import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 						a__							(   _UpperCamelCase		):
			A__				:   Optional[int]								=			['image_processor', 'tokenizer']
			A__				:   str								=			'LayoutLMv2ImageProcessor'
			A__				:   Any								=			('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')



			def __init__(						self      ,						UpperCAmelCase=None      ,						UpperCAmelCase=None      ,						**UpperCAmelCase    ) ->    Union[str, Any]:
								__a							=   None
								if "feature_extractor" in kwargs:
													warnings.warn(
													    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
													    ' instead.'      ,						__a      ,						)
													__a							=   kwargs.pop('feature_extractor'    )

								__a							=   image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
													raise ValueError('You need to specify an `image_processor`.'    )
								if tokenizer is None:
													raise ValueError('You need to specify a `tokenizer`.'    )

								super().__init__(__a      ,						__a    )



			def __call__(						self      ,						UpperCAmelCase      ,						UpperCAmelCase = None      ,						UpperCAmelCase = None      ,						UpperCAmelCase = None      ,						UpperCAmelCase = None      ,						UpperCAmelCase = True      ,						UpperCAmelCase = False      ,						UpperCAmelCase = False      ,						UpperCAmelCase = None      ,						UpperCAmelCase = 0      ,						UpperCAmelCase = None      ,						UpperCAmelCase = None      ,						UpperCAmelCase = None      ,						UpperCAmelCase = False      ,						UpperCAmelCase = False      ,						UpperCAmelCase = False      ,						UpperCAmelCase = False      ,						UpperCAmelCase = True      ,						UpperCAmelCase = None      ,						**UpperCAmelCase      ,						) ->    BatchEncoding:





								if self.image_processor.apply_ocr and (boxes is not None):
													raise ValueError(
													    'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'    )

								if self.image_processor.apply_ocr and (word_labels is not None):
													raise ValueError(
													    'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'    )

								if return_overflowing_tokens is True and return_offsets_mapping is False:
													raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.'    )

								# first, apply the image processor
								__a							=   self.image_processor(images=__a      ,						return_tensors=__a    )

								# second, apply the tokenizer
								if text is not None and self.image_processor.apply_ocr and text_pair is None:
													if isinstance(__a      ,						__a    ):
																		__a							=   [text]  # add batch dimension (as the image processor always adds a batch dimension)
													__a							=   features['words']

								__a							=   self.tokenizer(
								    text=text if text is not None else features['words']      ,						text_pair=text_pair if text_pair is not None else None      ,						boxes=boxes if boxes is not None else features['boxes']      ,						word_labels=__a      ,						add_special_tokens=__a      ,						padding=__a      ,						truncation=__a      ,						max_length=__a      ,						stride=__a      ,						pad_to_multiple_of=__a      ,						return_token_type_ids=__a      ,						return_attention_mask=__a      ,						return_overflowing_tokens=__a      ,						return_special_tokens_mask=__a      ,						return_offsets_mapping=__a      ,						return_length=__a      ,						verbose=__a      ,						return_tensors=__a      ,						**__a      ,						)

								# add pixel values
								__a							=   features.pop('pixel_values'    )
								if return_overflowing_tokens is True:
													__a							=   self.get_overflowing_images(__a      ,						encoded_inputs['overflow_to_sample_mapping']    )
								__a							=   images

								return encoded_inputs



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    Tuple:
								__a							=   []
								for sample_idx in overflow_to_sample_mapping:
													images_with_overflow.append(images[sample_idx]    )

								if len(__a    ) != len(__a    ):
													raise ValueError(
													    'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
													    f''' {len(__a    )} and {len(__a    )}'''    )

								return images_with_overflow



			def __SCREAMING_SNAKE_CASE							(						self      ,						*UpperCAmelCase      ,						**UpperCAmelCase    ) ->    Tuple:
								return self.tokenizer.batch_decode(*__a      ,						**__a    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						*UpperCAmelCase      ,						**UpperCAmelCase    ) ->    List[str]:
								return self.tokenizer.decode(*__a      ,						**__a    )



			@property
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Optional[int]:
								return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]



			@property
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:
								warnings.warn(
								    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'      ,						__a      ,						)
								return self.image_processor_class







			@property
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Optional[int]:
								warnings.warn(
								    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'      ,						__a      ,						)
								return self.image_processor







import os
def 						UpperCamelCase_(			)       -> int:
 UpperCAmelCase__							=				os.path.dirname(os.path.realpath(snake_case__   )   )
 UpperCAmelCase__							=				os.path.join(snake_case__  ,    'triangle.txt'   )

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

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


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



