
'''simple docstring'''





import os
def 	snake_case_							(				):
      snake_case_    							=				os.path.dirname(os.path.realpath(_a      )      )
      snake_case_    							=				os.path.join(_a			,       "triangle.txt"      )

      with open(_a      ) as f:
            snake_case_    							=				f.readlines()

      snake_case_    							=				[[int(_a      ) for y in x.rstrip("\r\n"      ).split(" "      )] for x in triangle]

      for i in range(1			,       len(_a      )      ):
            for j in range(len(a[i]      )      ):
                  snake_case_    							=				a[i - 1][j] if j != len(a[i - 1]      ) else 0
                  snake_case_    							=				a[i - 1][j - 1] if j > 0 else 0
                  a[i][j] += max(_a			,       _a      )
      return max(a[-1]      )


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



'''simple docstring'''





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
     from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
     A							     = False

if is_vision_available():
     from PIL import Image

     from transformers import (
         AutoProcessor,
         PixaStructImageProcessor,
         PixaStructProcessor,
         PreTrainedTokenizerFast,
         TaTokenizer,
     )



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11       ,				reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'       ,				)
@require_vision
@require_torch
class 			lowerCAmelCase__ (							unittest.TestCase							):



  '''simple docstring'''






  def 		_lowerCamelCase						(self	:			Tuple     ) ->							Tuple:
        snake_case_    							=				tempfile.mkdtemp()

        snake_case_    							=				PixaStructImageProcessor()
        snake_case_    							=				TaTokenizer.from_pretrained("t5-small"     )

        snake_case_    							=				PixaStructProcessor(lowerCAmelCase_						,			lowerCAmelCase_     )

        processor.save_pretrained(self.tmpdirname     )

  def 		_lowerCamelCase						(self	:			List[str]						,			**lowerCAmelCase_	:			int     ) ->							Optional[Any]:
        return AutoProcessor.from_pretrained(self.tmpdirname						,			**lowerCAmelCase_     ).tokenizer

  def 		_lowerCamelCase						(self	:			Union[str, Any]						,			**lowerCAmelCase_	:			List[str]     ) ->							List[Any]:
        return AutoProcessor.from_pretrained(self.tmpdirname						,			**lowerCAmelCase_     ).image_processor

  def 		_lowerCamelCase						(self	:			int     ) ->							List[str]:
        shutil.rmtree(self.tmpdirname     )

  def 		_lowerCamelCase						(self	:			Any     ) ->							Optional[Any]:
        snake_case_    							=				[np.random.randint(2_55						,			size=(3, 30, 4_00)						,			dtype=np.uinta     )]

        snake_case_    							=				[Image.fromarray(np.moveaxis(lowerCAmelCase_						,			0						,			-1     )     ) for x in image_inputs]

        return image_inputs

  def 		_lowerCamelCase						(self	:			Union[str, Any]     ) ->							Dict:
        snake_case_    							=				PixaStructProcessor(tokenizer=self.get_tokenizer()						,			image_processor=self.get_image_processor()     )
        processor.save_pretrained(self.tmpdirname     )

        snake_case_    							=				self.get_tokenizer(bos_token="(BOS)"						,			eos_token="(EOS)"     )
        snake_case_    							=				self.get_image_processor(do_normalize=lowerCAmelCase_						,			padding_value=1.0     )

        snake_case_    							=				PixaStructProcessor.from_pretrained(
            self.tmpdirname						,			bos_token="(BOS)"						,			eos_token="(EOS)"						,			do_normalize=lowerCAmelCase_						,			padding_value=1.0     )

        self.assertEqual(processor.tokenizer.get_vocab()						,			tokenizer_add_kwargs.get_vocab()     )
        self.assertIsInstance(processor.tokenizer						,			lowerCAmelCase_     )

        self.assertEqual(processor.image_processor.to_json_string()						,			image_processor_add_kwargs.to_json_string()     )
        self.assertIsInstance(processor.image_processor						,			lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Dict     ) ->							Optional[int]:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				self.prepare_image_inputs()

        snake_case_    							=				image_processor(lowerCAmelCase_						,			return_tensors="np"     )
        snake_case_    							=				processor(images=lowerCAmelCase_						,			return_tensors="np"     )

        for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()						,			input_processor[key].sum()						,			delta=1E-2     )

  def 		_lowerCamelCase						(self	:			Tuple     ) ->							List[str]:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				"lower newer"

        snake_case_    							=				processor(text=lowerCAmelCase_     )

        snake_case_    							=				tokenizer(lowerCAmelCase_						,			return_token_type_ids=lowerCAmelCase_						,			add_special_tokens=lowerCAmelCase_     )

        for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]						,			encoded_processor[key]     )

  def 		_lowerCamelCase						(self	:			str     ) ->							List[str]:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				"lower newer"
        snake_case_    							=				self.prepare_image_inputs()

        snake_case_    							=				processor(text=lowerCAmelCase_						,			images=lowerCAmelCase_     )

        self.assertListEqual(
            list(inputs.keys()     )						,			["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]     )

        # test if it raises when no input is passed
        with pytest.raises(lowerCAmelCase_     ):
              processor()

  def 		_lowerCamelCase						(self	:			Optional[Any]     ) ->							Any:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				"lower newer"
        snake_case_    							=				self.prepare_image_inputs()

        snake_case_    							=				processor(text=lowerCAmelCase_						,			images=lowerCAmelCase_     )

        snake_case_    							=				[5_12, 10_24, 20_48, 40_96]
        snake_case_    							=				[7_70, 7_70, 7_70, 7_70]
        # with text
        for i, max_patch in enumerate(lowerCAmelCase_     ):
              snake_case_    							=				processor(text=lowerCAmelCase_						,			images=lowerCAmelCase_						,			max_patches=lowerCAmelCase_     )
              self.assertEqual(inputs["flattened_patches"][0].shape[0]						,			lowerCAmelCase_     )
              self.assertEqual(inputs["flattened_patches"][0].shape[1]						,			expected_hidden_size[i]     )

        # without text input
        for i, max_patch in enumerate(lowerCAmelCase_     ):
              snake_case_    							=				processor(images=lowerCAmelCase_						,			max_patches=lowerCAmelCase_     )
              self.assertEqual(inputs["flattened_patches"][0].shape[0]						,			lowerCAmelCase_     )
              self.assertEqual(inputs["flattened_patches"][0].shape[1]						,			expected_hidden_size[i]     )

  def 		_lowerCamelCase						(self	:			int     ) ->							Any:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        snake_case_    							=				processor.batch_decode(lowerCAmelCase_     )
        snake_case_    							=				tokenizer.batch_decode(lowerCAmelCase_     )

        self.assertListEqual(lowerCAmelCase_						,			lowerCAmelCase_     )


  def 		_lowerCamelCase						(self	:			Optional[int]     ) ->							str:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				"lower newer"
        snake_case_    							=				self.prepare_image_inputs()

        snake_case_    							=				processor(text=lowerCAmelCase_						,			images=lowerCAmelCase_     )

        # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
        self.assertListEqual(
            list(inputs.keys()     )						,			["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]     )

        snake_case_    							=				processor(text=lowerCAmelCase_     )

        # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
        self.assertListEqual(list(inputs.keys()     )						,			["input_ids", "attention_mask"]     )

