






"""simple docstring"""







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


@add_end_docstrings(SCREAMING_SNAKE_CASE__							)
class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  **snake_case__      ):
												'''simple docstring'''
												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	,  **snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Any=	{}
												lowercase__    :				List[Any]=	{}
												lowercase__    :				Dict=	{}
												# preprocess args
												if "points_per_batch" in kwargs:
																		lowercase__    :				Optional[Any]=	kwargs["points_per_batch"]
												if "points_per_crop" in kwargs:
																		lowercase__    :				Any=	kwargs["points_per_crop"]
												if "crops_n_layers" in kwargs:
																		lowercase__    :				Dict=	kwargs["crops_n_layers"]
												if "crop_overlap_ratio" in kwargs:
																		lowercase__    :				Any=	kwargs["crop_overlap_ratio"]
												if "crop_n_points_downscale_factor" in kwargs:
																		lowercase__    :				Optional[int]=	kwargs["crop_n_points_downscale_factor"]
												# postprocess args
												if "pred_iou_thresh" in kwargs:
																		lowercase__    :				List[str]=	kwargs["pred_iou_thresh"]
												if "stability_score_offset" in kwargs:
																		lowercase__    :				Any=	kwargs["stability_score_offset"]
												if "mask_threshold" in kwargs:
																		lowercase__    :				Tuple=	kwargs["mask_threshold"]
												if "stability_score_thresh" in kwargs:
																		lowercase__    :				str=	kwargs["stability_score_thresh"]
												if "crops_nms_thresh" in kwargs:
																		lowercase__    :				Tuple=	kwargs["crops_nms_thresh"]
												if "output_rle_mask" in kwargs:
																		lowercase__    :				List[Any]=	kwargs["output_rle_mask"]
												if "output_bboxes_mask" in kwargs:
																		lowercase__    :				Dict=	kwargs["output_bboxes_mask"]
												return preprocess_kwargs, forward_params, postprocess_kwargs




						def __call__(       self	,  snake_case__	,  *snake_case__	,  snake_case__=None	,  snake_case__=None	,  **snake_case__      ):
												'''simple docstring'''
												return super().__call__(snake_case__	,  *snake_case__	,  num_workers=snake_case__	,  batch_size=snake_case__	,  **snake_case__      )




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__=64	,  snake_case__ = 0	,  snake_case__ = 512 / 1500	,  snake_case__ = 32	,  snake_case__ = 1	,  ):
												'''simple docstring'''
												lowercase__    :				Optional[int]=	load_image(snake_case__      )
												lowercase__    :				Optional[int]=	self.image_processor.size["longest_edge"]
												lowercase__,     lowercase__,     lowercase__,     lowercase__    :				List[Any]=	self.image_processor.generate_crop_boxes(
												    snake_case__	,  snake_case__	,  snake_case__	,  snake_case__	,  snake_case__	,  snake_case__      )
												lowercase__    :				Optional[Any]=	self.image_processor(images=snake_case__	,  return_tensors="pt"      )

												with self.device_placement():
																		if self.framework == "pt":
																								lowercase__    :				str=	self.get_inference_context()
																								with inference_context():
																														lowercase__    :				int=	self._ensure_tensor_on_device(snake_case__	,  device=self.device      )
																														lowercase__    :				int=	self.model.get_image_embeddings(model_inputs.pop("pixel_values"      )      )
																														lowercase__    :				Dict=	image_embeddings

												lowercase__    :				List[Any]=	grid_points.shape[1]
												lowercase__    :				Optional[int]=	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__      ):
																		lowercase__    :				Union[str, Any]=	grid_points[:, i : i + points_per_batch, :, :]
																		lowercase__    :				List[Any]=	input_labels[:, i : i + points_per_batch]
																		lowercase__    :				List[Any]=	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	,  snake_case__	,  snake_case__=0.88	,  snake_case__=0.95	,  snake_case__=0	,  snake_case__=1	,  ):
												'''simple docstring'''
												lowercase__    :				Union[str, Any]=	model_inputs.pop("input_boxes"      )
												lowercase__    :				Any=	model_inputs.pop("is_last"      )
												lowercase__    :				Tuple=	model_inputs.pop("original_sizes"      ).tolist()
												lowercase__    :				Optional[int]=	model_inputs.pop("reshaped_input_sizes"      ).tolist()

												lowercase__    :				List[str]=	self.model(**snake_case__      )

												# post processing happens here in order to avoid CPU GPU copies of ALL the masks
												lowercase__    :				Tuple=	model_outputs["pred_masks"]
												lowercase__    :				Tuple=	self.image_processor.post_process_masks(
												    snake_case__	,  snake_case__	,  snake_case__	,  snake_case__	,  binarize=snake_case__      )
												lowercase__    :				Dict=	model_outputs["iou_scores"]
												lowercase__,     lowercase__,     lowercase__    :				Any=	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	,  snake_case__	,  snake_case__=False	,  snake_case__=False	,  snake_case__=0.7	,  ):
												'''simple docstring'''
												lowercase__    :				Any=	[]
												lowercase__    :				Tuple=	[]
												lowercase__    :				List[Any]=	[]
												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"      )      )

												lowercase__    :				Dict=	torch.cat(snake_case__      )
												lowercase__    :				List[Any]=	torch.cat(snake_case__      )
												lowercase__,     lowercase__,     lowercase__,     lowercase__    :				Dict=	self.image_processor.post_process_for_mask_generation(
												    snake_case__	,  snake_case__	,  snake_case__	,  snake_case__      )

												lowercase__    :				str=	defaultdict(snake_case__      )
												for output in model_outputs:
																		for k, v in output.items():
																								extra[k].append(snake_case__      )

												lowercase__    :				Tuple=	{}
												if output_rle_mask:
																		lowercase__    :				Optional[int]=	rle_mask

												if output_bboxes_mask:
																		lowercase__    :				Optional[Any]=	bounding_boxes

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














"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a			:    Union[str, Any]				 =		{
    """configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a			:    int				 =		[
			    """PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """PegasusXForConditionalGeneration""",
			    """PegasusXModel""",
			    """PegasusXPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_pegasus_x import (
						    PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
						    PegasusXForConditionalGeneration,
						    PegasusXModel,
						    PegasusXPreTrainedModel,
						)


else:
			import sys

			a			:    Optional[Any]				 =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






