
'''simple docstring'''


from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
	from PIL import Image

	from ..image_utils import load_image

if is_torch_available():
	from ..models.auto.modeling_auto import (
	    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
	    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
	    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
	    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
	)


__SCREAMING_SNAKE_CASE						=      logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE						=      Dict[str, Any]
__SCREAMING_SNAKE_CASE						=      List[Prediction]




@add_end_docstrings(_SCREAMING_SNAKE_CASE	)
class     __snake_case			(    _SCREAMING_SNAKE_CASE	):




		def __init__(  self					:Union[str, Any]			,    *_SCREAMING_SNAKE_CASE					:int			,    **_SCREAMING_SNAKE_CASE					:Dict)					->			Dict:






								'''simple docstring'''






								super().__init__(*_SCREAMING_SNAKE_CASE			,    **_SCREAMING_SNAKE_CASE)

								if self.framework == "tf":
														raise ValueError(F"""The {self.__class__} is only available in PyTorch.""")

								requires_backends(self			,    """vision""")
								self.check_model_type(
								    dict(
								        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
								        + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
								        + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
								        + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()))




		def 	_snake_case	(  self					:List[str]			,    **_SCREAMING_SNAKE_CASE					:Tuple)					->			List[str]:






								'''simple docstring'''






								UpperCamelCase_     :	List[str]					     =     {}
								UpperCamelCase_     :	str					     =     {}
								if "subtask" in kwargs:
														UpperCamelCase_     :	Dict					     =     kwargs["""subtask"""]
														UpperCamelCase_     :	str					     =     kwargs["""subtask"""]
								if "threshold" in kwargs:
														UpperCamelCase_     :	Optional[int]					     =     kwargs["""threshold"""]
								if "mask_threshold" in kwargs:
														UpperCamelCase_     :	Tuple					     =     kwargs["""mask_threshold"""]
								if "overlap_mask_area_threshold" in kwargs:
														UpperCamelCase_     :	Dict					     =     kwargs["""overlap_mask_area_threshold"""]

								return preprocess_kwargs, {}, postprocess_kwargs




		def __call__(  self					:Any			,    _SCREAMING_SNAKE_CASE					:Union[str, Any]			,    **_SCREAMING_SNAKE_CASE					:Optional[int])					->			Union[Predictions, List[Prediction]]:






								'''simple docstring'''






								return super().__call__(_SCREAMING_SNAKE_CASE			,    **_SCREAMING_SNAKE_CASE)




		def 	_snake_case	(  self					:Union[str, Any]			,    _SCREAMING_SNAKE_CASE					:int			,    _SCREAMING_SNAKE_CASE					:str=None)					->			Union[str, Any]:






								'''simple docstring'''






								UpperCamelCase_     :	Dict					     =     load_image(_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	str					     =     [(image.height, image.width)]
								if self.model.config.__class__.__name__ == "OneFormerConfig":
														if subtask is None:
																				UpperCamelCase_     :	List[str]					     =     {}
														else:
																				UpperCamelCase_     :	Any					     =     {"""task_inputs""": [subtask]}
														UpperCamelCase_     :	int					     =     self.image_processor(images=[image]			,    return_tensors="""pt"""			,    **_SCREAMING_SNAKE_CASE)
														UpperCamelCase_     :	Union[str, Any]					     =     self.tokenizer(
														    inputs["""task_inputs"""]			,    padding="""max_length"""			,    max_length=self.model.config.task_seq_len			,    return_tensors=self.framework			,    )["""input_ids"""]
								else:
														UpperCamelCase_     :	int					     =     self.image_processor(images=[image]			,    return_tensors="""pt""")
								UpperCamelCase_     :	str					     =     target_size
								return inputs




		def 	_snake_case	(  self					:Any			,    _SCREAMING_SNAKE_CASE					:str)					->			List[str]:






								'''simple docstring'''






								UpperCamelCase_     :	Union[str, Any]					     =     model_inputs.pop("""target_size""")
								UpperCamelCase_     :	Union[str, Any]					     =     self.model(**_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	Optional[Any]					     =     target_size
								return model_outputs





		def 	_snake_case	(  self					:Optional[Any]			,    _SCREAMING_SNAKE_CASE					:Any			,    _SCREAMING_SNAKE_CASE					:Optional[Any]=None			,    _SCREAMING_SNAKE_CASE					:List[str]=0.9			,    _SCREAMING_SNAKE_CASE					:Dict=0.5			,    _SCREAMING_SNAKE_CASE					:Optional[int]=0.5)					->			Optional[int]:






								'''simple docstring'''






								UpperCamelCase_     :	List[str]					     =     None
								if subtask in {"panoptic", None} and hasattr(self.image_processor			,    """post_process_panoptic_segmentation"""):
														UpperCamelCase_     :	Dict					     =     self.image_processor.post_process_panoptic_segmentation
								elif subtask in {"instance", None} and hasattr(self.image_processor			,    """post_process_instance_segmentation"""):
														UpperCamelCase_     :	List[str]					     =     self.image_processor.post_process_instance_segmentation

								if fn is not None:
														UpperCamelCase_     :	Any					     =     fn(
														    _SCREAMING_SNAKE_CASE			,    threshold=_SCREAMING_SNAKE_CASE			,    mask_threshold=_SCREAMING_SNAKE_CASE			,    overlap_mask_area_threshold=_SCREAMING_SNAKE_CASE			,    target_sizes=model_outputs["""target_size"""]			,    )[0]

														UpperCamelCase_     :	str					     =     []
														UpperCamelCase_     :	int					     =     outputs["""segmentation"""]

														for segment in outputs["segments_info"]:
																				UpperCamelCase_     :	str					     =     (segmentation == segment["""id"""]) * 2_5_5
																				UpperCamelCase_     :	Optional[int]					     =     Image.fromarray(mask.numpy().astype(np.uinta)			,    mode="""L""")
																				UpperCamelCase_     :	Optional[Any]					     =     self.model.config.idalabel[segment["""label_id"""]]
																				UpperCamelCase_     :	Dict					     =     segment["""score"""]
																				annotation.append({"""score""": score, """label""": label, """mask""": mask})

								elif subtask in {"semantic", None} and hasattr(self.image_processor			,    """post_process_semantic_segmentation"""):
														UpperCamelCase_     :	Tuple					     =     self.image_processor.post_process_semantic_segmentation(
														    _SCREAMING_SNAKE_CASE			,    target_sizes=model_outputs["""target_size"""])[0]

														UpperCamelCase_     :	Union[str, Any]					     =     []
														UpperCamelCase_     :	str					     =     outputs.numpy()
														UpperCamelCase_     :	Any					     =     np.unique(_SCREAMING_SNAKE_CASE)

														for label in labels:
																				UpperCamelCase_     :	List[Any]					     =     (segmentation == label) * 2_5_5
																				UpperCamelCase_     :	str					     =     Image.fromarray(mask.astype(np.uinta)			,    mode="""L""")
																				UpperCamelCase_     :	Dict					     =     self.model.config.idalabel[label]
																				annotation.append({"""score""": None, """label""": label, """mask""": mask})
								else:
														raise ValueError(F"""Subtask {subtask} is not supported for model {type(self.model)}""")
								return annotation








'''simple docstring'''






import os






def 						_A					() ->	int:




     '''simple docstring'''



     _a         =				os.path.dirname(os.path.realpath(lowerCAmelCase__		)		)
     _a         =				os.path.join(lowerCAmelCase__					,    'triangle.txt'		)

     with open(lowerCAmelCase__		) as in_file:
          _a         =				[[int(lowerCAmelCase__		) for i in line.split()] for line in in_file]

     while len(lowerCAmelCase__		) != 1:
          _a         =				triangle.pop()
          _a         =				triangle[-1]
          for j in range(len(lowerCAmelCase__		) - 1		):
               curr_row[j] += max(last_row[j]					,    last_row[j + 1]		)
     return triangle[0][0]


if __name__ == "__main__":
 print(solution())

