





"""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

lowerCamelCase_       =		logging.get_logger(__name__)






@add_end_docstrings(A  )
class  _SCREAMING_SNAKE_CASE(       A  ):




       def __init__(						self		,**SCREAMING_SNAKE_CASE__							)      ->				int:





             """simple docstring"""






             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__							)      ->				Optional[int]:





             """simple docstring"""






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





             """simple docstring"""






             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__ = 5_12 / 15_00		,SCREAMING_SNAKE_CASE__ = 32		,SCREAMING_SNAKE_CASE__ = 1		,)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[Any]		    =  load_image(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.image_processor.size['''longest_edge''']
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  self.image_processor.generate_crop_boxes(
                 SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  self.image_processor(images=SCREAMING_SNAKE_CASE__		,return_tensors='''pt'''							)

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

             __SCREAMING_SNAKE_CASE       :str		    =  grid_points.shape[1]
             __SCREAMING_SNAKE_CASE       :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		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE       :str		    =  grid_points[:, i : i + points_per_batch, :, :]
                   __SCREAMING_SNAKE_CASE       :Any		    =  input_labels[:, i : i + points_per_batch]
                   __SCREAMING_SNAKE_CASE       :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		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=0.8_8		,SCREAMING_SNAKE_CASE__=0.9_5		,SCREAMING_SNAKE_CASE__=0		,SCREAMING_SNAKE_CASE__=1		,)      ->				Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[Any]		    =  model_inputs.pop('''input_boxes'''							)
             __SCREAMING_SNAKE_CASE       :Tuple		    =  model_inputs.pop('''is_last'''							)
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  model_inputs.pop('''original_sizes'''							).tolist()
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  model_inputs.pop('''reshaped_input_sizes'''							).tolist()

             __SCREAMING_SNAKE_CASE       :List[Any]		    =  self.model(**SCREAMING_SNAKE_CASE__							)

             # post processing happens here in order to avoid CPU GPU copies of ALL the masks
             __SCREAMING_SNAKE_CASE       :Tuple		    =  model_outputs['''pred_masks''']
             __SCREAMING_SNAKE_CASE       :Tuple		    =  self.image_processor.post_process_masks(
                 SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,binarize=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :List[str]		    =  model_outputs['''iou_scores''']
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  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		,)      ->				Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  []
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  []
             __SCREAMING_SNAKE_CASE       :Dict		    =  []
             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'''							)							)

             __SCREAMING_SNAKE_CASE       :Dict		    =  torch.cat(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :int		    =  torch.cat(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :Dict		    =  self.image_processor.post_process_for_mask_generation(
                 SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

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

             __SCREAMING_SNAKE_CASE       :Tuple		    =  {}
             if output_rle_mask:
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  rle_mask

             if output_bboxes_mask:
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  bounding_boxes

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






"""simple docstring"""
from torch import nn






class  _SCREAMING_SNAKE_CASE(       nn.Module  ):




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				List[str]:





             """simple docstring"""






             super().__init__()
             __SCREAMING_SNAKE_CASE       :Tuple		    =  class_size
             __SCREAMING_SNAKE_CASE       :str		    =  embed_size
             # self.mlp1 = nn.Linear(embed_size, embed_size)
             # self.mlp2 = (nn.Linear(embed_size, class_size))
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  nn.Linear(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.mlp(SCREAMING_SNAKE_CASE__							)
             return logits