


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							:   str						=						logging.get_logger(__name__)






@add_end_docstrings(_UpperCamelCase					)
class    _UpperCamelCase     (       _UpperCamelCase					):

				"""simple docstring"""







				def __init__(    self					, **a__ )						->					List[Any]:
					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   _UpperCAmelCase       (    self					, **a__ )						->					Dict:
					A             =					{}
					A             =					{}
					A             =					{}
					# preprocess args
					if "points_per_batch" in kwargs:
						A             =					kwargs['points_per_batch']
					if "points_per_crop" in kwargs:
						A             =					kwargs['points_per_crop']
					if "crops_n_layers" in kwargs:
						A             =					kwargs['crops_n_layers']
					if "crop_overlap_ratio" in kwargs:
						A             =					kwargs['crop_overlap_ratio']
					if "crop_n_points_downscale_factor" in kwargs:
						A             =					kwargs['crop_n_points_downscale_factor']
					# postprocess args
					if "pred_iou_thresh" in kwargs:
						A             =					kwargs['pred_iou_thresh']
					if "stability_score_offset" in kwargs:
						A             =					kwargs['stability_score_offset']
					if "mask_threshold" in kwargs:
						A             =					kwargs['mask_threshold']
					if "stability_score_thresh" in kwargs:
						A             =					kwargs['stability_score_thresh']
					if "crops_nms_thresh" in kwargs:
						A             =					kwargs['crops_nms_thresh']
					if "output_rle_mask" in kwargs:
						A             =					kwargs['output_rle_mask']
					if "output_bboxes_mask" in kwargs:
						A             =					kwargs['output_bboxes_mask']
					return preprocess_kwargs, forward_params, postprocess_kwargs

				def __call__(    self					, a__					, *a__					, a__=None					, a__=None					, **a__ )						->					List[str]:
					return super().__call__(lowerCamelCase_					, *lowerCamelCase_					, num_workers=lowerCamelCase_					, batch_size=lowerCamelCase_					, **lowerCamelCase_ )

				def   _UpperCAmelCase       (    self					, a__					, a__=64					, a__ = 0					, a__ = 512 / 1500					, a__ = 32					, a__ = 1					, )						->					Dict:
					A             =					load_image(lowerCamelCase_ )
					A             =					self.image_processor.size['longest_edge']
					A             =					self.image_processor.generate_crop_boxes(
					    lowerCamelCase_					, lowerCamelCase_					, lowerCamelCase_					, lowerCamelCase_					, lowerCamelCase_					, lowerCamelCase_ )
					A             =					self.image_processor(images=lowerCamelCase_					, return_tensors="""pt""" )

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

					A             =					grid_points.shape[1]
					A             =					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_ ):
						A             =					grid_points[:, i : i + points_per_batch, :, :]
						A             =					input_labels[:, i : i + points_per_batch]
						A             =					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					, a__					, a__=0.88					, a__=0.95					, a__=0					, a__=1					, )						->					Optional[Any]:
					A             =					model_inputs.pop("""input_boxes""" )
					A             =					model_inputs.pop("""is_last""" )
					A             =					model_inputs.pop("""original_sizes""" ).tolist()
					A             =					model_inputs.pop("""reshaped_input_sizes""" ).tolist()

					A             =					self.model(**lowerCamelCase_ )

					# post processing happens here in order to avoid CPU GPU copies of ALL the masks
					A             =					model_outputs['pred_masks']
					A             =					self.image_processor.post_process_masks(
					    lowerCamelCase_					, lowerCamelCase_					, lowerCamelCase_					, lowerCamelCase_					, binarize=lowerCamelCase_ )
					A             =					model_outputs['iou_scores']
					A             =					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   _UpperCAmelCase       (    self					, a__					, a__=False					, a__=False					, a__=0.7					, )						->					Union[str, Any]:
					A             =					[]
					A             =					[]
					A             =					[]
					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""" ) )

					A             =					torch.cat(lowerCamelCase_ )
					A             =					torch.cat(lowerCamelCase_ )
					A             =					self.image_processor.post_process_for_mask_generation(
					    lowerCamelCase_					, lowerCamelCase_					, lowerCamelCase_					, lowerCamelCase_ )

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

					A             =					{}
					if output_rle_mask:
						A             =					rle_mask

					if output_bboxes_mask:
						A             =					bounding_boxes

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










from ..utils import DummyObject, requires_backends


class       __lowerCAmelCase					(       metaclass=_UpperCamelCase      ):


	'''simple docstring'''





	_A													=				["note_seq"]

	def __init__(						self:						int,					*lowerCamelCase_:						Optional[int],					**lowerCamelCase_:						Tuple			):
			requires_backends(self,					['note_seq']			)

	@classmethod
	def    snake_case__(						cls:						Optional[Any],					*lowerCamelCase_:						Tuple,					**lowerCamelCase_:						Optional[Any]			):
			requires_backends(cls,					['note_seq']			)





	@classmethod
	def    snake_case__(						cls:						Any,					*lowerCamelCase_:						Dict,					**lowerCamelCase_:						str			):
			requires_backends(cls,					['note_seq']			)


