


'''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

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


@add_end_docstrings(SCREAMING_SNAKE_CASE__      )
class       a__				( SCREAMING_SNAKE_CASE__      ):







				"""simple docstring"""



				def __init__(self       ,			**__lowercase		):
										super().__init__(**snake_case__		)

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

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




				def __call__(self       ,			__lowercase       ,			__lowercase = None       ,			**__lowercase       ,			):
										if "text_queries" in kwargs:
																__lowerCAmelCase							       =				kwargs.pop('''text_queries'''		)

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




				def 					lowercase  (self       ,			**__lowercase		):
										__lowerCAmelCase							       =				{}
										if "threshold" in kwargs:
																__lowerCAmelCase							       =				kwargs["threshold"]
										if "top_k" in kwargs:
																__lowerCAmelCase							       =				kwargs["top_k"]
										return {}, {}, postprocess_params




				def 					lowercase  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				load_image(inputs['''image''']		)
										__lowerCAmelCase							       =				inputs["candidate_labels"]
										if isinstance(snake_case__       ,			snake_case__		):
																__lowerCAmelCase							       =				candidate_labels.split(''','''		)

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




				def 					lowercase  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				model_inputs.pop('''target_size'''		)
										__lowerCAmelCase							       =				model_inputs.pop('''candidate_label'''		)
										__lowerCAmelCase							       =				model_inputs.pop('''is_last'''		)

										__lowerCAmelCase							       =				self.model(**snake_case__		)

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




				def 					lowercase  (self       ,			__lowercase       ,			__lowercase=0.1       ,			__lowercase=None		):
										__lowerCAmelCase							       =				[]
										for model_output in model_outputs:
																__lowerCAmelCase							       =				model_output["candidate_label"]
																__lowerCAmelCase							       =				BaseModelOutput(snake_case__		)
																__lowerCAmelCase							       =				self.image_processor.post_process_object_detection(
																    outputs=snake_case__       ,			threshold=snake_case__       ,			target_sizes=model_output['''target_size''']		)[0]

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

																						__lowerCAmelCase							       =				{"score": score, "label": label, "box": box}
																						results.append(snake_case__		)

										__lowerCAmelCase							       =				sorted(snake_case__       ,			key=lambda __lowercase		: x["score"]       ,			reverse=snake_case__		)
										if top_k:
																__lowerCAmelCase							       =				results[:top_k]

										return results




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





'''simple docstring'''

from __future__ import annotations

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				sorted(numsa + numsa)
						__lowerCAmelCase       ,					__lowerCAmelCase							       =				divmod(len(lowerCamelCase),				2)
						if mod == 1:
												return all_numbers[div]
						else:
												return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	_UpperCAmelCase				: Union[str, Any]							    =				[float(x) for x in input("""Enter the elements of first array: """).split()]
	_UpperCAmelCase				: int							    =				[float(x) for x in input("""Enter the elements of second array: """).split()]
	print(f"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")

