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

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


@add_end_docstrings(_a  )
class     lowercase     (  _a  ):




							def __init__(    self :       Any						,				**_UpperCamelCase :       Dict   )       ->					Optional[int]:



												'''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 						__snake_case(    self :       Tuple						,				**_UpperCamelCase :       Tuple   )       ->					Optional[int]:



												'''simple docstring'''

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




							def __call__(    self :       Dict						,				_UpperCamelCase :       Optional[Any]						,				*_UpperCamelCase :       Optional[Any]						,				_UpperCamelCase :       str=None						,				_UpperCamelCase :       str=None						,				**_UpperCamelCase :       Union[str, Any]   )       ->					List[Any]:



												'''simple docstring'''

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




							def 						__snake_case(    self :       List[str]						,				_UpperCamelCase :       List[str]						,				_UpperCamelCase :       Union[str, Any]=64						,				_UpperCamelCase :       int = 0						,				_UpperCamelCase :       float = 512 / 1_500						,				_UpperCamelCase :       Optional[int] = 32						,				_UpperCamelCase :       Optional[int] = 1						,				)       ->					Dict:



												'''simple docstring'''

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

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

												SCREAMING_SNAKE_CASE           =							grid_points.shape[1]
												SCREAMING_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						,				lowerCAmelCase_						,				lowerCAmelCase_   ):
																	SCREAMING_SNAKE_CASE           =							grid_points[:, i : i + points_per_batch, :, :]
																	SCREAMING_SNAKE_CASE           =							input_labels[:, i : i + points_per_batch]
																	SCREAMING_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 						__snake_case(    self :       Any						,				_UpperCamelCase :       Tuple						,				_UpperCamelCase :       List[Any]=0.8_8						,				_UpperCamelCase :       Tuple=0.9_5						,				_UpperCamelCase :       str=0						,				_UpperCamelCase :       Optional[int]=1						,				)       ->					str:



												'''simple docstring'''

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

												SCREAMING_SNAKE_CASE           =							self.model(**lowerCAmelCase_   )

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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							[]
												SCREAMING_SNAKE_CASE           =							[]
												SCREAMING_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"   )   )

												SCREAMING_SNAKE_CASE           =							torch.cat(lowerCAmelCase_   )
												SCREAMING_SNAKE_CASE           =							torch.cat(lowerCAmelCase_   )
												SCREAMING_SNAKE_CASE      , SCREAMING_SNAKE_CASE      , SCREAMING_SNAKE_CASE      , SCREAMING_SNAKE_CASE           =							self.image_processor.post_process_for_mask_generation(
												    lowerCAmelCase_						,				lowerCAmelCase_						,				lowerCAmelCase_						,				lowerCAmelCase_   )

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

												SCREAMING_SNAKE_CASE           =							{}
												if output_rle_mask:
																	SCREAMING_SNAKE_CASE           =							rle_mask

												if output_bboxes_mask:
																	SCREAMING_SNAKE_CASE           =							bounding_boxes

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










'''simple docstring'''


from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass

@dataclass
class      A       :
							lowercase_							     =							42
							lowercase_							     =							42



class      A       :







							def __init__(    self			:    Optional[Any]				,     lowerCAmelCase_			:    int   )     ->				str:


														"""simple docstring"""


														_a      	=							[[] for _ in range(lowerCAmelCase_   )]
														_a      	=							size







							def __getitem__(    self			:    Any				,     lowerCAmelCase_			:    int   )     ->				Iterator[Edge]:


														"""simple docstring"""


														return iter(self._graph[vertex]   )







							@property
							def   __lowerCAmelCase     (    self			:    str   )     ->				Tuple:


														"""simple docstring"""


														return self._size







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    int   )     ->				Dict:


														"""simple docstring"""


														if weight not in (0, 1):
																					raise ValueError('''Edge weight must be either 0 or 1.'''   )

														if to_vertex < 0 or to_vertex >= self.size:
																					raise ValueError('''Vertex indexes must be in [0; size).'''   )

														self._graph[from_vertex].append(Edge(lowerCAmelCase_				,     lowerCAmelCase_   )   )







							def   __lowerCAmelCase     (    self			:    Tuple				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    int   )     ->				int | None:


														"""simple docstring"""


														_a      	=							deque([start_vertex]   )
														_a      	=							[None] * self.size
														_a      	=							0

														while queue:
																					_a      	=							queue.popleft()
																					_a      	=							distances[current_vertex]
																					if current_distance is None:
																												continue

																					for edge in self[current_vertex]:
																												_a      	=							current_distance + edge.weight
																												_a      	=							distances[edge.destination_vertex]
																												if (
																												    isinstance(lowerCAmelCase_				,     lowerCAmelCase_   )
																												    and new_distance >= dest_vertex_distance
																												):
																																			continue
																												_a      	=							new_distance
																												if edge.weight == 0:
																																			queue.appendleft(edge.destination_vertex   )
																												else:
																																			queue.append(edge.destination_vertex   )

														if distances[finish_vertex] is None:
																					raise ValueError('''No path from start_vertex to finish_vertex.'''   )

														return distances[finish_vertex]


if __name__ == "__main__":
				import doctest

				doctest.testmod()

