
'''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    					=   logging.get_logger(__name__)

@add_end_docstrings(__magic_name__    )
class 						a   (				__magic_name__    ):




				def __init__(							self	:				Union[str, Any],    **SCREAMING_SNAKE_CASE_	:				Tuple      ):
										super().__init__(**SCREAMING_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(SCREAMING_SNAKE_CASE_      )




				def __call__(							self	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				Union[str, "Image.Image", List[Dict[str, Any]]],    SCREAMING_SNAKE_CASE_	:				Union[str, List[str]] = None,    **SCREAMING_SNAKE_CASE_	:				str,    ):
										if "text_queries" in kwargs:
																snake_case       :     Optional[Any] =    kwargs.pop('''text_queries'''      )

										if isinstance(SCREAMING_SNAKE_CASE_,    (str, Image.Image)      ):
																snake_case       :     int =    {'''image''': image, '''candidate_labels''': candidate_labels}
										else:
																snake_case       :     Optional[Any] =    image
										snake_case       :     str =    super().__call__(SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )
										return results




				def 	__snake_case							(							self	:				Any,    **SCREAMING_SNAKE_CASE_	:				str      ):
										snake_case       :     Tuple =    {}
										if "threshold" in kwargs:
																snake_case       :     Tuple =    kwargs['''threshold''']
										if "top_k" in kwargs:
																snake_case       :     List[Any] =    kwargs['''top_k''']
										return {}, {}, postprocess_params




				def 	__snake_case							(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				Dict      ):
										snake_case       :     Any =    load_image(inputs['''image''']      )
										snake_case       :     Optional[Any] =    inputs['''candidate_labels''']
										if isinstance(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ):
																snake_case       :     Union[str, Any] =    candidate_labels.split(''','''      )

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




				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				List[Any]      ):
										snake_case       :     List[Any] =    model_inputs.pop('''target_size'''      )
										snake_case       :     List[Any] =    model_inputs.pop('''candidate_label'''      )
										snake_case       :     Optional[int] =    model_inputs.pop('''is_last'''      )

										snake_case       :     Dict =    self.model(**SCREAMING_SNAKE_CASE_      )

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




				def 	__snake_case							(							self	:				int,    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				Tuple=0.1,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=None      ):
										snake_case       :     Any =    []
										for model_output in model_outputs:
																snake_case       :     int =    model_output['''candidate_label''']
																snake_case       :     Union[str, Any] =    BaseModelOutput(SCREAMING_SNAKE_CASE_      )
																snake_case       :     Tuple =    self.image_processor.post_process_object_detection(
																    outputs=SCREAMING_SNAKE_CASE_,    threshold=SCREAMING_SNAKE_CASE_,    target_sizes=model_output['''target_size''']      )[0]

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

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

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

										return results




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



'''simple docstring'''





from __future__ import annotations

def      A (       A_							:					list[int]    ,							A_							:					int       ):
						snake_case       :     list[list[int]] =    []
						snake_case       :     list[int] =    []
						snake_case       :     int =    0
						snake_case       :     int =    sum(A_       )
						create_state_space_tree(A_    ,							A_    ,							A_    ,							A_    ,							A_    ,							A_       )
						return result

def      A (       A_							:					list[int]    ,							A_							:					int    ,							A_							:					int    ,							A_							:					list[int]    ,							A_							:					list[list[int]]    ,							A_							:					int    ,							):
						if sum(A_       ) > max_sum or (remaining_nums_sum + sum(A_       )) < max_sum:
												return
						if sum(A_       ) == max_sum:
												result.append(A_       )
												return
						for index in range(A_    ,							len(A_       )       ):
												create_state_space_tree(
												    A_    ,							A_    ,							index + 1    ,							[*path, nums[index]]    ,							A_    ,							remaining_nums_sum - nums[index]    ,							)
UpperCAmelCase    					=   [3, 34, 4, 12, 5, 2]
UpperCAmelCase    					=   9
UpperCAmelCase    					=   generate_sum_of_subsets_soln(nums, max_sum)





print(*result)

