






'''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

__A    	=   logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase_							)
class a_    (    UpperCamelCase_							):







   def __init__(self					,   **__a)			->      Optional[Any]:
      """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     SCREAMING_SNAKE_CASE__					(self					,   **__a)			->      int:
      """simple docstring"""






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







   def __call__(self					,   __a					,   *__a					,   __a=None					,   __a=None					,   **__a)			->      Dict:
      """simple docstring"""






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







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a=6_4					,   __a = 0					,   __a = 5_1_2 / 1_5_0_0					,   __a = 3_2					,   __a = 1					,   )			->      List[Any]:
      """simple docstring"""






      __snake_case				:      Tuple     							= load_image(__a)
      __snake_case				:      List[Any]     							= self.image_processor.size['longest_edge']
      __snake_case				:      List[Any]     							= self.image_processor.generate_crop_boxes(
          __a					,   __a					,   __a					,   __a					,   __a					,   __a)
      __snake_case				:      Union[str, Any]     							= self.image_processor(images=__a					,   return_tensors='pt')

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

      __snake_case				:      Optional[Any]     							= grid_points.shape[1]
      __snake_case				:      Dict     							= 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				:      Union[str, Any]     							= grid_points[:, i : i + points_per_batch, :, :]
         __snake_case				:      Any     							= input_labels[:, i : i + points_per_batch]
         __snake_case				:      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     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a=0.88					,   __a=0.95					,   __a=0					,   __a=1					,   )			->      Optional[int]:
      """simple docstring"""






      __snake_case				:      List[Any]     							= model_inputs.pop('input_boxes')
      __snake_case				:      str     							= model_inputs.pop('is_last')
      __snake_case				:      int     							= model_inputs.pop('original_sizes').tolist()
      __snake_case				:      str     							= model_inputs.pop('reshaped_input_sizes').tolist()

      __snake_case				:      Tuple     							= self.model(**__a)

      # post processing happens here in order to avoid CPU GPU copies of ALL the masks
      __snake_case				:      str     							= model_outputs['pred_masks']
      __snake_case				:      Any     							= self.image_processor.post_process_masks(
          __a					,   __a					,   __a					,   __a					,   binarize=__a)
      __snake_case				:      Optional[Any]     							= model_outputs['iou_scores']
      __snake_case				:      Optional[int]     							= 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     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a=False					,   __a=False					,   __a=0.7					,   )			->      List[str]:
      """simple docstring"""






      __snake_case				:      int     							= []
      __snake_case				:      Optional[Any]     							= []
      __snake_case				:      int     							= []
      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				:      Union[str, Any]     							= torch.cat(__a)
      __snake_case				:      Tuple     							= torch.cat(__a)
      __snake_case				:      List[str]     							= self.image_processor.post_process_for_mask_generation(
          __a					,   __a					,   __a					,   __a)

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

      __snake_case				:      List[str]     							= {}
      if output_rle_mask:
         __snake_case				:      Optional[int]     							= rle_mask

      if output_bboxes_mask:
         __snake_case				:      int     							= bounding_boxes

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







'''simple docstring'''






def        _SCREAMING_SNAKE_CASE  (				A  :       float  ,		A  :       list[float]  )    -> float:







   """simple docstring"""
   if discount_rate < 0:
      raise ValueError('Discount rate cannot be negative'  )
   if not cash_flows:
      raise ValueError('Cash flows list cannot be empty'  )
   __snake_case				:      List[str]     							= sum(
       cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(A  )  )
   return round(A  ,		ndigits=2  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()