





'''simple docstring'''




import os







def _lowerCAmelCase     (      _lowerCAmelCase = "matrix.txt"    )->     int:
 with open(os.path.join(os.path.dirname(_lowerCAmelCase    )       ,    _lowerCAmelCase    )    ) as in_file:
  __UpperCAmelCase					=   in_file.read()

 __UpperCAmelCase					=   [[int(_lowerCAmelCase    ) for cell in row.split(','    )] for row in data.strip().splitlines()]
 __UpperCAmelCase					=   [[0 for cell in row] for row in grid]
 __UpperCAmelCase					=   len(grid[0]    )

 __UpperCAmelCase					=   [[0 for i in range(_lowerCAmelCase    )] for j in range(_lowerCAmelCase    )]
 __UpperCAmelCase					=   grid[0][0]
 for i in range(1       ,    _lowerCAmelCase    ):
  __UpperCAmelCase					=   grid[0][i] + dp[0][i - 1]
 for i in range(1       ,    _lowerCAmelCase    ):
  __UpperCAmelCase					=   grid[i][0] + dp[i - 1][0]

 for i in range(1       ,    _lowerCAmelCase    ):
  for j in range(1       ,    _lowerCAmelCase    ):
   __UpperCAmelCase					=   grid[i][j] + min(dp[i - 1][j]       ,    dp[i][j - 1]    )

 return dp[-1][-1]


if __name__ == "__main__":
    print(F"""{solution() = }""")












'''simple docstring'''




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_A:					Union[str, Any]							=				get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
    _A:					Optional[Any]							=				json.load(f)






@require_torch
class UpperCAmelCase							(		unittest.TestCase							):



    def 					__lowerCamelCase						(  self     ,			__A					):
     return FSMTTokenizer.from_pretrained(__A					)



    def 					__lowerCamelCase						(  self     ,			__A					):
     __UpperCAmelCase					=   FSMTForConditionalGeneration.from_pretrained(__A					).to(__A					)
     if torch_device == "cuda":
      model.half()
     return model




    @parameterized.expand(
        [
            ['en-ru', 2_6.0],
            ['ru-en', 2_2.0],
            ['en-de', 2_2.0],
            ['de-en', 2_9.0],
        ]					)
    @slow
    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     # note: this test is not testing the best performance since it only evals a small batch
     # but it should be enough to detect a regression in the output quality
     __UpperCAmelCase					=   f'facebook/wmt19-{pair}'
     __UpperCAmelCase					=   self.get_tokenizer(__A					)
     __UpperCAmelCase					=   self.get_model(__A					)

     __UpperCAmelCase					=   bleu_data[pair]['src']
     __UpperCAmelCase					=   bleu_data[pair]['tgt']

     __UpperCAmelCase					=   tokenizer(__A     ,			return_tensors='pt'     ,			truncation=__A     ,			padding='longest'					).to(__A					)
     __UpperCAmelCase					=   model.generate(
         input_ids=batch.input_ids     ,			num_beams=8     ,			)
     __UpperCAmelCase					=   tokenizer.batch_decode(
         __A     ,			skip_special_tokens=__A     ,			clean_up_tokenization_spaces=__A					)
     __UpperCAmelCase					=   calculate_bleu(__A     ,			__A					)
     print(__A					)
     self.assertGreaterEqual(scores['bleu']     ,			__A					)





