



'''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

UpperCamelCase__       :	Dict    = logging.get_logger(__name__)






@add_end_docstrings(_SCREAMING_SNAKE_CASE    )
class 						_UpperCamelCase  (							_SCREAMING_SNAKE_CASE    ):


			'''simple docstring'''

			def __init__(							self     : Union[str, Any]							,     **lowerCAmelCase__     : str						):





					"""simple docstring"""






					super().__init__(**lowerCAmelCase__						)
					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(lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Union[str, Any]							,     **lowerCAmelCase__     : Optional[int]						):





					"""simple docstring"""






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

			def __call__(							self     : List[Any]							,     lowerCAmelCase__     : int							,     *lowerCAmelCase__     : Tuple							,     lowerCAmelCase__     : Optional[Any]=None							,     lowerCAmelCase__     : int=None							,     **lowerCAmelCase__     : Dict						):





					"""simple docstring"""






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

			def UpperCamelCase__					(							self     : List[str]							,     lowerCAmelCase__     : Tuple							,     lowerCAmelCase__     : Dict=6_4							,     lowerCAmelCase__     : int = 0							,     lowerCAmelCase__     : float = 5_1_2 / 1_5_0_0							,     lowerCAmelCase__     : Optional[int] = 3_2							,     lowerCAmelCase__     : Optional[int] = 1							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		int           =		load_image(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Any           =		self.image_processor.size["""longest_edge"""]
					__SCREAMING_SNAKE_CASE       :		Dict           =		self.image_processor.generate_crop_boxes(
					    lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		int           =		self.image_processor(images=lowerCAmelCase__							,     return_tensors="""pt"""						)

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

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		grid_points.shape[1]
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		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							,     lowerCAmelCase__							,     lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE       :		List[Any]           =		grid_points[:, i : i + points_per_batch, :, :]
							__SCREAMING_SNAKE_CASE       :		List[str]           =		input_labels[:, i : i + points_per_batch]
							__SCREAMING_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 UpperCamelCase__					(							self     : Tuple							,     lowerCAmelCase__     : Optional[Any]							,     lowerCAmelCase__     : Optional[int]=0.88							,     lowerCAmelCase__     : int=0.95							,     lowerCAmelCase__     : str=0							,     lowerCAmelCase__     : List[str]=1							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Any           =		model_inputs.pop("""input_boxes"""						)
					__SCREAMING_SNAKE_CASE       :		Any           =		model_inputs.pop("""is_last"""						)
					__SCREAMING_SNAKE_CASE       :		int           =		model_inputs.pop("""original_sizes"""						).tolist()
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		model_inputs.pop("""reshaped_input_sizes"""						).tolist()

					__SCREAMING_SNAKE_CASE       :		Any           =		self.model(**lowerCAmelCase__						)

					# post processing happens here in order to avoid CPU GPU copies of ALL the masks
					__SCREAMING_SNAKE_CASE       :		str           =		model_outputs["""pred_masks"""]
					__SCREAMING_SNAKE_CASE       :		Dict           =		self.image_processor.post_process_masks(
					    lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     binarize=lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		model_outputs["""iou_scores"""]
					__SCREAMING_SNAKE_CASE       :		Any           =		self.image_processor.filter_masks(
					    masks[0]							,     iou_scores[0]							,     original_sizes[0]							,     input_boxes[0]							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     )
					return {
					    "masks": masks,
					    "is_last": is_last,
					    "boxes": boxes,
					    "iou_scores": iou_scores,
					}



			def UpperCamelCase__					(							self     : Union[str, Any]							,     lowerCAmelCase__     : Union[str, Any]							,     lowerCAmelCase__     : Optional[Any]=False							,     lowerCAmelCase__     : int=False							,     lowerCAmelCase__     : Tuple=0.7							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[str]           =		[]
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		[]
					__SCREAMING_SNAKE_CASE       :		str           =		[]
					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"""						)						)

					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		torch.cat(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		torch.cat(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Dict           =		self.image_processor.post_process_for_mask_generation(
					    lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		defaultdict(lowerCAmelCase__						)
					for output in model_outputs:
							for k, v in output.items():
									extra[k].append(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		List[Any]           =		{}
					if output_rle_mask:
							__SCREAMING_SNAKE_CASE       :		List[Any]           =		rle_mask

					if output_bboxes_mask:
							__SCREAMING_SNAKE_CASE       :		Any           =		bounding_boxes

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



'''simple docstring'''






from math import ceil







def 				UpperCamelCase_			(    A__					:   int = 10_01					):
  '''simple docstring'''





  lowerCAmelCase_			:      List[Any]			=   1

  for i in range(1							,    int(ceil(n / 2.0					)					)					):
    lowerCAmelCase_			:      int			=   2 * i + 1
    lowerCAmelCase_			:      Tuple			=   2 * i
    lowerCAmelCase_			:      Tuple			=   total + 4 * odd**2 - 6 * even

  return total


if __name__ == "__main__":
  import sys

  if len(sys.argv) == 1:
    print(solution())
  else:
    try:
      __A					:  str											=      int(sys.argv[1])
      print(solution(n))
    except ValueError:
      print("Invalid entry - please enter a number")



