'''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

__snake_case						  =		logging.get_logger(__name__)





@add_end_docstrings(__a      )
class       _a    (		__a      ):


  """simple docstring"""

  def __init__(		self      :       Tuple   ,				**lowercase_      :       Optional[int]      ):







    '''simple docstring'''


    super().__init__(**lowercase_      )
    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(lowercase_      )




  def 		lowerCamelCase__  (		self      :       Any   ,				**lowercase_      :       Union[str, Any]      ):







    '''simple docstring'''


    lowercase_     =       {}
    lowercase_     =       {}
    lowercase_     =       {}
    # preprocess args
    if "points_per_batch" in kwargs:
      lowercase_     =       kwargs["""points_per_batch"""]
    if "points_per_crop" in kwargs:
      lowercase_     =       kwargs["""points_per_crop"""]
    if "crops_n_layers" in kwargs:
      lowercase_     =       kwargs["""crops_n_layers"""]
    if "crop_overlap_ratio" in kwargs:
      lowercase_     =       kwargs["""crop_overlap_ratio"""]
    if "crop_n_points_downscale_factor" in kwargs:
      lowercase_     =       kwargs["""crop_n_points_downscale_factor"""]
    # postprocess args
    if "pred_iou_thresh" in kwargs:
      lowercase_     =       kwargs["""pred_iou_thresh"""]
    if "stability_score_offset" in kwargs:
      lowercase_     =       kwargs["""stability_score_offset"""]
    if "mask_threshold" in kwargs:
      lowercase_     =       kwargs["""mask_threshold"""]
    if "stability_score_thresh" in kwargs:
      lowercase_     =       kwargs["""stability_score_thresh"""]
    if "crops_nms_thresh" in kwargs:
      lowercase_     =       kwargs["""crops_nms_thresh"""]
    if "output_rle_mask" in kwargs:
      lowercase_     =       kwargs["""output_rle_mask"""]
    if "output_bboxes_mask" in kwargs:
      lowercase_     =       kwargs["""output_bboxes_mask"""]
    return preprocess_kwargs, forward_params, postprocess_kwargs




  def __call__(		self      :       Tuple   ,				lowercase_      :       List[Any]   ,				*lowercase_      :       Union[str, Any]   ,				lowercase_      :       int=None   ,				lowercase_      :       Optional[int]=None   ,				**lowercase_      :       int      ):







    '''simple docstring'''


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




  def 		lowerCamelCase__  (		self      :       Union[str, Any]   ,				lowercase_      :       int   ,				lowercase_      :       Optional[Any]=64   ,				lowercase_      :       int = 0   ,				lowercase_      :       float = 512 / 1_500   ,				lowercase_      :       Optional[int] = 32   ,				lowercase_      :       Optional[int] = 1   ,				):







    '''simple docstring'''


    lowercase_     =       load_image(lowercase_      )
    lowercase_     =       self.image_processor.size["""longest_edge"""]
    lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     =       self.image_processor.generate_crop_boxes(
        lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_      )
    lowercase_     =       self.image_processor(images=lowercase_   ,				return_tensors="""pt"""      )

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

    lowercase_     =       grid_points.shape[1]
    lowercase_     =       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   ,				lowercase_   ,				lowercase_      ):
      lowercase_     =       grid_points[:, i : i + points_per_batch, :, :]
      lowercase_     =       input_labels[:, i : i + points_per_batch]
      lowercase_     =       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      :       Tuple   ,				lowercase_      :       Optional[Any]   ,				lowercase_      :       List[str]=0.8_8   ,				lowercase_      :       Tuple=0.9_5   ,				lowercase_      :       Any=0   ,				lowercase_      :       Tuple=1   ,				):







    '''simple docstring'''


    lowercase_     =       model_inputs.pop("""input_boxes"""      )
    lowercase_     =       model_inputs.pop("""is_last"""      )
    lowercase_     =       model_inputs.pop("""original_sizes"""      ).tolist()
    lowercase_     =       model_inputs.pop("""reshaped_input_sizes"""      ).tolist()

    lowercase_     =       self.model(**lowercase_      )

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





  def 		lowerCamelCase__  (		self      :       List[Any]   ,				lowercase_      :       Any   ,				lowercase_      :       Optional[int]=False   ,				lowercase_      :       str=False   ,				lowercase_      :       int=0.7   ,				):







    '''simple docstring'''


    lowercase_     =       []
    lowercase_     =       []
    lowercase_     =       []
    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_     =       torch.cat(lowercase_      )
    lowercase_     =       torch.cat(lowercase_      )
    lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     =       self.image_processor.post_process_for_mask_generation(
        lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_      )

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

    lowercase_     =       {}
    if output_rle_mask:
      lowercase_     =       rle_mask

    if output_bboxes_mask:
      lowercase_     =       bounding_boxes

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


'''simple docstring'''


import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


__snake_case						  =		logging.get_logger(__name__)





class       _a    (		__a      ):


  """simple docstring"""

  def __init__(		self      :       int   ,				*lowercase_      :       Tuple   ,				**lowercase_      :       List[Any]      ):







    '''simple docstring'''


    warnings.warn(
        """The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
        """ Please use PoolFormerImageProcessor instead."""   ,				lowercase_   ,				)
    super().__init__(*lowercase_   ,				**lowercase_      )

