'''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						: Optional[int]						=						logging.get_logger(__name__)





@add_end_docstrings(_UpperCAmelCase)
class     __magic_name__						(    _UpperCAmelCase):







			def __init__(	self				:  Any				,			**lowercase_				:  Dict    ):
										super().__init__(**lowercase_    )
										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(lowercase_    )







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







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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple				,			lowercase_				:  Dict				,			lowercase_				:  List[Any]=64				,			lowercase_				:  int = 0				,			lowercase_				:  float = 512 / 1500				,			lowercase_				:  Optional[int] = 32				,			lowercase_				:  Optional[int] = 1				,			):
										lowercase_					:			Tuple										=       load_image(lowercase_    )
										lowercase_					:			str										=       self.image_processor.size["""longest_edge"""]
										lowercase_						,						lowercase_						,						lowercase_						,						lowercase_					:			str										=       self.image_processor.generate_crop_boxes(
										    lowercase_				,			lowercase_				,			lowercase_				,			lowercase_				,			lowercase_				,			lowercase_    )
										lowercase_					:			int										=       self.image_processor(images=lowercase_				,			return_tensors="""pt"""    )

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

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

										lowercase_					:			Any										=       self.model(**lowercase_    )

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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  Tuple				,			lowercase_				:  List[str]=False				,			lowercase_				:  List[str]=False				,			lowercase_				:  str=0.7				,			):
										lowercase_					:			Optional[Any]										=       []
										lowercase_					:			Union[str, Any]										=       []
										lowercase_					:			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"""    )    )

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

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

										lowercase_					:			Dict										=       {}
										if output_rle_mask:
																	lowercase_					:			Tuple										=       rle_mask

										if output_bboxes_mask:
																	lowercase_					:			Tuple										=       bounding_boxes

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






'''simple docstring'''




import operator as op




def 							lowerCamelCase	(      UpperCAmelCase__       :					Optional[Any] )			->      int:
							lowercase_					:			Optional[Any]										=       []
							lowercase_					:			str										=       lambda UpperCAmelCase__      ,      UpperCAmelCase__ : int(x / y )  # noqa: E731 integer division operation
							lowercase_					:			Optional[Any]										=       {
							    """^""": op.pow,
							    """*""": op.mul,
							    """/""": div,
							    """+""": op.add,
							    """-""": op.sub,
							}  # operators & their respective operation

							# print table header
							print("""Symbol""".center(8 )      ,      """Action""".center(12 )      ,      """Stack"""      ,      sep=""" | """ )
							print("""-""" * (30 + len(UpperCAmelCase__ )) )

							for x in post_fix:
														if x.isdigit():  # if x in digit
																					stack.append(UpperCAmelCase__ )  # append x to stack
																					# output in tabular format
																					print(x.rjust(8 )      ,      ("""push(""" + x + """)""").ljust(12 )      ,      """,""".join(UpperCAmelCase__ )      ,      sep=""" | """ )
														else:
																					lowercase_					:			str										=       stack.pop()  # pop stack
																					# output in tabular format
																					print("""""".rjust(8 )      ,      ("""pop(""" + b + """)""").ljust(12 )      ,      """,""".join(UpperCAmelCase__ )      ,      sep=""" | """ )

																					lowercase_					:			Optional[int]										=       stack.pop()  # pop stack
																					# output in tabular format
																					print("""""".rjust(8 )      ,      ("""pop(""" + a + """)""").ljust(12 )      ,      """,""".join(UpperCAmelCase__ )      ,      sep=""" | """ )

																					stack.append(
																					    str(opr[x](int(UpperCAmelCase__ )      ,      int(UpperCAmelCase__ ) ) ) )  # evaluate the 2 values popped from stack & push result to stack
																					# output in tabular format
																					print(
																					    x.rjust(8 )      ,      ("""push(""" + a + x + b + """)""").ljust(12 )      ,      """,""".join(UpperCAmelCase__ )      ,      sep=""" | """      ,      )

							return int(stack[0] )


if __name__ == "__main__":
						_lowercase						: Tuple						=						input("\n\nEnter a Postfix Equation (space separated) = ").split(" ")
						print("\n\tResult = ", solve(Postfix))





