






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__   :						str													=   logging.get_logger(__name__)


@add_end_docstrings(UpperCAmelCase__     )
class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def __init__(  self		,		**_UpperCAmelCase			)      ->			Optional[Any]:
     super().__init__(**_UpperCAmelCase			)
     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(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		**_UpperCAmelCase			)      ->			Any:
     UpperCamelCase_						=  {}
     UpperCamelCase_						=  {}
     UpperCamelCase_						=  {}
     # preprocess args
     if "points_per_batch" in kwargs:
        UpperCamelCase_						=  kwargs['points_per_batch']
     if "points_per_crop" in kwargs:
        UpperCamelCase_						=  kwargs['points_per_crop']
     if "crops_n_layers" in kwargs:
        UpperCamelCase_						=  kwargs['crops_n_layers']
     if "crop_overlap_ratio" in kwargs:
        UpperCamelCase_						=  kwargs['crop_overlap_ratio']
     if "crop_n_points_downscale_factor" in kwargs:
        UpperCamelCase_						=  kwargs['crop_n_points_downscale_factor']
     # postprocess args
     if "pred_iou_thresh" in kwargs:
        UpperCamelCase_						=  kwargs['pred_iou_thresh']
     if "stability_score_offset" in kwargs:
        UpperCamelCase_						=  kwargs['stability_score_offset']
     if "mask_threshold" in kwargs:
        UpperCamelCase_						=  kwargs['mask_threshold']
     if "stability_score_thresh" in kwargs:
        UpperCamelCase_						=  kwargs['stability_score_thresh']
     if "crops_nms_thresh" in kwargs:
        UpperCamelCase_						=  kwargs['crops_nms_thresh']
     if "output_rle_mask" in kwargs:
        UpperCamelCase_						=  kwargs['output_rle_mask']
     if "output_bboxes_mask" in kwargs:
        UpperCamelCase_						=  kwargs['output_bboxes_mask']
     return preprocess_kwargs, forward_params, postprocess_kwargs




  def __call__(  self		,		_UpperCAmelCase		,		*_UpperCAmelCase		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		**_UpperCAmelCase			)      ->			List[str]:
     return super().__call__(_UpperCAmelCase		,		*_UpperCAmelCase		,		num_workers=_UpperCAmelCase		,		batch_size=_UpperCAmelCase		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase=64		,		_UpperCAmelCase = 0		,		_UpperCAmelCase = 512 / 1500		,		_UpperCAmelCase = 32		,		_UpperCAmelCase = 1		,		)      ->			Optional[Any]:
     UpperCamelCase_						=  load_image(_UpperCAmelCase			)
     UpperCamelCase_						=  self.image_processor.size['longest_edge']
     UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_						=  self.image_processor.generate_crop_boxes(
         _UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)
     UpperCamelCase_						=  self.image_processor(images=_UpperCAmelCase		,		return_tensors='pt'			)

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

     UpperCamelCase_						=  grid_points.shape[1]
     UpperCamelCase_						=  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		,		_UpperCAmelCase		,		_UpperCAmelCase			):
        UpperCamelCase_						=  grid_points[:, i : i + points_per_batch, :, :]
        UpperCamelCase_						=  input_labels[:, i : i + points_per_batch]
        UpperCamelCase_						=  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		,		_UpperCAmelCase		,		_UpperCAmelCase=0.8_8		,		_UpperCAmelCase=0.9_5		,		_UpperCAmelCase=0		,		_UpperCAmelCase=1		,		)      ->			List[Any]:
     UpperCamelCase_						=  model_inputs.pop('input_boxes'			)
     UpperCamelCase_						=  model_inputs.pop('is_last'			)
     UpperCamelCase_						=  model_inputs.pop('original_sizes'			).tolist()
     UpperCamelCase_						=  model_inputs.pop('reshaped_input_sizes'			).tolist()

     UpperCamelCase_						=  self.model(**_UpperCAmelCase			)

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




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase=False		,		_UpperCAmelCase=False		,		_UpperCAmelCase=0.7		,		)      ->			Any:
     UpperCamelCase_						=  []
     UpperCamelCase_						=  []
     UpperCamelCase_						=  []
     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'			)			)

     UpperCamelCase_						=  torch.cat(_UpperCAmelCase			)
     UpperCamelCase_						=  torch.cat(_UpperCAmelCase			)
     UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_						=  self.image_processor.post_process_for_mask_generation(
         _UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)

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

     UpperCamelCase_						=  {}
     if output_rle_mask:
        UpperCamelCase_						=  rle_mask

     if output_bboxes_mask:
        UpperCamelCase_						=  bounding_boxes

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







from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCamelCase  =logging.get_logger(__name__)




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	['''pixel_values''']






     def __init__(				self    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = 3_2    ,    __SCREAMING_SNAKE_CASE=PILImageResampling.BILINEAR    ,    __SCREAMING_SNAKE_CASE = True    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> None:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					do_resize
           UpperCamelCase__     : Tuple											=					do_rescale
           UpperCamelCase__     : Dict											=					size_divisor
           UpperCamelCase__     : str											=					resample
           super().__init__(**__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE	)	-> np.ndarray:




           """simple docstring"""







           UpperCamelCase__					,UpperCamelCase__     : Optional[Any]											=					get_image_size(__SCREAMING_SNAKE_CASE	)
           # Rounds the height and width down to the closest multiple of size_divisor
           UpperCamelCase__     : List[Any]											=					height // size_divisor * size_divisor
           UpperCamelCase__     : Dict											=					width // size_divisor * size_divisor
           UpperCamelCase__     : str											=					resize(__SCREAMING_SNAKE_CASE    ,    (new_h, new_w)    ,    resample=__SCREAMING_SNAKE_CASE    ,    data_format=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)
           return image






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE	)	-> np.ndarray:




           """simple docstring"""







           return rescale(image=__SCREAMING_SNAKE_CASE    ,    scale=__SCREAMING_SNAKE_CASE    ,    data_format=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE=None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = ChannelDimension.FIRST    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> BatchFeature:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					do_resize if do_resize is not None else self.do_resize
           UpperCamelCase__     : List[Any]											=					do_rescale if do_rescale is not None else self.do_rescale
           UpperCamelCase__     : Union[str, Any]											=					size_divisor if size_divisor is not None else self.size_divisor
           UpperCamelCase__     : List[str]											=					resample if resample is not None else self.resample

           if do_resize and size_divisor is None:
                 raise ValueError('''size_divisor is required for resizing'''	)

           UpperCamelCase__     : List[Any]											=					make_list_of_images(__SCREAMING_SNAKE_CASE	)

           if not valid_images(__SCREAMING_SNAKE_CASE	):
                 raise ValueError('''Invalid image(s)'''	)

           # All transformations expect numpy arrays.
           UpperCamelCase__     : Dict											=					[to_numpy_array(__SCREAMING_SNAKE_CASE	) for img in images]

           if do_resize:
                 UpperCamelCase__     : Optional[int]											=					[self.resize(__SCREAMING_SNAKE_CASE    ,    size_divisor=__SCREAMING_SNAKE_CASE    ,    resample=__SCREAMING_SNAKE_CASE	) for image in images]

           if do_rescale:
                 UpperCamelCase__     : Union[str, Any]											=					[self.rescale(__SCREAMING_SNAKE_CASE    ,    scale=1 / 2_5_5	) for image in images]

           UpperCamelCase__     : List[str]											=					[to_channel_dimension_format(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	) for image in images]

           UpperCamelCase__     : Optional[Any]											=					{'''pixel_values''': images}
           return BatchFeature(data=__SCREAMING_SNAKE_CASE    ,    tensor_type=__SCREAMING_SNAKE_CASE	)


