





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_      							=     logging.get_logger(__name__)
@add_end_docstrings(__lowerCamelCase					)
class 			UpperCamelCase_		(  __lowerCamelCase					):




					def __init__(		self				,						**lowerCAmelCase_  )		->							Tuple:
										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 	lowerCAmelCase			(		self				,						**lowerCAmelCase_  )		->							List[Any]:
										_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				,						lowerCAmelCase_				,						*lowerCAmelCase_				,						lowerCAmelCase_=None				,						lowerCAmelCase_=None				,						**lowerCAmelCase_  )		->							int:
										return super().__call__(a_				,						*a_				,						num_workers=a_				,						batch_size=a_				,						**a_  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_=64				,						lowerCAmelCase_ = 0				,						lowerCAmelCase_ = 512 / 1500				,						lowerCAmelCase_ = 32				,						lowerCAmelCase_ = 1				,						)		->							Any:
										_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 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_=0.88				,						lowerCAmelCase_=0.95				,						lowerCAmelCase_=0				,						lowerCAmelCase_=1				,						)		->							Union[str, Any]:
										_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 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_=False				,						lowerCAmelCase_=False				,						lowerCAmelCase_=0.7				,						)		->							str:
										_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}










import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class 			UpperCamelCase_		(  unittest.TestCase					):




					def 	lowerCAmelCase			(		self  )		->							List[Any]:
										debug_launcher(test_script.main  )






					def 	lowerCAmelCase			(		self  )		->							Optional[Any]:
										debug_launcher(test_ops.main  )



