
"""simple docstring"""





from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
				import torch

				from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

_a														=		logging.get_logger(__name__)






@add_end_docstrings(snake_case__	)
class     _UpperCAmelCase(    snake_case__	):



		def __init__(					self				,		**__a)       ->       List[Any]:






							'''simple docstring'''
							super().__init__(**__a)
							requires_backends(self				,		'''vision''')
							requires_backends(self				,		'''torch''')

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

							self.check_model_type(__a)



		def 						UpperCAmelCase							(					self				,		**__a)       ->       Tuple:






							'''simple docstring'''
							_UpperCamelCase							 =  {}
							_UpperCamelCase							 =  {}
							_UpperCamelCase							 =  {}
							# preprocess args
							if "points_per_batch" in kwargs:
												_UpperCamelCase							 =  kwargs['''points_per_batch''']
							if "points_per_crop" in kwargs:
												_UpperCamelCase							 =  kwargs['''points_per_crop''']
							if "crops_n_layers" in kwargs:
												_UpperCamelCase							 =  kwargs['''crops_n_layers''']
							if "crop_overlap_ratio" in kwargs:
												_UpperCamelCase							 =  kwargs['''crop_overlap_ratio''']
							if "crop_n_points_downscale_factor" in kwargs:
												_UpperCamelCase							 =  kwargs['''crop_n_points_downscale_factor''']
							# postprocess args
							if "pred_iou_thresh" in kwargs:
												_UpperCamelCase							 =  kwargs['''pred_iou_thresh''']
							if "stability_score_offset" in kwargs:
												_UpperCamelCase							 =  kwargs['''stability_score_offset''']
							if "mask_threshold" in kwargs:
												_UpperCamelCase							 =  kwargs['''mask_threshold''']
							if "stability_score_thresh" in kwargs:
												_UpperCamelCase							 =  kwargs['''stability_score_thresh''']
							if "crops_nms_thresh" in kwargs:
												_UpperCamelCase							 =  kwargs['''crops_nms_thresh''']
							if "output_rle_mask" in kwargs:
												_UpperCamelCase							 =  kwargs['''output_rle_mask''']
							if "output_bboxes_mask" in kwargs:
												_UpperCamelCase							 =  kwargs['''output_bboxes_mask''']
							return preprocess_kwargs, forward_params, postprocess_kwargs



		def __call__(					self				,		__a				,		*__a				,		__a=None				,		__a=None				,		**__a)       ->       int:






							'''simple docstring'''
							return super().__call__(__a				,		*__a				,		num_workers=__a				,		batch_size=__a				,		**__a)



		def 						UpperCAmelCase							(					self				,		__a				,		__a=64				,		__a = 0				,		__a = 5_12 / 15_00				,		__a = 32				,		__a = 1				,		)       ->       List[Any]:






							'''simple docstring'''
							_UpperCamelCase							 =  load_image(__a)
							_UpperCamelCase							 =  self.image_processor.size['''longest_edge''']
							_UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase							 =  self.image_processor.generate_crop_boxes(
							    __a				,		__a				,		__a				,		__a				,		__a				,		__a)
							_UpperCamelCase							 =  self.image_processor(images=__a				,		return_tensors='''pt''')

							with self.device_placement():
												if self.framework == "pt":
																	_UpperCamelCase							 =  self.get_inference_context()
																	with inference_context():
																						_UpperCamelCase							 =  self._ensure_tensor_on_device(__a				,		device=self.device)
																						_UpperCamelCase							 =  self.model.get_image_embeddings(model_inputs.pop('''pixel_values'''))
																						_UpperCamelCase							 =  image_embeddings

							_UpperCamelCase							 =  grid_points.shape[1]
							_UpperCamelCase							 =  points_per_batch if points_per_batch is not None else n_points

							if points_per_batch <= 0:
												raise ValueError(
												    '''Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. '''
												    '''To return all points at once, set points_per_batch to None''')

							for i in range(0				,		__a				,		__a):
												_UpperCamelCase							 =  grid_points[:, i : i + points_per_batch, :, :]
												_UpperCamelCase							 =  input_labels[:, i : i + points_per_batch]
												_UpperCamelCase							 =  i == n_points - points_per_batch
												yield {
												    "input_points": batched_points,
												    "input_labels": labels,
												    "input_boxes": crop_boxes,
												    "is_last": is_last,
												    **model_inputs,
												}



		def 						UpperCAmelCase							(					self				,		__a				,		__a=0.88				,		__a=0.95				,		__a=0				,		__a=1				,		)       ->       str:






							'''simple docstring'''
							_UpperCamelCase							 =  model_inputs.pop('''input_boxes''')
							_UpperCamelCase							 =  model_inputs.pop('''is_last''')
							_UpperCamelCase							 =  model_inputs.pop('''original_sizes''').tolist()
							_UpperCamelCase							 =  model_inputs.pop('''reshaped_input_sizes''').tolist()

							_UpperCamelCase							 =  self.model(**__a)

							# post processing happens here in order to avoid CPU GPU copies of ALL the masks
							_UpperCamelCase							 =  model_outputs['''pred_masks''']
							_UpperCamelCase							 =  self.image_processor.post_process_masks(
							    __a				,		__a				,		__a				,		__a				,		binarize=__a)
							_UpperCamelCase							 =  model_outputs['''iou_scores''']
							_UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase							 =  self.image_processor.filter_masks(
							    masks[0]				,		iou_scores[0]				,		original_sizes[0]				,		input_boxes[0]				,		__a				,		__a				,		__a				,		__a				,		)
							return {
							    "masks": masks,
							    "is_last": is_last,
							    "boxes": boxes,
							    "iou_scores": iou_scores,
							}




		def 						UpperCAmelCase							(					self				,		__a				,		__a=False				,		__a=False				,		__a=0.7				,		)       ->       List[Any]:






							'''simple docstring'''
							_UpperCamelCase							 =  []
							_UpperCamelCase							 =  []
							_UpperCamelCase							 =  []
							for model_output in model_outputs:
												all_scores.append(model_output.pop('''iou_scores'''))
												all_masks.extend(model_output.pop('''masks'''))
												all_boxes.append(model_output.pop('''boxes'''))

							_UpperCamelCase							 =  torch.cat(__a)
							_UpperCamelCase							 =  torch.cat(__a)
							_UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase							 =  self.image_processor.post_process_for_mask_generation(
							    __a				,		__a				,		__a				,		__a)

							_UpperCamelCase							 =  defaultdict(__a)
							for output in model_outputs:
												for k, v in output.items():
																	extra[k].append(__a)

							_UpperCamelCase							 =  {}
							if output_rle_mask:
												_UpperCamelCase							 =  rle_mask

							if output_bboxes_mask:
												_UpperCamelCase							 =  bounding_boxes

							return {"masks": output_masks, "scores": iou_scores, **optional, **extra}














from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat


def __A    (      )->  tuple[list[int], int]:





						"""simple docstring"""



						_UpperCAmelCase									=			[randint(-1_000 ,		1_000	) for i in range(10	)]
						_UpperCAmelCase									=			randint(-5_000 ,		5_000	)
						return (arr, r)


_a      	=  make_dataset()


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  tuple[int, ...]:





						"""simple docstring"""



						for triplet in permutations(__lowerCAmelCase ,		3	):
												if sum(__lowerCAmelCase	) == target:
																		return tuple(sorted(__lowerCAmelCase	)	)
						return (0, 0, 0)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  tuple[int, int, int]:





						"""simple docstring"""



						arr.sort()
						_UpperCAmelCase									=			len(__lowerCAmelCase	)
						for i in range(n - 1	):
												_UpperCAmelCase					, _UpperCAmelCase									=			i + 1, n - 1
												while left < right:
																		if arr[i] + arr[left] + arr[right] == target:
																								return (arr[i], arr[left], arr[right])
																		elif arr[i] + arr[left] + arr[right] < target:
																								left += 1
																		elif arr[i] + arr[left] + arr[right] > target:
																								right -= 1
						return (0, 0, 0)


def __A    (      )->  tuple[float, float]:





						"""simple docstring"""



						_UpperCAmelCase									=			'\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n'
						_UpperCAmelCase									=			'\ntriplet_sum1(*dataset)\n'
						_UpperCAmelCase									=			'\ntriplet_sum2(*dataset)\n'
						_UpperCAmelCase									=			repeat(setup=__lowerCAmelCase ,		stmt=__lowerCAmelCase ,		repeat=5 ,		number=10_000	)
						_UpperCAmelCase									=			repeat(setup=__lowerCAmelCase ,		stmt=__lowerCAmelCase ,		repeat=5 ,		number=10_000	)
						return (min(__lowerCAmelCase	), min(__lowerCAmelCase	))


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

		testmod()
		_a      	=  solution_times()
		print(F'''The time for naive implementation is {times[0]}.''')
		print(F'''The time for optimized implementation is {times[1]}.''')
