				candidate_labels.split(","     )

							UpperCAmelCase_						=				torch.tensor([[image.height, image.width]]    ,	dtype=torch.intaa     )
							for i, candidate_label in enumerate(lowerCAmelCase     ):
									UpperCAmelCase_						=				self.tokenizer(lowerCAmelCase    ,	return_tensors=self.framework     )
									UpperCAmelCase_						=				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    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				model_inputs.pop("target_size"     )
							UpperCAmelCase_						=				model_inputs.pop("candidate_label"     )
							UpperCAmelCase_						=				model_inputs.pop("is_last"     )

							UpperCAmelCase_						=				self.model(**lowerCAmelCase     )

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



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase=0.1    ,	lowerCAmelCase=None     ):
							UpperCAmelCase_						=				[]
							for model_output in model_outputs:
									UpperCAmelCase_						=				model_output["candidate_label"]
									UpperCAmelCase_						=				BaseModelOutput(lowerCAmelCase     )
									UpperCAmelCase_						=				self.image_processor.post_process_object_detection(
									    outputs=lowerCAmelCase    ,	threshold=lowerCAmelCase    ,	target_sizes=model_output["target_size"]     )[0]

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

											UpperCAmelCase_						=				{"score": score, "label": label, "box": box}
											results.append(lowerCAmelCase     )

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

							return results



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






def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							str:
		UpperCAmelCase_						=				int(__SCREAMING_SNAKE_CASE )
		if decimal in (0, 1):  # Exit cases for the recursion
				return str(__SCREAMING_SNAKE_CASE )
		UpperCAmelCase_					,						UpperCAmelCase_						=				divmod(__SCREAMING_SNAKE_CASE			,     2 )
		return binary_recursive(__SCREAMING_SNAKE_CASE ) + str(__SCREAMING_SNAKE_CASE )







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							str:
		UpperCAmelCase_						=				str(__SCREAMING_SNAKE_CASE ).strip()
		if not number:
				raise ValueError("No input value was provided" )
		UpperCAmelCase_						=				"-" if number.startswith("-" ) else ""
		UpperCAmelCase_						=				number.lstrip("-" )
		if not number.isnumeric():
				raise ValueError("Input value is not an integer" )
		return f'''{negative}0b{binary_recursive(int(__SCREAMING_SNAKE_CASE ) )}'''


if __name__ == "__main__":
			from doctest import testmod

			testmod()

