





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

SCREAMING_SNAKE_CASE__			      =						logging.get_logger(__name__)







@add_end_docstrings(__a  )
class   __lowerCAmelCase			(					__a  ):





   def __init__( self					,			**snake_case      )				->  Optional[Any]:
      """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 	_snake_case ( self					,			**snake_case      )				->  str:
      """simple docstring"""





      a__	:						Any      		=  {}
      a__	:						str      		=  {}
      a__	:						Any      		=  {}
      # preprocess args
      if "points_per_batch" in kwargs:
         a__	:						Dict      		=  kwargs["points_per_batch"]
      if "points_per_crop" in kwargs:
         a__	:						Union[str, Any]      		=  kwargs["points_per_crop"]
      if "crops_n_layers" in kwargs:
         a__	:						str      		=  kwargs["crops_n_layers"]
      if "crop_overlap_ratio" in kwargs:
         a__	:						Optional[int]      		=  kwargs["crop_overlap_ratio"]
      if "crop_n_points_downscale_factor" in kwargs:
         a__	:						Tuple      		=  kwargs["crop_n_points_downscale_factor"]
      # postprocess args
      if "pred_iou_thresh" in kwargs:
         a__	:						List[Any]      		=  kwargs["pred_iou_thresh"]
      if "stability_score_offset" in kwargs:
         a__	:						Any      		=  kwargs["stability_score_offset"]
      if "mask_threshold" in kwargs:
         a__	:						Dict      		=  kwargs["mask_threshold"]
      if "stability_score_thresh" in kwargs:
         a__	:						str      		=  kwargs["stability_score_thresh"]
      if "crops_nms_thresh" in kwargs:
         a__	:						Dict      		=  kwargs["crops_nms_thresh"]
      if "output_rle_mask" in kwargs:
         a__	:						int      		=  kwargs["output_rle_mask"]
      if "output_bboxes_mask" in kwargs:
         a__	:						List[Any]      		=  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      )				->  Optional[int]:
      """simple docstring"""





      return super().__call__(snake_case__					,			*snake_case__					,			num_workers=snake_case__					,			batch_size=snake_case__					,			**snake_case__      )





   def 	_snake_case ( self					,			snake_case					,			snake_case=64					,			snake_case = 0					,			snake_case = 512 / 1_500					,			snake_case = 32					,			snake_case = 1					,			)				->  Optional[Any]:
      """simple docstring"""





      a__	:						Optional[Any]      		=  load_image(snake_case__      )
      a__	:						str      		=  self.image_processor.size["longest_edge"]
      a__		,     a__		,     a__		,     a__	:						str      		=  self.image_processor.generate_crop_boxes(
          snake_case__					,			snake_case__					,			snake_case__					,			snake_case__					,			snake_case__					,			snake_case__      )
      a__	:						List[Any]      		=  self.image_processor(images=snake_case__					,			return_tensors="pt"      )

      with self.device_placement():
         if self.framework == "pt":
            a__	:						List[str]      		=  self.get_inference_context()
            with inference_context():
               a__	:						Any      		=  self._ensure_tensor_on_device(snake_case__					,			device=self.device      )
               a__	:						Tuple      		=  self.model.get_image_embeddings(model_inputs.pop("pixel_values"      )      )
               a__	:						Optional[Any]      		=  image_embeddings

      a__	:						Dict      		=  grid_points.shape[1]
      a__	:						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__      ):
         a__	:						Dict      		=  grid_points[:, i : i + points_per_batch, :, :]
         a__	:						List[Any]      		=  input_labels[:, i : i + points_per_batch]
         a__	:						Tuple      		=  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 	_snake_case ( self					,			snake_case					,			snake_case=0.88					,			snake_case=0.95					,			snake_case=0					,			snake_case=1					,			)				->  str:
      """simple docstring"""





      a__	:						str      		=  model_inputs.pop("input_boxes"      )
      a__	:						Optional[Any]      		=  model_inputs.pop("is_last"      )
      a__	:						Union[str, Any]      		=  model_inputs.pop("original_sizes"      ).tolist()
      a__	:						Optional[Any]      		=  model_inputs.pop("reshaped_input_sizes"      ).tolist()

      a__	:						str      		=  self.model(**snake_case__      )

      # post processing happens here in order to avoid CPU GPU copies of ALL the masks
      a__	:						str      		=  model_outputs["pred_masks"]
      a__	:						List[str]      		=  self.image_processor.post_process_masks(
          snake_case__					,			snake_case__					,			snake_case__					,			snake_case__					,			binarize=snake_case__      )
      a__	:						Any      		=  model_outputs["iou_scores"]
      a__		,     a__		,     a__	:						Optional[int]      		=  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 	_snake_case ( self					,			snake_case					,			snake_case=False					,			snake_case=False					,			snake_case=0.7					,			)				->  List[str]:
      """simple docstring"""





      a__	:						Dict      		=  []
      a__	:						Optional[int]      		=  []
      a__	:						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"      )      )

      a__	:						str      		=  torch.cat(snake_case__      )
      a__	:						List[Any]      		=  torch.cat(snake_case__      )
      a__		,     a__		,     a__		,     a__	:						int      		=  self.image_processor.post_process_for_mask_generation(
          snake_case__					,			snake_case__					,			snake_case__					,			snake_case__      )

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

      a__	:						List[Any]      		=  {}
      if output_rle_mask:
         a__	:						Any      		=  rle_mask

      if output_bboxes_mask:
         a__	:						int      		=  bounding_boxes

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








from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE__       :			List[str]			      =						{"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						[
      """VAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """VanForImageClassification""",
      """VanModel""",
      """VanPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_van import (
        VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
        VanForImageClassification,
        VanModel,
        VanPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE__       :			List[str]			      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure)

