import os
def       A	(    _lowercase = "input.txt"						):

						with open(os.path.join(os.path.dirname(_lowercase						)		,  _lowercase						)						) as input_file:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							[
												    [int(_lowercase						) for element in line.split(''','''						)]
												    for line in input_file.readlines()
												]

						SCREAMING_SNAKE_CASE							:    Any							=							len(_lowercase						)
						SCREAMING_SNAKE_CASE							:    str							=							len(matrix[0]						)

						SCREAMING_SNAKE_CASE							:    int							=							[[-1 for _ in range(_lowercase						)] for _ in range(_lowercase						)]
						for i in range(_lowercase						):
												SCREAMING_SNAKE_CASE							:    List[str]							=							matrix[i][0]

						for j in range(1		,  _lowercase						):
												for i in range(_lowercase						):
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							minimal_path_sums[i][j - 1] + matrix[i][j]

												for i in range(1		,  _lowercase						):
																		SCREAMING_SNAKE_CASE							:    Any							=							min(
																		    minimal_path_sums[i][j]		,  minimal_path_sums[i - 1][j] + matrix[i][j]						)

												for i in range(rows - 2		,  -1		,  -1						):
																		SCREAMING_SNAKE_CASE							:    int							=							min(
																		    minimal_path_sums[i][j]		,  minimal_path_sums[i + 1][j] + matrix[i][j]						)

						return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums						)


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






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
					import tensorflow as tf

					from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM





@require_tf
@require_sentencepiece
@require_tokenizers
class 					lowercase__						(   unittest.TestCase):


						@slow
						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							TFAutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							AutoTokenizer.from_pretrained('''google/mt5-small'''						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							tokenizer('''Hello there'''    ,		return_tensors='''tf'''						).input_ids
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tokenizer('''Hi I am'''    ,		return_tensors='''tf'''						).input_ids

												SCREAMING_SNAKE_CASE							:    List[Any]							=							model(UpperCamelCase__    ,		labels=UpperCamelCase__						).loss
												SCREAMING_SNAKE_CASE							:    int							=							-tf.math.reduce_mean(UpperCamelCase__						).numpy()

												SCREAMING_SNAKE_CASE							:    List[str]							=							-21.22_8168
												self.assertTrue(abs(mtf_score - EXPECTED_SCORE						) < 2E-4						)





