




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(__magic_name__ )
class     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			def __init__(      self			,						**lowerCamelCase__							)    ->      Any:



							'''simple docstring'''
							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       lowercase_    (      self			,						**lowerCamelCase__							)    ->      List[Any]:



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


			def __call__(      self			,						lowerCamelCase__			,						*lowerCamelCase__			,						lowerCamelCase__=None			,						lowerCamelCase__=None			,						**lowerCamelCase__							)    ->      List[Any]:



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


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__=64			,						lowerCamelCase__ = 0			,						lowerCamelCase__ = 512 / 1_500			,						lowerCamelCase__ = 32			,						lowerCamelCase__ = 1			,						)    ->      Optional[int]:



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

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

							__lowerCamelCase		       =    grid_points.shape[1]
							__lowerCamelCase		       =    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__							):
											__lowerCamelCase		       =    grid_points[:, i : i + points_per_batch, :, :]
											__lowerCamelCase		       =    input_labels[:, i : i + points_per_batch]
											__lowerCamelCase		       =    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       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__=0.88			,						lowerCamelCase__=0.95			,						lowerCamelCase__=0			,						lowerCamelCase__=1			,						)    ->      Any:



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

							__lowerCamelCase		       =    self.model(**lowerCamelCase__							)

							# post processing happens here in order to avoid CPU GPU copies of ALL the masks
							__lowerCamelCase		       =    model_outputs['pred_masks']
							__lowerCamelCase		       =    self.image_processor.post_process_masks(
							    lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						binarize=lowerCamelCase__							)
							__lowerCamelCase		       =    model_outputs['iou_scores']
							__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    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       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__=False			,						lowerCamelCase__=False			,						lowerCamelCase__=0.7			,						)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    []
							__lowerCamelCase		       =    []
							__lowerCamelCase		       =    []
							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'							)							)

							__lowerCamelCase		       =    torch.cat(lowerCamelCase__							)
							__lowerCamelCase		       =    torch.cat(lowerCamelCase__							)
							__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    self.image_processor.post_process_for_mask_generation(
							    lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)

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

							__lowerCamelCase		       =    {}
							if output_rle_mask:
											__lowerCamelCase		       =    rle_mask

							if output_bboxes_mask:
											__lowerCamelCase		       =    bounding_boxes

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





class   __lowercase				:




			"""simple docstring"""







			def __init__( self  :     List[Any]   ,						lowerCAmelCase__  :     Optional[int]   ,						lowerCAmelCase__  :     List[Any]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       name
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       val



			def __str__( self  :     Dict):
									return F"{self.__class__.__name__}({self.name}, {self.val})"




			def __lt__( self  :     List[str]   ,						lowerCAmelCase__  :     Any):
									return self.val < other.val







class   __lowercase				:




			"""simple docstring"""







			def __init__( self  :     Tuple   ,						lowerCAmelCase__  :     Dict):
									SCREAMING_SNAKE_CASE_: str  		=       {}
									SCREAMING_SNAKE_CASE_: int  		=       {}
									SCREAMING_SNAKE_CASE_: Any  		=       self.build_heap(lowerCAmelCase__)



			def __getitem__( self  :     List[Any]   ,						lowerCAmelCase__  :     Dict):
									return self.get_value(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     Dict):
									return (idx - 1) // 2



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     Optional[Any]):
									return idx * 2 + 1



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     Tuple):
									return idx * 2 + 2



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     Optional[int]):
									return self.heap_dict[key]



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]   ,						lowerCAmelCase__  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Tuple  		=       len(lowerCAmelCase__) - 1
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.get_parent_idx(lowerCAmelCase__)

									for idx, i in enumerate(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       idx
															SCREAMING_SNAKE_CASE_: str  		=       i.val

									for i in range(lowerCAmelCase__   ,						-1   ,						-1):
															self.sift_down(lowerCAmelCase__   ,						lowerCAmelCase__)
									return array



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     Optional[int]   ,						lowerCAmelCase__  :     List[str]):
									while True:
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.get_left_child_idx(lowerCAmelCase__)  # noqa: E741
															SCREAMING_SNAKE_CASE_: Dict  		=       self.get_right_child_idx(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: int  		=       idx
															if l < len(lowerCAmelCase__) and array[l] < array[idx]:
																					SCREAMING_SNAKE_CASE_: List[str]  		=       l
															if r < len(lowerCAmelCase__) and array[r] < array[smallest]:
																					SCREAMING_SNAKE_CASE_: str  		=       r

															if smallest != idx:
																					SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       array[smallest], array[idx]
																					(
																					    (
																					    SCREAMING_SNAKE_CASE_
																					)		,    (
																					    SCREAMING_SNAKE_CASE_
																					)		,    
																					): Optional[Any]  		=       (
																					    self.idx_of_element[array[smallest]],
																					    self.idx_of_element[array[idx]],
																					)
																					SCREAMING_SNAKE_CASE_: Optional[int]  		=       smallest
															else:
																					break



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     str):
									SCREAMING_SNAKE_CASE_: Any  		=       self.get_parent_idx(lowerCAmelCase__)
									while p >= 0 and self.heap[p] > self.heap[idx]:
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       self.heap[idx], self.heap[p]
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       (
															    self.idx_of_element[self.heap[idx]],
															    self.idx_of_element[self.heap[p]],
															)
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       p
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.get_parent_idx(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									return self.heap[0]



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       self.heap[-1], self.heap[0]
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[str]  		=       (
									    self.idx_of_element[self.heap[-1]],
									    self.idx_of_element[self.heap[0]],
									)

									SCREAMING_SNAKE_CASE_: int  		=       self.heap.pop()
									del self.idx_of_element[x]
									self.sift_down(0   ,						self.heap)
									return x



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     Tuple):
									self.heap.append(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[str]  		=       len(self.heap) - 1
									SCREAMING_SNAKE_CASE_: List[str]  		=       node.val
									self.sift_up(len(self.heap) - 1)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									return len(self.heap) == 0




			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     Any   ,						lowerCAmelCase__  :     Optional[int]):
									assert (
									    self.heap[self.idx_of_element[node]].val > new_value
									), "newValue must be less that current value"
									SCREAMING_SNAKE_CASE_: Any  		=       new_value
									SCREAMING_SNAKE_CASE_: Tuple  		=       new_value
									self.sift_up(self.idx_of_element[node])







lowerCAmelCase							:  int   	=						Node("""R""", -1)
lowerCAmelCase							:  str   	=						Node("""B""", 6)
lowerCAmelCase							:  str   	=						Node("""A""", 3)
lowerCAmelCase							:  List[str]   	=						Node("""X""", 1)
lowerCAmelCase							:  Union[str, Any]   	=						Node("""E""", 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
lowerCAmelCase							:  Optional[Any]   	=						MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print("""Min Heap - before decrease key""")
for i in my_min_heap.heap:
						print(i)

print("""Min Heap - After decrease key of node [B -> -17]""")
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
						print(i)

if __name__ == "__main__":
						import doctest

						doctest.testmod()






