
'''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

UpperCAmelCase_       :   Dict   =       logging.get_logger(__name__)

@add_end_docstrings(__UpperCAmelCase )
class  UpperCAmelCase__						(       __UpperCAmelCase ):



    def __init__(	self      :       Dict,**__A      :       Any     ):
          super().__init__(**lowerCAmelCase_     )
          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(lowerCAmelCase_     )



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



    def __call__(	self      :       Union[str, Any],__A      :       List[str],*__A      :       Any,__A      :       Optional[int]=None,__A      :       List[str]=None,**__A      :       str     ):
          return super().__call__(lowerCAmelCase_,*lowerCAmelCase_,num_workers=lowerCAmelCase_,batch_size=lowerCAmelCase_,**lowerCAmelCase_     )



    def   lowerCamelCase_  (	self      :       Dict,__A      :       Optional[Any],__A      :       Optional[Any]=6_4,__A      :       int = 0,__A      :       float = 5_1_2 / 1_5_0_0,__A      :       Optional[int] = 3_2,__A      :       Optional[int] = 1,):
          _lowerCamelCase				:   Optional[int]								=   load_image(lowerCAmelCase_     )
          _lowerCamelCase				:   Dict								=   self.image_processor.size["longest_edge"]
          _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   Tuple								=   self.image_processor.generate_crop_boxes(
              lowerCAmelCase_,lowerCAmelCase_,lowerCAmelCase_,lowerCAmelCase_,lowerCAmelCase_,lowerCAmelCase_     )
          _lowerCamelCase				:   Union[str, Any]								=   self.image_processor(images=lowerCAmelCase_,return_tensors="pt"     )

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

          _lowerCamelCase				:   List[Any]								=   grid_points.shape[1]
          _lowerCamelCase				:   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,lowerCAmelCase_,lowerCAmelCase_     ):
                _lowerCamelCase				:   int								=   grid_points[:, i : i + points_per_batch, :, :]
                _lowerCamelCase				:   Union[str, Any]								=   input_labels[:, i : i + points_per_batch]
                _lowerCamelCase				:   Union[str, 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   lowerCamelCase_  (	self      :       Any,__A      :       List[Any],__A      :       Any=0.88,__A      :       Tuple=0.95,__A      :       str=0,__A      :       Dict=1,):
          _lowerCamelCase				:   Union[str, Any]								=   model_inputs.pop("input_boxes"     )
          _lowerCamelCase				:   Dict								=   model_inputs.pop("is_last"     )
          _lowerCamelCase				:   int								=   model_inputs.pop("original_sizes"     ).tolist()
          _lowerCamelCase				:   str								=   model_inputs.pop("reshaped_input_sizes"     ).tolist()

          _lowerCamelCase				:   int								=   self.model(**lowerCAmelCase_     )

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



    def   lowerCamelCase_  (	self      :       List[Any],__A      :       Optional[int],__A      :       Any=False,__A      :       List[Any]=False,__A      :       Tuple=0.7,):
          _lowerCamelCase				:   Optional[Any]								=   []
          _lowerCamelCase				:   Optional[Any]								=   []
          _lowerCamelCase				:   Tuple								=   []
          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"     )     )

          _lowerCamelCase				:   Dict								=   torch.cat(lowerCAmelCase_     )
          _lowerCamelCase				:   Tuple								=   torch.cat(lowerCAmelCase_     )
          _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   Optional[Any]								=   self.image_processor.post_process_for_mask_generation(
              lowerCAmelCase_,lowerCAmelCase_,lowerCAmelCase_,lowerCAmelCase_     )

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

          _lowerCamelCase				:   List[Any]								=   {}
          if output_rle_mask:
                _lowerCamelCase				:   Optional[int]								=   rle_mask

          if output_bboxes_mask:
                _lowerCamelCase				:   int								=   bounding_boxes

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

'''simple docstring'''






import warnings

from ..trainer import Trainer
from ..utils import logging


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

class  UpperCAmelCase__						(       A ):



    def __init__(	self      :       int,__A      :       Any=None,**__A      :       Optional[Any]     ):
          warnings.warn(
              "`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` "
              "instead.",__A,)
          super().__init__(args=__A,**__A     )