






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__  :Dict             =							logging.get_logger(__name__)

@add_end_docstrings(lowerCAmelCase__      )
class 		__a				(    lowerCAmelCase__      ):
		def __init__(      self      ,   **_SCREAMING_SNAKE_CASE						)			->	Union[str, Any]:




									"""simple docstring"""





									super().__init__(**_SCREAMING_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(_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   **_SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									_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      ,   _SCREAMING_SNAKE_CASE      ,   *_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   **_SCREAMING_SNAKE_CASE						)			->	Optional[int]:




									"""simple docstring"""





									return super().__call__(_SCREAMING_SNAKE_CASE      ,   *_SCREAMING_SNAKE_CASE      ,   num_workers=_SCREAMING_SNAKE_CASE      ,   batch_size=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=64      ,   _SCREAMING_SNAKE_CASE = 0      ,   _SCREAMING_SNAKE_CASE = 512 / 1500      ,   _SCREAMING_SNAKE_CASE = 32      ,   _SCREAMING_SNAKE_CASE = 1      ,   )			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      load_image(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      self.image_processor.size['longest_edge']
									_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      self.image_processor.generate_crop_boxes(
									    _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      self.image_processor(images=_SCREAMING_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(_SCREAMING_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      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_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      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=0.88      ,   _SCREAMING_SNAKE_CASE=0.95      ,   _SCREAMING_SNAKE_CASE=0      ,   _SCREAMING_SNAKE_CASE=1      ,   )			->	Dict:




									"""simple docstring"""





									_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(**_SCREAMING_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(
									    _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   binarize=_SCREAMING_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]      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   )
									return {
									    "masks": masks,
									    "is_last": is_last,
									    "boxes": boxes,
									    "iou_scores": iou_scores,
									}






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=False      ,   _SCREAMING_SNAKE_CASE=False      ,   _SCREAMING_SNAKE_CASE=0.7      ,   )			->	int:




									"""simple docstring"""





									_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(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      torch.cat(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      self.image_processor.post_process_for_mask_generation(
									    _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

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




from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt

def 	lowerCAmelCase__			(      a__:		Sequence[float]      ,  a__:		int      ,  a__:		int	)  ->		tuple[int | None, int | None, float]:


							'''simple docstring'''





							if not arr:
														return None, None, 0
							if low == high:
														return low, high, arr[low]

							_UpperCAmelCase					=      (low + high) // 2
							_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      max_subarray(a__      ,  a__      ,  a__	)
							_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      max_subarray(a__      ,  mid + 1      ,  a__	)
							_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      max_cross_sum(a__      ,  a__      ,  a__      ,  a__	)
							if left_sum >= right_sum and left_sum >= cross_sum:
														return left_low, left_high, left_sum
							elif right_sum >= left_sum and right_sum >= cross_sum:
														return right_low, right_high, right_sum
							return cross_left, cross_right, cross_sum

def 	lowerCAmelCase__			(      a__:		Sequence[float]      ,  a__:		int      ,  a__:		int      ,  a__:		int	)  ->		tuple[int, int, float]:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      float('-inf'	), -1
							_UpperCAmelCase		,	_UpperCAmelCase					=      float('-inf'	), -1

							_UpperCAmelCase					=      0
							for i in range(a__      ,  low - 1      ,  -1	):
														summ += arr[i]
														if summ > left_sum:
																					_UpperCAmelCase					=      summ
																					_UpperCAmelCase					=      i

							_UpperCAmelCase					=      0
							for i in range(mid + 1      ,  high + 1	):
														summ += arr[i]
														if summ > right_sum:
																					_UpperCAmelCase					=      summ
																					_UpperCAmelCase					=      i

							return max_left, max_right, (left_sum + right_sum)

def 	lowerCAmelCase__			(      a__:		int	)  ->		float:


							'''simple docstring'''





							_UpperCAmelCase					=      [randint(1      ,  a__	) for _ in range(a__	)]
							_UpperCAmelCase					=      time.time()
							max_subarray(a__      ,  0      ,  input_size - 1	)
							_UpperCAmelCase					=      time.time()
							return end - start

def 	lowerCAmelCase__			(      )  ->		None:


							'''simple docstring'''





							_UpperCAmelCase					=      [1_0, 1_0_0, 1_0_0_0, 1_0_0_0_0, 5_0_0_0_0, 1_0_0_0_0_0, 2_0_0_0_0_0, 3_0_0_0_0_0, 4_0_0_0_0_0, 5_0_0_0_0_0]
							_UpperCAmelCase					=      [time_max_subarray(a__	) for input_size in input_sizes]
							print('No of Inputs\t\tTime Taken'	)
							for input_size, runtime in zip(a__      ,  a__	):
														print(a__      ,  '\t\t'      ,  a__	)
							plt.plot(a__      ,  a__	)
							plt.xlabel('Number of Inputs'	)
							plt.ylabel('Time taken in seconds'	)
							plt.show()


if __name__ == "__main__":
		from doctest import testmod

		testmod()
