




'''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

_lowercase  =			logging.get_logger(__name__)
@add_end_docstrings(UpperCAmelCase__						)
class 						a_				(			UpperCAmelCase__						):




	def __init__(       self     :  Any       ,		**__lowerCAmelCase     :  Union[str, Any]      ):
							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   lowercase__   (       self     :  Optional[Any]       ,		**__lowerCAmelCase     :  Union[str, Any]      ):
							__snake_case						 =   {}
							__snake_case						 =   {}
							__snake_case						 =   {}
							# preprocess args
							if "points_per_batch" in kwargs:
													__snake_case						 =   kwargs['points_per_batch']
							if "points_per_crop" in kwargs:
													__snake_case						 =   kwargs['points_per_crop']
							if "crops_n_layers" in kwargs:
													__snake_case						 =   kwargs['crops_n_layers']
							if "crop_overlap_ratio" in kwargs:
													__snake_case						 =   kwargs['crop_overlap_ratio']
							if "crop_n_points_downscale_factor" in kwargs:
													__snake_case						 =   kwargs['crop_n_points_downscale_factor']
							# postprocess args
							if "pred_iou_thresh" in kwargs:
													__snake_case						 =   kwargs['pred_iou_thresh']
							if "stability_score_offset" in kwargs:
													__snake_case						 =   kwargs['stability_score_offset']
							if "mask_threshold" in kwargs:
													__snake_case						 =   kwargs['mask_threshold']
							if "stability_score_thresh" in kwargs:
													__snake_case						 =   kwargs['stability_score_thresh']
							if "crops_nms_thresh" in kwargs:
													__snake_case						 =   kwargs['crops_nms_thresh']
							if "output_rle_mask" in kwargs:
													__snake_case						 =   kwargs['output_rle_mask']
							if "output_bboxes_mask" in kwargs:
													__snake_case						 =   kwargs['output_bboxes_mask']
							return preprocess_kwargs, forward_params, postprocess_kwargs




	def __call__(       self     :  List[Any]       ,		__lowerCAmelCase     :  Optional[Any]       ,		*__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  Tuple=None       ,		__lowerCAmelCase     :  Optional[int]=None       ,		**__lowerCAmelCase     :  Dict      ):
							return super().__call__(__lowerCAmelCase       ,		*__lowerCAmelCase       ,		num_workers=__lowerCAmelCase       ,		batch_size=__lowerCAmelCase       ,		**__lowerCAmelCase      )




	def   lowercase__   (       self     :  str       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  List[str]=6_4       ,		__lowerCAmelCase     :  int = 0       ,		__lowerCAmelCase     :  float = 5_1_2 / 1_5_0_0       ,		__lowerCAmelCase     :  Optional[int] = 3_2       ,		__lowerCAmelCase     :  Optional[int] = 1       ,		):
							__snake_case						 =   load_image(__lowerCAmelCase      )
							__snake_case						 =   self.image_processor.size['longest_edge']
							__snake_case ,       __snake_case ,       __snake_case ,       __snake_case						 =   self.image_processor.generate_crop_boxes(
							    __lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase      )
							__snake_case						 =   self.image_processor(images=__lowerCAmelCase       ,		return_tensors='pt'      )

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

							__snake_case						 =   grid_points.shape[1]
							__snake_case						 =   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      ):
													__snake_case						 =   grid_points[:, i : i + points_per_batch, :, :]
													__snake_case						 =   input_labels[:, i : i + points_per_batch]
													__snake_case						 =   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   lowercase__   (       self     :  Optional[int]       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  List[str]=0.88       ,		__lowerCAmelCase     :  Optional[int]=0.95       ,		__lowerCAmelCase     :  Dict=0       ,		__lowerCAmelCase     :  Optional[Any]=1       ,		):
							__snake_case						 =   model_inputs.pop('input_boxes'      )
							__snake_case						 =   model_inputs.pop('is_last'      )
							__snake_case						 =   model_inputs.pop('original_sizes'      ).tolist()
							__snake_case						 =   model_inputs.pop('reshaped_input_sizes'      ).tolist()

							__snake_case						 =   self.model(**__lowerCAmelCase      )

							# post processing happens here in order to avoid CPU GPU copies of ALL the masks
							__snake_case						 =   model_outputs['pred_masks']
							__snake_case						 =   self.image_processor.post_process_masks(
							    __lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase       ,		binarize=__lowerCAmelCase      )
							__snake_case						 =   model_outputs['iou_scores']
							__snake_case ,       __snake_case ,       __snake_case						 =   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   lowercase__   (       self     :  int       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  Union[str, Any]=False       ,		__lowerCAmelCase     :  int=False       ,		__lowerCAmelCase     :  Dict=0.7       ,		):
							__snake_case						 =   []
							__snake_case						 =   []
							__snake_case						 =   []
							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						 =   torch.cat(__lowerCAmelCase      )
							__snake_case						 =   torch.cat(__lowerCAmelCase      )
							__snake_case ,       __snake_case ,       __snake_case ,       __snake_case						 =   self.image_processor.post_process_for_mask_generation(
							    __lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase      )

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

							__snake_case						 =   {}
							if output_rle_mask:
													__snake_case						 =   rle_mask

							if output_bboxes_mask:
													__snake_case						 =   bounding_boxes

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







'''simple docstring'''


from __future__ import annotations

import math
def 						lowerCamelCase__   (				a    ,	a							):
						__snake_case						 =   u
						for i in range(1    ,	a							):
												__snake_case						 =   temp * (u - i)
						return temp
def 						lowerCamelCase__   (				):
						__snake_case						 =   int(input('enter the numbers of values: '							)							)
						__snake_case						 =   []
						for _ in range(a							):
												y.append([]							)
						for i in range(a							):
												for j in range(a							):
																		y[i].append(a							)
																		__snake_case						 =   0

						print('enter the values of parameters in a list: '							)
						__snake_case						 =   list(map(a    ,	input().split()							)							)

						print('enter the values of corresponding parameters: '							)
						for i in range(a							):
												__snake_case						 =   float(input()							)

						__snake_case						 =   int(input('enter the value to interpolate: '							)							)
						__snake_case						 =   (value - x[0]) / (x[1] - x[0])

						# for calculating forward difference table

						for i in range(1    ,	a							):
												for j in range(n - i							):
																		__snake_case						 =   y[j + 1][i - 1] - y[j][i - 1]

						__snake_case						 =   y[0][0]
						for i in range(1    ,	a							):
												summ += (ucal(a    ,	a							) * y[0][i]) / math.factorial(a							)

						print(f'the value at {value} is {summ}'							)


if __name__ == "__main__":
	main()

