




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(__lowercase     )
class       _A				(		__lowercase     ):






		def __init__(    self						,		**_SCREAMING_SNAKE_CASE			):
								super().__init__(**_SCREAMING_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(_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		**_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			{}
								_UpperCAmelCase      =			{}
								_UpperCAmelCase      =			{}
								# preprocess args
								if "points_per_batch" in kwargs:
														_UpperCAmelCase      =			kwargs["""points_per_batch"""]
								if "points_per_crop" in kwargs:
														_UpperCAmelCase      =			kwargs["""points_per_crop"""]
								if "crops_n_layers" in kwargs:
														_UpperCAmelCase      =			kwargs["""crops_n_layers"""]
								if "crop_overlap_ratio" in kwargs:
														_UpperCAmelCase      =			kwargs["""crop_overlap_ratio"""]
								if "crop_n_points_downscale_factor" in kwargs:
														_UpperCAmelCase      =			kwargs["""crop_n_points_downscale_factor"""]
								# postprocess args
								if "pred_iou_thresh" in kwargs:
														_UpperCAmelCase      =			kwargs["""pred_iou_thresh"""]
								if "stability_score_offset" in kwargs:
														_UpperCAmelCase      =			kwargs["""stability_score_offset"""]
								if "mask_threshold" in kwargs:
														_UpperCAmelCase      =			kwargs["""mask_threshold"""]
								if "stability_score_thresh" in kwargs:
														_UpperCAmelCase      =			kwargs["""stability_score_thresh"""]
								if "crops_nms_thresh" in kwargs:
														_UpperCAmelCase      =			kwargs["""crops_nms_thresh"""]
								if "output_rle_mask" in kwargs:
														_UpperCAmelCase      =			kwargs["""output_rle_mask"""]
								if "output_bboxes_mask" in kwargs:
														_UpperCAmelCase      =			kwargs["""output_bboxes_mask"""]
								return preprocess_kwargs, forward_params, postprocess_kwargs






		def __call__(    self						,		_SCREAMING_SNAKE_CASE						,		*_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		**_SCREAMING_SNAKE_CASE			):
								return super().__call__(_SCREAMING_SNAKE_CASE						,		*_SCREAMING_SNAKE_CASE						,		num_workers=_SCREAMING_SNAKE_CASE						,		batch_size=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=64						,		_SCREAMING_SNAKE_CASE = 0						,		_SCREAMING_SNAKE_CASE = 512 / 1500						,		_SCREAMING_SNAKE_CASE = 32						,		_SCREAMING_SNAKE_CASE = 1						,		):
								_UpperCAmelCase      =			load_image(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			self.image_processor.size["""longest_edge"""]
								_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase      =			self.image_processor.generate_crop_boxes(
								    _SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			self.image_processor(images=_SCREAMING_SNAKE_CASE						,		return_tensors="""pt"""			)

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

								_UpperCAmelCase      =			grid_points.shape[1]
								_UpperCAmelCase      =			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						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
														_UpperCAmelCase      =			grid_points[:, i : i + points_per_batch, :, :]
														_UpperCAmelCase      =			input_labels[:, i : i + points_per_batch]
														_UpperCAmelCase      =			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						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=0.88						,		_SCREAMING_SNAKE_CASE=0.95						,		_SCREAMING_SNAKE_CASE=0						,		_SCREAMING_SNAKE_CASE=1						,		):
								_UpperCAmelCase      =			model_inputs.pop("""input_boxes"""			)
								_UpperCAmelCase      =			model_inputs.pop("""is_last"""			)
								_UpperCAmelCase      =			model_inputs.pop("""original_sizes"""			).tolist()
								_UpperCAmelCase      =			model_inputs.pop("""reshaped_input_sizes"""			).tolist()

								_UpperCAmelCase      =			self.model(**_SCREAMING_SNAKE_CASE			)

								# post processing happens here in order to avoid CPU GPU copies of ALL the masks
								_UpperCAmelCase      =			model_outputs["""pred_masks"""]
								_UpperCAmelCase      =			self.image_processor.post_process_masks(
								    _SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		binarize=_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			model_outputs["""iou_scores"""]
								_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase      =			self.image_processor.filter_masks(
								    masks[0]						,		iou_scores[0]						,		original_sizes[0]						,		input_boxes[0]						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		)
								return {
								    "masks": masks,
								    "is_last": is_last,
								    "boxes": boxes,
								    "iou_scores": iou_scores,
								}






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=False						,		_SCREAMING_SNAKE_CASE=False						,		_SCREAMING_SNAKE_CASE=0.7						,		):
								_UpperCAmelCase      =			[]
								_UpperCAmelCase      =			[]
								_UpperCAmelCase      =			[]
								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"""			)			)

								_UpperCAmelCase      =			torch.cat(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			torch.cat(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase      =			self.image_processor.post_process_for_mask_generation(
								    _SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)

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

								_UpperCAmelCase      =			{}
								if output_rle_mask:
														_UpperCAmelCase      =			rle_mask

								if output_bboxes_mask:
														_UpperCAmelCase      =			bounding_boxes

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





import math




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				float:
						if (
						    not isinstance(snake_case			,     (int, float)       )
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError("""power_factor must be a valid float value between -1 and 1."""       )
						return apparent_power * power_factor




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				float:
						if (
						    not isinstance(snake_case			,     (int, float)       )
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError("""power_factor must be a valid float value between -1 and 1."""       )
						return apparent_power * math.sqrt(1 - power_factor**2       )


if __name__ == "__main__":
				import doctest

				doctest.testmod()