





"""simple docstring"""





from __future__ import annotations

import numpy as np


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						List[Any]			):







      '''simple docstring'''




      return np.maximum(0							,						_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
       print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]













"""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(a       )
class 					_lowerCAmelCase   (     a       ):
       """simple docstring"""




       def __init__(    self    , **__UpperCAmelCase      ):



         '''simple docstring'''

         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      snake_case						(    self    , **__UpperCAmelCase      ):



         '''simple docstring'''

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






       def __call__(    self    , __UpperCAmelCase    , *__UpperCAmelCase    , __UpperCAmelCase=None    , __UpperCAmelCase=None    , **__UpperCAmelCase      ):



         '''simple docstring'''

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






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase=6_4    , __UpperCAmelCase = 0    , __UpperCAmelCase = 5_1_2 / 1_5_0_0    , __UpperCAmelCase = 3_2    , __UpperCAmelCase = 1    , ):



         '''simple docstring'''

         lowerCAmelCase__			:Union[str, Any]											=							load_image(__UpperCAmelCase      )
         lowerCAmelCase__			:int											=							self.image_processor.size['longest_edge']
         lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:int											=							self.image_processor.generate_crop_boxes(
             __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase      )
         lowerCAmelCase__			:Union[str, Any]											=							self.image_processor(images=__UpperCAmelCase    , return_tensors='pt'      )

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

         lowerCAmelCase__			:List[Any]											=							grid_points.shape[1]
         lowerCAmelCase__			:Union[str, Any]											=							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      ):
           lowerCAmelCase__			:Optional[Any]											=							grid_points[:, i : i + points_per_batch, :, :]
           lowerCAmelCase__			:List[str]											=							input_labels[:, i : i + points_per_batch]
           lowerCAmelCase__			:List[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      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase=0.88    , __UpperCAmelCase=0.95    , __UpperCAmelCase=0    , __UpperCAmelCase=1    , ):



         '''simple docstring'''

         lowerCAmelCase__			:Any											=							model_inputs.pop('input_boxes'      )
         lowerCAmelCase__			:Optional[int]											=							model_inputs.pop('is_last'      )
         lowerCAmelCase__			:Dict											=							model_inputs.pop('original_sizes'      ).tolist()
         lowerCAmelCase__			:Dict											=							model_inputs.pop('reshaped_input_sizes'      ).tolist()

         lowerCAmelCase__			:Optional[int]											=							self.model(**__UpperCAmelCase      )

         # post processing happens here in order to avoid CPU GPU copies of ALL the masks
         lowerCAmelCase__			:int											=							model_outputs['pred_masks']
         lowerCAmelCase__			:Optional[Any]											=							self.image_processor.post_process_masks(
             __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , binarize=__UpperCAmelCase      )
         lowerCAmelCase__			:Any											=							model_outputs['iou_scores']
         lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:Tuple											=							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      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase=False    , __UpperCAmelCase=False    , __UpperCAmelCase=0.7    , ):



         '''simple docstring'''

         lowerCAmelCase__			:Dict											=							[]
         lowerCAmelCase__			:Optional[Any]											=							[]
         lowerCAmelCase__			: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'      )      )

         lowerCAmelCase__			:Dict											=							torch.cat(__UpperCAmelCase      )
         lowerCAmelCase__			:Dict											=							torch.cat(__UpperCAmelCase      )
         lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:Any											=							self.image_processor.post_process_for_mask_generation(
             __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase      )

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

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

         if output_bboxes_mask:
           lowerCAmelCase__			:Optional[int]											=							bounding_boxes

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