





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(SCREAMING_SNAKE_CASE__  )
class 		SCREAMING_SNAKE_CASE__    (	SCREAMING_SNAKE_CASE__  ):


      def __init__(self	:				int    ,      **a__	:				str				):





         """simple docstring"""



         super().__init__(**a__				)
         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(a__				)


      def        a			(self	:				Optional[int]    ,      **a__	:				int				):





         """simple docstring"""



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


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





         """simple docstring"""



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


      def        a			(self	:				List[Any]    ,      a__	:				Union[str, Any]    ,      a__	:				Tuple=64    ,      a__	:				int = 0    ,      a__	:				float = 512 / 1500    ,      a__	:				Optional[int] = 32    ,      a__	:				Optional[int] = 1    ,      ):





         """simple docstring"""



         __snake_case								=       load_image(a__				)
         __snake_case								=       self.image_processor.size['longest_edge']
         __snake_case								=       self.image_processor.generate_crop_boxes(
             a__    ,      a__    ,      a__    ,      a__    ,      a__    ,      a__				)
         __snake_case								=       self.image_processor(images=a__    ,      return_tensors='''pt'''				)

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

         __snake_case								=       grid_points.shape[1]
         __snake_case								=       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    ,      a__    ,      a__				):
            __snake_case								=       grid_points[:, i : i + points_per_batch, :, :]
            __snake_case								=       input_labels[:, i : i + points_per_batch]
            __snake_case								=       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        a			(self	:				Any    ,      a__	:				List[str]    ,      a__	:				str=0.8_8    ,      a__	:				Optional[int]=0.9_5    ,      a__	:				str=0    ,      a__	:				Optional[int]=1    ,      ):





         """simple docstring"""



         __snake_case								=       model_inputs.pop('''input_boxes'''				)
         __snake_case								=       model_inputs.pop('''is_last'''				)
         __snake_case								=       model_inputs.pop('''original_sizes'''				).tolist()
         __snake_case								=       model_inputs.pop('''reshaped_input_sizes'''				).tolist()

         __snake_case								=       self.model(**a__				)

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


      def        a			(self	:				Optional[Any]    ,      a__	:				str    ,      a__	:				Tuple=False    ,      a__	:				Any=False    ,      a__	:				Tuple=0.7    ,      ):





         """simple docstring"""



         __snake_case								=       []
         __snake_case								=       []
         __snake_case								=       []
         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'''				)				)

         __snake_case								=       torch.cat(a__				)
         __snake_case								=       torch.cat(a__				)
         __snake_case								=       self.image_processor.post_process_for_mask_generation(
             a__    ,      a__    ,      a__    ,      a__				)

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

         __snake_case								=       {}
         if output_rle_mask:
            __snake_case								=       rle_mask

         if output_bboxes_mask:
            __snake_case								=       bounding_boxes

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








# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
    import torch

class 		SCREAMING_SNAKE_CASE__    (	TensorFormatter[Mapping, 'torch.Tensor', Mapping]  ):


      def __init__(self	:				Optional[int]    ,      a__	:				Tuple=None    ,      **a__	:				Optional[int]				):





         """simple docstring"""



         super().__init__(features=a__				)
         __snake_case								=       torch_tensor_kwargs
         import torch  # noqa import torch at initialization


      def        a			(self	:				Union[str, Any]    ,      a__	:				Union[str, Any]				):





         """simple docstring"""



         import torch

         if isinstance(a__    ,      a__				) and column:
            if all(
                isinstance(a__    ,      torch.Tensor				) and x.shape == column[0].shape and x.dtype == column[0].dtype
                for x in column				):
               return torch.stack(a__				)
         return column


      def        a			(self	:				Optional[Any]    ,      a__	:				str				):





         """simple docstring"""



         import torch

         if isinstance(a__    ,      (str, bytes, type(a__				))				):
            return value
         elif isinstance(a__    ,      (np.character, np.ndarray)				) and np.issubdtype(value.dtype    ,      np.character				):
            return value.tolist()

         __snake_case								=       {}

         if isinstance(a__    ,      (np.number, np.ndarray)				) and np.issubdtype(value.dtype    ,      np.integer				):
            __snake_case								=       {'''dtype''': torch.intaa}
         elif isinstance(a__    ,      (np.number, np.ndarray)				) and np.issubdtype(value.dtype    ,      np.floating				):
            __snake_case								=       {'''dtype''': torch.floataa}
         elif config.PIL_AVAILABLE and "PIL" in sys.modules:
            import PIL.Image

            if isinstance(a__    ,      PIL.Image.Image				):
               __snake_case								=       np.asarray(a__				)
         return torch.tensor(a__    ,      **{**default_dtype, **self.torch_tensor_kwargs}				)


      def        a			(self	:				Optional[int]    ,      a__	:				str				):





         """simple docstring"""



         import torch

         # support for torch, tf, jax etc.
         if hasattr(a__    ,      '''__array__'''				) and not isinstance(a__    ,      torch.Tensor				):
            __snake_case								=       data_struct.__array__()
         # support for nested types like struct of list of struct
         if isinstance(a__    ,      np.ndarray				):
            if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
               return self._consolidate([self.recursive_tensorize(a__				) for substruct in data_struct]				)
         elif isinstance(a__    ,      (list, tuple)				):
            return self._consolidate([self.recursive_tensorize(a__				) for substruct in data_struct]				)
         return self._tensorize(a__				)


      def        a			(self	:				Optional[Any]    ,      a__	:				dict				):





         """simple docstring"""



         return map_nested(self._recursive_tensorize    ,      a__    ,      map_list=a__				)


      def        a			(self	:				Optional[int]    ,      a__	:				pa.Table				):





         """simple docstring"""



         __snake_case								=       self.numpy_arrow_extractor().extract_row(a__				)
         __snake_case								=       self.python_features_decoder.decode_row(a__				)
         return self.recursive_tensorize(a__				)


      def        a			(self	:				List[Any]    ,      a__	:				pa.Table				):





         """simple docstring"""



         __snake_case								=       self.numpy_arrow_extractor().extract_column(a__				)
         __snake_case								=       self.python_features_decoder.decode_column(a__    ,      pa_table.column_names[0]				)
         __snake_case								=       self.recursive_tensorize(a__				)
         __snake_case								=       self._consolidate(a__				)
         return column


      def        a			(self	:				Optional[int]    ,      a__	:				pa.Table				):





         """simple docstring"""



         __snake_case								=       self.numpy_arrow_extractor().extract_batch(a__				)
         __snake_case								=       self.python_features_decoder.decode_batch(a__				)
         __snake_case								=       self.recursive_tensorize(a__				)
         for column_name in batch:
            __snake_case								=       self._consolidate(batch[column_name]				)
         return batch

