



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

lowercase_													=      logging.get_logger(__name__)






@add_end_docstrings(SCREAMING_SNAKE_CASE					)
class     __a			(  SCREAMING_SNAKE_CASE					):



	def __init__(    self				:     Tuple					,						**snake_case_				:     Optional[Any])->				Any:
						super().__init__(**snake_case_)
						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(snake_case_)



	def 		UpperCamelCase				(    self				:     Union[str, Any]					,						**snake_case_				:     Any)->				List[Any]:
						__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				:     List[Any]					,						snake_case_				:     str					,						*snake_case_				:     Tuple					,						snake_case_				:     Any=None					,						snake_case_				:     List[str]=None					,						**snake_case_				:     Union[str, Any])->				Tuple:
						return super().__call__(snake_case_					,						*snake_case_					,						num_workers=snake_case_					,						batch_size=snake_case_					,						**snake_case_)



	def 		UpperCamelCase				(    self				:     Tuple					,						snake_case_				:     Dict					,						snake_case_				:     Any=64					,						snake_case_				:     int = 0					,						snake_case_				:     float = 5_12 / 15_00					,						snake_case_				:     Optional[int] = 32					,						snake_case_				:     Optional[int] = 1					,						)->				str:
						__lowerCAmelCase						 =load_image(snake_case_)
						__lowerCAmelCase						 =self.image_processor.size["""longest_edge"""]
						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase						 =self.image_processor.generate_crop_boxes(
						    snake_case_					,						snake_case_					,						snake_case_					,						snake_case_					,						snake_case_					,						snake_case_)
						__lowerCAmelCase						 =self.image_processor(images=snake_case_					,						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(snake_case_					,						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					,						snake_case_					,						snake_case_):
											__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 		UpperCamelCase				(    self				:     str					,						snake_case_				:     Dict					,						snake_case_				:     Dict=0.8_8					,						snake_case_				:     Dict=0.9_5					,						snake_case_				:     Optional[Any]=0					,						snake_case_				:     List[Any]=1					,						)->				Any:
						__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(**snake_case_)

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



	def 		UpperCamelCase				(    self				:     List[str]					,						snake_case_				:     List[str]					,						snake_case_				:     List[Any]=False					,						snake_case_				:     Dict=False					,						snake_case_				:     int=0.7					,						)->				Tuple:
						__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(snake_case_)
						__lowerCAmelCase						 =torch.cat(snake_case_)
						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase						 =self.image_processor.post_process_for_mask_generation(
						    snake_case_					,						snake_case_					,						snake_case_					,						snake_case_)

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

						__lowerCAmelCase						 ={}
						if output_rle_mask:
											__lowerCAmelCase						 =rle_mask

						if output_bboxes_mask:
											__lowerCAmelCase						 =bounding_boxes

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









def        __lowerCAmelCase		(	__lowerCamelCase						:     int					, __lowerCamelCase						:     int						)	->    bool:
					return numa ^ numa < 0


if __name__ == "__main__":
				import doctest

				doctest.testmod()




