'''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_      	=	logging.get_logger(__name__)




@add_end_docstrings(_SCREAMING_SNAKE_CASE					)
class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):




						def __init__(    self :       Dict							,							**UpperCamelCase_ :       Any      )     ->				int:
													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      __lowerCamelCase       (    self :       List[str]							,							**UpperCamelCase_ :       int      )     ->				int:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = {}
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = {}
													SCREAMING_SNAKE_CASE__     :Dict						    = {}
													# 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[str]						    = kwargs['points_per_crop']
													if "crops_n_layers" in kwargs:
																				SCREAMING_SNAKE_CASE__     :str						    = kwargs['crops_n_layers']
													if "crop_overlap_ratio" in kwargs:
																				SCREAMING_SNAKE_CASE__     :Any						    = kwargs['crop_overlap_ratio']
													if "crop_n_points_downscale_factor" in kwargs:
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = kwargs['crop_n_points_downscale_factor']
													# postprocess args
													if "pred_iou_thresh" in kwargs:
																				SCREAMING_SNAKE_CASE__     :Optional[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__     :Optional[Any]						    = kwargs['mask_threshold']
													if "stability_score_thresh" in kwargs:
																				SCREAMING_SNAKE_CASE__     :Dict						    = kwargs['stability_score_thresh']
													if "crops_nms_thresh" in kwargs:
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = kwargs['crops_nms_thresh']
													if "output_rle_mask" in kwargs:
																				SCREAMING_SNAKE_CASE__     :str						    = kwargs['output_rle_mask']
													if "output_bboxes_mask" in kwargs:
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = kwargs['output_bboxes_mask']
													return preprocess_kwargs, forward_params, postprocess_kwargs




						def __call__(    self :       Dict							,							UpperCamelCase_ :       List[str]							,							*UpperCamelCase_ :       List[str]							,							UpperCamelCase_ :       Dict=None							,							UpperCamelCase_ :       Any=None							,							**UpperCamelCase_ :       List[Any]      )     ->				Tuple:
													return super().__call__(SCREAMING_SNAKE_CASE_							,							*SCREAMING_SNAKE_CASE_							,							num_workers=SCREAMING_SNAKE_CASE_							,							batch_size=SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_      )




						def      __lowerCamelCase       (    self :       Dict							,							UpperCamelCase_ :       Union[str, Any]							,							UpperCamelCase_ :       Union[str, Any]=64							,							UpperCamelCase_ :       int = 0							,							UpperCamelCase_ :       float = 5_12 / 15_00							,							UpperCamelCase_ :       Optional[int] = 32							,							UpperCamelCase_ :       Optional[int] = 1							,							)     ->				Tuple:
													SCREAMING_SNAKE_CASE__     :List[Any]						    = load_image(SCREAMING_SNAKE_CASE_      )
													SCREAMING_SNAKE_CASE__     :Any						    = self.image_processor.size['longest_edge']
													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Any						    = self.image_processor.generate_crop_boxes(
													    SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.image_processor(images=SCREAMING_SNAKE_CASE_							,							return_tensors='pt'      )

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

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = grid_points.shape[1]
													SCREAMING_SNAKE_CASE__     :str						    = 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_      ):
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = grid_points[:, i : i + points_per_batch, :, :]
																				SCREAMING_SNAKE_CASE__     :Dict						    = input_labels[:, i : i + points_per_batch]
																				SCREAMING_SNAKE_CASE__     :str						    = 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 :       Optional[Any]							,							UpperCamelCase_ :       Dict							,							UpperCamelCase_ :       Union[str, Any]=0.88							,							UpperCamelCase_ :       Optional[int]=0.95							,							UpperCamelCase_ :       List[Any]=0							,							UpperCamelCase_ :       List[Any]=1							,							)     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = model_inputs.pop('input_boxes'      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = model_inputs.pop('is_last'      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = model_inputs.pop('original_sizes'      ).tolist()
													SCREAMING_SNAKE_CASE__     :Tuple						    = model_inputs.pop('reshaped_input_sizes'      ).tolist()

													SCREAMING_SNAKE_CASE__     :int						    = self.model(**SCREAMING_SNAKE_CASE_      )

													# post processing happens here in order to avoid CPU GPU copies of ALL the masks
													SCREAMING_SNAKE_CASE__     :List[str]						    = model_outputs['pred_masks']
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.image_processor.post_process_masks(
													    SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							binarize=SCREAMING_SNAKE_CASE_      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = model_outputs['iou_scores']
													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :List[str]						    = 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      __lowerCamelCase       (    self :       List[str]							,							UpperCamelCase_ :       Any							,							UpperCamelCase_ :       List[str]=False							,							UpperCamelCase_ :       Any=False							,							UpperCamelCase_ :       List[str]=0.7							,							)     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :int						    = []
													SCREAMING_SNAKE_CASE__     :Dict						    = []
													SCREAMING_SNAKE_CASE__     :Optional[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'      )      )

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = torch.cat(SCREAMING_SNAKE_CASE_      )
													SCREAMING_SNAKE_CASE__     :List[str]						    = torch.cat(SCREAMING_SNAKE_CASE_      )
													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :str						    = self.image_processor.post_process_for_mask_generation(
													    SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_      )

													SCREAMING_SNAKE_CASE__     :str						    = defaultdict(SCREAMING_SNAKE_CASE_      )
													for output in model_outputs:
																				for k, v in output.items():
																											extra[k].append(SCREAMING_SNAKE_CASE_      )

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

													if output_bboxes_mask:
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = bounding_boxes

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








from __future__ import annotations

from math import pi, sqrt




def 							a		(				snake_case__:   float		,  snake_case__:   float   ):


				'''simple docstring'''

				if inductance <= 0:
								raise ValueError('''Inductance cannot be 0 or negative'''   )

				elif capacitance <= 0:
								raise ValueError('''Capacitance cannot be 0 or negative'''   )

				else:
								return (
								    "Resonant frequency",
								    float(1 / (2 * pi * (sqrt(inductance * capacitance   )))   ),
								)


if __name__ == "__main__":
						import doctest

						doctest.testmod()




