



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(lowerCAmelCase__ )
class 			__lowerCAmelCase  (						lowerCAmelCase__ ):




 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 							lowerCamelCase   (    self						,					**__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     {}
    __lowerCamelCase    				=     {}
    __lowerCamelCase    				=     {}
    # preprocess args
    if "points_per_batch" in kwargs:
       __lowerCamelCase    				=     kwargs['''points_per_batch''']
    if "points_per_crop" in kwargs:
       __lowerCamelCase    				=     kwargs['''points_per_crop''']
    if "crops_n_layers" in kwargs:
       __lowerCamelCase    				=     kwargs['''crops_n_layers''']
    if "crop_overlap_ratio" in kwargs:
       __lowerCamelCase    				=     kwargs['''crop_overlap_ratio''']
    if "crop_n_points_downscale_factor" in kwargs:
       __lowerCamelCase    				=     kwargs['''crop_n_points_downscale_factor''']
    # postprocess args
    if "pred_iou_thresh" in kwargs:
       __lowerCamelCase    				=     kwargs['''pred_iou_thresh''']
    if "stability_score_offset" in kwargs:
       __lowerCamelCase    				=     kwargs['''stability_score_offset''']
    if "mask_threshold" in kwargs:
       __lowerCamelCase    				=     kwargs['''mask_threshold''']
    if "stability_score_thresh" in kwargs:
       __lowerCamelCase    				=     kwargs['''stability_score_thresh''']
    if "crops_nms_thresh" in kwargs:
       __lowerCamelCase    				=     kwargs['''crops_nms_thresh''']
    if "output_rle_mask" in kwargs:
       __lowerCamelCase    				=     kwargs['''output_rle_mask''']
    if "output_bboxes_mask" in kwargs:
       __lowerCamelCase    				=     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 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase=64						,					__UpperCAmelCase = 0						,					__UpperCAmelCase = 512 / 1500						,					__UpperCAmelCase = 32						,					__UpperCAmelCase = 1						,					):



    '''simple docstring'''
    __lowerCamelCase    				=     load_image(__UpperCAmelCase					)
    __lowerCamelCase    				=     self.image_processor.size['''longest_edge''']
    __lowerCamelCase				,__lowerCamelCase				,__lowerCamelCase				,__lowerCamelCase    				=     self.image_processor.generate_crop_boxes(
        __UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase					)
    __lowerCamelCase    				=     self.image_processor(images=__UpperCAmelCase						,					return_tensors='''pt'''					)

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

    __lowerCamelCase    				=     grid_points.shape[1]
    __lowerCamelCase    				=     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    				=     grid_points[:, i : i + points_per_batch, :, :]
       __lowerCamelCase    				=     input_labels[:, i : i + points_per_batch]
       __lowerCamelCase    				=     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						,					__UpperCAmelCase						,					__UpperCAmelCase=0.88						,					__UpperCAmelCase=0.95						,					__UpperCAmelCase=0						,					__UpperCAmelCase=1						,					):



    '''simple docstring'''
    __lowerCamelCase    				=     model_inputs.pop('''input_boxes'''					)
    __lowerCamelCase    				=     model_inputs.pop('''is_last'''					)
    __lowerCamelCase    				=     model_inputs.pop('''original_sizes'''					).tolist()
    __lowerCamelCase    				=     model_inputs.pop('''reshaped_input_sizes'''					).tolist()

    __lowerCamelCase    				=     self.model(**__UpperCAmelCase					)

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



    '''simple docstring'''
    __lowerCamelCase    				=     []
    __lowerCamelCase    				=     []
    __lowerCamelCase    				=     []
    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    				=     torch.cat(__UpperCAmelCase					)
    __lowerCamelCase    				=     torch.cat(__UpperCAmelCase					)
    __lowerCamelCase				,__lowerCamelCase				,__lowerCamelCase				,__lowerCamelCase    				=     self.image_processor.post_process_for_mask_generation(
        __UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase					)

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

    __lowerCamelCase    				=     {}
    if output_rle_mask:
       __lowerCamelCase    				=     rle_mask

    if output_bboxes_mask:
       __lowerCamelCase    				=     bounding_boxes

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











from __future__ import annotations





def 		a__      (		_UpperCamelCase      :			list[float]				):
   if len(_UpperCamelCase				) < 2:
      raise ValueError('''Monogons and Digons are not polygons in the Euclidean space'''				)
   if any(i <= 0 for i in nums				):
      raise ValueError('''All values must be greater than 0'''				)
   __lowerCamelCase    				=     nums.copy()
   copy_nums.sort()
   return copy_nums[-1] < sum(copy_nums[:-1]				)


if __name__ == "__main__":
  import doctest

  doctest.testmod()






