





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

__UpperCamelCase:	str = logging.get_logger(__name__)


@add_end_docstrings(_UpperCamelCase      )
class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	def __init__(						self:						int,					**lowerCamelCase_:						str			):
			super().__init__(**lowerCamelCase_			)
			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(lowerCamelCase_			)

	def    snake_case__(						self:						int,					**lowerCamelCase_:						Dict			):
			lowercase__					:      List[str]      =		{}
			lowercase__					:      Optional[int]      =		{}
			lowercase__					:      Optional[int]      =		{}
			# preprocess args
			if "points_per_batch" in kwargs:
					lowercase__					:      List[Any]      =		kwargs['points_per_batch']
			if "points_per_crop" in kwargs:
					lowercase__					:      int      =		kwargs['points_per_crop']
			if "crops_n_layers" in kwargs:
					lowercase__					:      int      =		kwargs['crops_n_layers']
			if "crop_overlap_ratio" in kwargs:
					lowercase__					:      Any      =		kwargs['crop_overlap_ratio']
			if "crop_n_points_downscale_factor" in kwargs:
					lowercase__					:      Dict      =		kwargs['crop_n_points_downscale_factor']
			# postprocess args
			if "pred_iou_thresh" in kwargs:
					lowercase__					:      Dict      =		kwargs['pred_iou_thresh']
			if "stability_score_offset" in kwargs:
					lowercase__					:      int      =		kwargs['stability_score_offset']
			if "mask_threshold" in kwargs:
					lowercase__					:      Optional[Any]      =		kwargs['mask_threshold']
			if "stability_score_thresh" in kwargs:
					lowercase__					:      List[Any]      =		kwargs['stability_score_thresh']
			if "crops_nms_thresh" in kwargs:
					lowercase__					:      Any      =		kwargs['crops_nms_thresh']
			if "output_rle_mask" in kwargs:
					lowercase__					:      List[str]      =		kwargs['output_rle_mask']
			if "output_bboxes_mask" in kwargs:
					lowercase__					:      str      =		kwargs['output_bboxes_mask']
			return preprocess_kwargs, forward_params, postprocess_kwargs

	def __call__(						self:						Any,					lowerCamelCase_:						Optional[int],					*lowerCamelCase_:						List[Any],					lowerCamelCase_:						Optional[Any]=None,					lowerCamelCase_:						List[str]=None,					**lowerCamelCase_:						Any			):
			return super().__call__(lowerCamelCase_,					*lowerCamelCase_,					num_workers=lowerCamelCase_,					batch_size=lowerCamelCase_,					**lowerCamelCase_			)

	def    snake_case__(						self:						Optional[int],					lowerCamelCase_:						List[str],					lowerCamelCase_:						Dict=64,					lowerCamelCase_:						int = 0,					lowerCamelCase_:						float = 512 / 1500,					lowerCamelCase_:						Optional[int] = 32,					lowerCamelCase_:						Optional[int] = 1,					):
			lowercase__					:      Any      =		load_image(lowerCamelCase_			)
			lowercase__					:      Optional[int]      =		self.image_processor.size['longest_edge']
			lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__					:      Tuple      =		self.image_processor.generate_crop_boxes(
			    lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_			)
			lowercase__					:      List[Any]      =		self.image_processor(images=lowerCamelCase_,					return_tensors='pt'			)

			with self.device_placement():
					if self.framework == "pt":
							lowercase__					:      List[Any]      =		self.get_inference_context()
							with inference_context():
									lowercase__					:      List[Any]      =		self._ensure_tensor_on_device(lowerCamelCase_,					device=self.device			)
									lowercase__					:      List[Any]      =		self.model.get_image_embeddings(model_inputs.pop('pixel_values'			)			)
									lowercase__					:      Union[str, Any]      =		image_embeddings

			lowercase__					:      Optional[Any]      =		grid_points.shape[1]
			lowercase__					:      Optional[Any]      =		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,					lowerCamelCase_,					lowerCamelCase_			):
					lowercase__					:      Any      =		grid_points[:, i : i + points_per_batch, :, :]
					lowercase__					:      Any      =		input_labels[:, i : i + points_per_batch]
					lowercase__					:      str      =		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    snake_case__(						self:						Dict,					lowerCamelCase_:						Any,					lowerCamelCase_:						int=0.8_8,					lowerCamelCase_:						Union[str, Any]=0.9_5,					lowerCamelCase_:						Tuple=0,					lowerCamelCase_:						Optional[int]=1,					):
			lowercase__					:      Any      =		model_inputs.pop('input_boxes'			)
			lowercase__					:      Any      =		model_inputs.pop('is_last'			)
			lowercase__					:      Union[str, Any]      =		model_inputs.pop('original_sizes'			).tolist()
			lowercase__					:      Tuple      =		model_inputs.pop('reshaped_input_sizes'			).tolist()

			lowercase__					:      Union[str, Any]      =		self.model(**lowerCamelCase_			)

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





	def    snake_case__(						self:						Union[str, Any],					lowerCamelCase_:						Dict,					lowerCamelCase_:						Tuple=False,					lowerCamelCase_:						Union[str, Any]=False,					lowerCamelCase_:						Optional[int]=0.7,					):
			lowercase__					:      Union[str, Any]      =		[]
			lowercase__					:      int      =		[]
			lowercase__					:      Union[str, Any]      =		[]
			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'			)			)

			lowercase__					:      Tuple      =		torch.cat(lowerCamelCase_			)
			lowercase__					:      Dict      =		torch.cat(lowerCamelCase_			)
			lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__					:      List[str]      =		self.image_processor.post_process_for_mask_generation(
			    lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_			)

			lowercase__					:      List[str]      =		defaultdict(lowerCamelCase_			)
			for output in model_outputs:
					for k, v in output.items():
							extra[k].append(lowerCamelCase_			)

			lowercase__					:      Union[str, Any]      =		{}
			if output_rle_mask:
					lowercase__					:      Dict      =		rle_mask

			if output_bboxes_mask:
					lowercase__					:      Optional[int]      =		bounding_boxes

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









__UpperCamelCase:	str = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

__UpperCamelCase:	Tuple = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : dict[int, list[int]]   ,    _lowercase     : int   ,    _lowercase     : list[bool]			)   ->							list[int]:




		'''simple docstring'''
		lowercase__					:      str      =		True
		lowercase__					:      int      =		[]

		for neighbour in graph[vert]:
				if not visited[neighbour]:
						order += topology_sort(_lowercase   ,    _lowercase   ,    _lowercase			)

		order.append(_lowercase			)

		return order


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : dict[int, list[int]]   ,    _lowercase     : int   ,    _lowercase     : list[bool]			)   ->							list[int]:




		'''simple docstring'''
		lowercase__					:      Union[str, Any]      =		True
		lowercase__					:      Optional[Any]      =		[vert]

		for neighbour in reversed_graph[vert]:
				if not visited[neighbour]:
						component += find_components(_lowercase   ,    _lowercase   ,    _lowercase			)

		return component





def       SCREAMING_SNAKE_CASE__  (       _lowercase     : dict[int, list[int]]			)   ->							list[list[int]]:




		'''simple docstring'''
		lowercase__					:      Optional[int]      =		len(_lowercase			) * [False]
		lowercase__					:      dict[int, list[int]]      =		{vert: [] for vert in range(len(_lowercase			)			)}

		for vert, neighbours in graph.items():
				for neighbour in neighbours:
						reversed_graph[neighbour].append(_lowercase			)

		lowercase__					:      Dict      =		[]
		for i, was_visited in enumerate(_lowercase			):
				if not was_visited:
						order += topology_sort(_lowercase   ,    _lowercase   ,    _lowercase			)

		lowercase__					:      Dict      =		[]
		lowercase__					:      Optional[Any]      =		len(_lowercase			) * [False]

		for i in range(len(_lowercase			)			):
				lowercase__					:      List[Any]      =		order[len(_lowercase			) - i - 1]
				if not visited[vert]:
						lowercase__					:      Tuple      =		find_components(_lowercase   ,    _lowercase   ,    _lowercase			)
						components_list.append(_lowercase			)

		return components_list


