



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__  :  Any								=					logging.get_logger(__name__)



@add_end_docstrings(UpperCamelCase_						)
class 				__snake_case						(     UpperCamelCase_						):




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




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




							def    UpperCAmelCase__			(       self     :       str				,				A_     :       str				,				A_     :       int=6_4				,				A_     :       Any = 0				,				A_     :       Optional[Any] = 5_1_2 / 1_5_0_0				,				A_     :       List[str] = 3_2				,				A_     :       Tuple = 1				,				):
									lowerCAmelCase_			:     List[Any]									= load_image(lowerCamelCase_)
									lowerCAmelCase_			:     List[Any]									= self.image_processor.size['''longest_edge''']
									lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[int]									= self.image_processor.generate_crop_boxes(
									    lowerCamelCase_				,				lowerCamelCase_				,				lowerCamelCase_				,				lowerCamelCase_				,				lowerCamelCase_				,				lowerCamelCase_)
									lowerCAmelCase_			:     Dict									= self.image_processor(images=lowerCamelCase_				,				return_tensors='''pt''')

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

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

									lowerCAmelCase_			:     List[str]									= self.model(**lowerCamelCase_)

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

									lowerCAmelCase_			:     Any									= torch.cat(lowerCamelCase_)
									lowerCAmelCase_			:     Optional[int]									= torch.cat(lowerCamelCase_)
									lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_			:     Any									= self.image_processor.post_process_for_mask_generation(
									    lowerCamelCase_				,				lowerCamelCase_				,				lowerCamelCase_				,				lowerCamelCase_)

									lowerCAmelCase_			:     Optional[int]									= defaultdict(lowerCamelCase_)
									for output in model_outputs:
											for k, v in output.items():
													extra[k].append(lowerCamelCase_)

									lowerCAmelCase_			:     Optional[int]									= {}
									if output_rle_mask:
											lowerCAmelCase_			:     List[Any]									= rle_mask

									if output_bboxes_mask:
											lowerCAmelCase_			:     List[str]									= bounding_boxes

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










import cmath
import math






def     a(      A       :    float ,     A       :    float ,     A       :    float ,     A       :    float       )		->       complex:
					"""simple docstring"""





					a					     =   math.radians(A       )
					a					     =   math.radians(A       )

					# Convert voltage and current to rectangular form
					a					     =   cmath.rect(A ,     A       )
					a					     =   cmath.rect(A ,     A       )

					# Calculate apparent power
					return voltage_rect * current_rect


if __name__ == "__main__":
				import doctest

				doctest.testmod()
