
import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
							import torch





@require_torch
@require_optimum
@slow
class     lowerCamelCase      (       unittest.TestCase					):







	def  snake_case__     (     self      :List[Any]			)		->      Optional[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= '''hf-internal-testing/tiny-random-t5'''
				SCREAMING_SNAKE_CASE     						= AutoTokenizer.from_pretrained(lowercase			)
				SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

				SCREAMING_SNAKE_CASE     						= tokenizer('''This is me'''   ,     return_tensors='''pt'''			)

				SCREAMING_SNAKE_CASE     						= model.to_bettertransformer()

				self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				SCREAMING_SNAKE_CASE     						= model.generate(**lowercase			)

				SCREAMING_SNAKE_CASE     						= model.reverse_bettertransformer()

				self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				with tempfile.TemporaryDirectory() as tmpdirname:
							model.save_pretrained(lowercase			)

							SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

							self.assertFalse(
							    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

							SCREAMING_SNAKE_CASE     						= model_reloaded.generate(**lowercase			)
							self.assertTrue(torch.allclose(lowercase   ,     lowercase			)			)







	def  snake_case__     (     self      :Union[str, Any]			)		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= '''hf-internal-testing/tiny-random-t5'''
				SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

				SCREAMING_SNAKE_CASE     						= model.to_bettertransformer()

				with tempfile.TemporaryDirectory() as tmpdirname:
							with self.assertRaises(lowercase			):
										model.save_pretrained(lowercase			)

							SCREAMING_SNAKE_CASE     						= model.reverse_bettertransformer()
							model.save_pretrained(lowercase			)

import os


def    a      ( a = "matrix.txt"	)     ->int:




			'''simple docstring'''


			with open(os.path.join(os.path.dirname(a	)		,						a	)	) as in_file:
						SCREAMING_SNAKE_CASE     						= in_file.read()

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

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

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

			return dp[-1][-1]


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