






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							:					Union[str, Any]     						=     logging.get_logger(__name__)






@add_end_docstrings(__a   )
class     UpperCamelCase(  __a   ):


					def __init__(						self	:		Any     ,    **SCREAMING_SNAKE_CASE	:		Tuple						)			->	List[str]:




											'''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 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]     ,    **SCREAMING_SNAKE_CASE	:		str						)			->	List[Any]:




											'''simple docstring'''

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


					def __call__(						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    *SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		int=None     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=None     ,    **SCREAMING_SNAKE_CASE	:		Optional[Any]						)			->	Tuple:




											'''simple docstring'''

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		Any=6_4     ,    SCREAMING_SNAKE_CASE	:		int = 0     ,    SCREAMING_SNAKE_CASE	:		float = 5_1_2 / 1_5_0_0     ,    SCREAMING_SNAKE_CASE	:		Optional[int] = 3_2     ,    SCREAMING_SNAKE_CASE	:		Optional[int] = 1     ,    )			->	int:




											'''simple docstring'''

											__snake_case				=		load_image(a_						)
											__snake_case				=		self.image_processor.size["""longest_edge"""]
											__snake_case				=		self.image_processor.generate_crop_boxes(
											    a_     ,    a_     ,    a_     ,    a_     ,    a_     ,    a_						)
											__snake_case				=		self.image_processor(images=a_     ,    return_tensors="pt"						)

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

											__snake_case				=		grid_points.shape[1]
											__snake_case				=		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_						):
																	__snake_case				=		grid_points[:, i : i + points_per_batch, :, :]
																	__snake_case				=		input_labels[:, i : i + points_per_batch]
																	__snake_case				=		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 							SCREAMING_SNAKE_CASE_  (						self	:		str     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=0.88     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=0.95     ,    SCREAMING_SNAKE_CASE	:		Dict=0     ,    SCREAMING_SNAKE_CASE	:		Any=1     ,    )			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		model_inputs.pop("input_boxes"						)
											__snake_case				=		model_inputs.pop("is_last"						)
											__snake_case				=		model_inputs.pop("original_sizes"						).tolist()
											__snake_case				=		model_inputs.pop("reshaped_input_sizes"						).tolist()

											__snake_case				=		self.model(**a_						)

											# post processing happens here in order to avoid CPU GPU copies of ALL the masks
											__snake_case				=		model_outputs["""pred_masks"""]
											__snake_case				=		self.image_processor.post_process_masks(
											    a_     ,    a_     ,    a_     ,    a_     ,    binarize=a_						)
											__snake_case				=		model_outputs["""iou_scores"""]
											__snake_case				=		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 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=False     ,    SCREAMING_SNAKE_CASE	:		int=False     ,    SCREAMING_SNAKE_CASE	:		Tuple=0.7     ,    )			->	Any:




											'''simple docstring'''

											__snake_case				=		[]
											__snake_case				=		[]
											__snake_case				=		[]
											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"						)						)

											__snake_case				=		torch.cat(a_						)
											__snake_case				=		torch.cat(a_						)
											__snake_case				=		self.image_processor.post_process_for_mask_generation(
											    a_     ,    a_     ,    a_     ,    a_						)

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

											__snake_case				=		{}
											if output_rle_mask:
																	__snake_case				=		rle_mask

											if output_bboxes_mask:
																	__snake_case				=		bounding_boxes

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









def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	int:


						'''simple docstring'''






						__snake_case				=		abs(_lowerCAmelCase )
						__snake_case				=		0
						while n > 0:
												res += n % 10
												n //= 10
						return res





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	int:


						'''simple docstring'''






						__snake_case				=		abs(_lowerCAmelCase )
						return n if n < 10 else n % 10 + sum_of_digits(n // 10 )





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	int:


						'''simple docstring'''






						return sum(int(_lowerCAmelCase ) for c in str(abs(_lowerCAmelCase ) ) )





def 	_lowerCAmelCase       (    )						->	None:


						'''simple docstring'''






						from collections.abc import Callable
						from timeit import timeit

						def benchmark_a_function(_lowerCAmelCase      ,	_lowerCAmelCase ) -> None:
												__snake_case				=		F'''{func.__name__}({value})'''
												__snake_case				=		timeit(F'''__main__.{call}'''      ,	setup="import __main__" )
												print(F'''{call:56} = {func(_lowerCAmelCase )} -- {timing:.4f} seconds''' )

						for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
												for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
																		benchmark_a_function(_lowerCAmelCase      ,	_lowerCAmelCase )
												print()


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()





