






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 __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	def __init__(							self       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							super().__init__(**UpperCAmelCase    )
							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(UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    **UpperCAmelCase    ):
							"""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       ,    UpperCAmelCase       ,    *UpperCAmelCase       ,    UpperCAmelCase=None       ,    UpperCAmelCase=None       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							return super().__call__(UpperCAmelCase       ,    *UpperCAmelCase       ,    num_workers=UpperCAmelCase       ,    batch_size=UpperCAmelCase       ,    **UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase=64       ,    UpperCAmelCase = 0       ,    UpperCAmelCase = 512 / 1500       ,    UpperCAmelCase = 32       ,    UpperCAmelCase = 1       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			load_image(UpperCAmelCase    )
							_UpperCAmelCase									=			self.image_processor.size['longest_edge']
							_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			self.image_processor.generate_crop_boxes(
							    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    )
							_UpperCAmelCase									=			self.image_processor(images=UpperCAmelCase       ,    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(UpperCAmelCase       ,    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       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
													_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       ,    UpperCAmelCase       ,    UpperCAmelCase=0.88       ,    UpperCAmelCase=0.95       ,    UpperCAmelCase=0       ,    UpperCAmelCase=1       ,    ):
							"""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(**UpperCAmelCase    )

							# 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(
							    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    binarize=UpperCAmelCase    )
							_UpperCAmelCase									=			model_outputs['iou_scores']
							_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			self.image_processor.filter_masks(
							    masks[0]       ,    iou_scores[0]       ,    original_sizes[0]       ,    input_boxes[0]       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    )
							return {
							    "masks": masks,
							    "is_last": is_last,
							    "boxes": boxes,
							    "iou_scores": iou_scores,
							}

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase=False       ,    UpperCAmelCase=False       ,    UpperCAmelCase=0.7       ,    ):
							"""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(UpperCAmelCase    )
							_UpperCAmelCase									=			torch.cat(UpperCAmelCase    )
							_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			self.image_processor.post_process_for_mask_generation(
							    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    )

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

							_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]}.''')
