




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       a					(       _A      ):

     '''simple docstring'''





     def __init__(     self       :      List[Any] ,   **__snake_case       :      Optional[Any]    ):
         super().__init__(**__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(__snake_case    )
     def   lowerCamelCase_       (     self       :      Any ,   **__snake_case       :      Optional[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       :      Optional[int] ,   __snake_case       :      Optional[int] ,   *__snake_case       :      Optional[int] ,   __snake_case       :      List[Any]=None ,   __snake_case       :      Tuple=None ,   **__snake_case       :      Dict    ):
         return super().__call__(__snake_case ,   *__snake_case ,   num_workers=__snake_case ,   batch_size=__snake_case ,   **__snake_case    )
     def   lowerCamelCase_       (     self       :      List[Any] ,   __snake_case       :      Dict ,   __snake_case       :      str=64 ,   __snake_case       :      int = 0 ,   __snake_case       :      float = 5_12 / 15_00 ,   __snake_case       :      Optional[int] = 32 ,   __snake_case       :      Optional[int] = 1 ,   ):
         UpperCAmelCase_										=					load_image(__snake_case    )
         UpperCAmelCase_										=					self.image_processor.size['''longest_edge''']
         UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_										=					self.image_processor.generate_crop_boxes(
             __snake_case ,   __snake_case ,   __snake_case ,   __snake_case ,   __snake_case ,   __snake_case    )
         UpperCAmelCase_										=					self.image_processor(images=__snake_case ,   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(__snake_case ,   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 ,   __snake_case ,   __snake_case    ):
             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   lowerCamelCase_       (     self       :      int ,   __snake_case       :      List[str] ,   __snake_case       :      int=0.88 ,   __snake_case       :      Union[str, Any]=0.95 ,   __snake_case       :      List[str]=0 ,   __snake_case       :      Dict=1 ,   ):
         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(**__snake_case    )

         # 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(
             __snake_case ,   __snake_case ,   __snake_case ,   __snake_case ,   binarize=__snake_case    )
         UpperCAmelCase_										=					model_outputs['''iou_scores''']
         UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_										=					self.image_processor.filter_masks(
             masks[0] ,   iou_scores[0] ,   original_sizes[0] ,   input_boxes[0] ,   __snake_case ,   __snake_case ,   __snake_case ,   __snake_case ,   )
         return {
             "masks": masks,
             "is_last": is_last,
             "boxes": boxes,
             "iou_scores": iou_scores,
         }




     def   lowerCamelCase_       (     self       :      Any ,   __snake_case       :      Optional[int] ,   __snake_case       :      Tuple=False ,   __snake_case       :      Optional[int]=False ,   __snake_case       :      List[str]=0.7 ,   ):
         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(__snake_case    )
         UpperCAmelCase_										=					torch.cat(__snake_case    )
         UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_										=					self.image_processor.post_process_for_mask_generation(
             __snake_case ,   __snake_case ,   __snake_case ,   __snake_case    )

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

         UpperCAmelCase_										=					{}
         if output_rle_mask:
             UpperCAmelCase_										=					rle_mask

         if output_bboxes_mask:
             UpperCAmelCase_										=					bounding_boxes

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











import numpy as np




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						np.array				)  ->							np.array:
    return (2 / (1 + np.exp(-2 * vector				))) - 1


if __name__ == "__main__":
 import doctest

 doctest.testmod()





