


import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
    from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments







@require_torch
class     _lowercase				(							unittest.TestCase):


       """simple docstring"""




       def 		lowerCAmelCase  (     self							:    int      ,   __lowerCamelCase							:    Any ):


             '''simple docstring'''



             for model_result in results.values():
                   for batch_size, sequence_length in zip(model_result["bs"]      ,   model_result["ss"] ):
                         lowerCamelCase__	:       List[Any]					=						model_result["result"][batch_size][sequence_length]
                         self.assertIsNotNone(__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						"sshleifer/tiny-gpt2"
             lowerCamelCase__	:       Optional[Any]					=						PyTorchBenchmarkArguments(
                 models=[MODEL_ID]      ,   training=__lowerCamelCase      ,   inference=__lowerCamelCase      ,   sequence_lengths=[8]      ,   batch_sizes=[1]      ,   multi_process=__lowerCamelCase      ,   )
             lowerCamelCase__	:       List[Any]					=						PyTorchBenchmark(__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						benchmark.run()
             self.check_results_dict_not_empty(results.time_inference_result )
             self.check_results_dict_not_empty(results.memory_inference_result )





       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						"sgugger/tiny-distilbert-classification"
             lowerCamelCase__	:       List[Any]					=						PyTorchBenchmarkArguments(
                 models=[MODEL_ID]      ,   training=__lowerCamelCase      ,   inference=__lowerCamelCase      ,   sequence_lengths=[8]      ,   batch_sizes=[1]      ,   multi_process=__lowerCamelCase      ,   only_pretrain_model=__lowerCamelCase      ,   )
             lowerCamelCase__	:       Optional[Any]					=						PyTorchBenchmark(__lowerCamelCase )
             lowerCamelCase__	:       Any					=						benchmark.run()
             self.check_results_dict_not_empty(results.time_inference_result )
             self.check_results_dict_not_empty(results.memory_inference_result )





       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						"sshleifer/tiny-gpt2"
             lowerCamelCase__	:       Union[str, Any]					=						PyTorchBenchmarkArguments(
                 models=[MODEL_ID]      ,   training=__lowerCamelCase      ,   inference=__lowerCamelCase      ,   torchscript=__lowerCamelCase      ,   sequence_lengths=[8]      ,   batch_sizes=[1]      ,   multi_process=__lowerCamelCase      ,   )
             lowerCamelCase__	:       str					=						PyTorchBenchmark(__lowerCamelCase )
             lowerCamelCase__	:       Union[str, Any]					=						benchmark.run()
             self.check_results_dict_not_empty(results.time_inference_result )
             self.check_results_dict_not_empty(results.memory_inference_result )





       @unittest.skipIf(torch_device == "cpu"      ,   "Cant do half precision" )
       def 		lowerCAmelCase  (     self							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						"sshleifer/tiny-gpt2"
             lowerCamelCase__	:       int					=						PyTorchBenchmarkArguments(
                 models=[MODEL_ID]      ,   training=__lowerCamelCase      ,   inference=__lowerCamelCase      ,   fpaa=__lowerCamelCase      ,   sequence_lengths=[8]      ,   batch_sizes=[1]      ,   multi_process=__lowerCamelCase      ,   )
             lowerCamelCase__	:       Optional[int]					=						PyTorchBenchmark(__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						benchmark.run()
             self.check_results_dict_not_empty(results.time_inference_result )
             self.check_results_dict_not_empty(results.memory_inference_result )





       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						"sshleifer/tiny-gpt2"
             lowerCamelCase__	:       Any					=						AutoConfig.from_pretrained(__lowerCamelCase )
             # set architectures equal to `None`
             lowerCamelCase__	:       Any					=						None
             lowerCamelCase__	:       Dict					=						PyTorchBenchmarkArguments(
                 models=[MODEL_ID]      ,   training=__lowerCamelCase      ,   inference=__lowerCamelCase      ,   sequence_lengths=[8]      ,   batch_sizes=[1]      ,   multi_process=__lowerCamelCase      ,   )
             lowerCamelCase__	:       Optional[int]					=						PyTorchBenchmark(__lowerCamelCase      ,   configs=[config] )
             lowerCamelCase__	:       Any					=						benchmark.run()
             self.check_results_dict_not_empty(results.time_inference_result )
             self.check_results_dict_not_empty(results.memory_inference_result )





       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						"sshleifer/tiny-gpt2"
             lowerCamelCase__	:       Union[str, Any]					=						PyTorchBenchmarkArguments(
                 models=[MODEL_ID]      ,   training=__lowerCamelCase      ,   inference=__lowerCamelCase      ,   sequence_lengths=[8]      ,   batch_sizes=[1]      ,   multi_process=__lowerCamelCase      ,   )
             lowerCamelCase__	:       Optional[Any]					=						PyTorchBenchmark(__lowerCamelCase )
             lowerCamelCase__	:       Optional[Any]					=						benchmark.run()
             self.check_results_dict_not_empty(results.time_train_result )
             self.check_results_dict_not_empty(results.memory_train_result )





       @unittest.skipIf(torch_device == "cpu"      ,   "Can't do half precision" )
       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						"sshleifer/tiny-gpt2"
             lowerCamelCase__	:       Optional[int]					=						PyTorchBenchmarkArguments(
                 models=[MODEL_ID]      ,   training=__lowerCamelCase      ,   inference=__lowerCamelCase      ,   sequence_lengths=[8]      ,   batch_sizes=[1]      ,   fpaa=__lowerCamelCase      ,   multi_process=__lowerCamelCase      ,   )
             lowerCamelCase__	:       Any					=						PyTorchBenchmark(__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						benchmark.run()
             self.check_results_dict_not_empty(results.time_train_result )
             self.check_results_dict_not_empty(results.memory_train_result )





       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						"sshleifer/tiny-gpt2"
             lowerCamelCase__	:       List[Any]					=						AutoConfig.from_pretrained(__lowerCamelCase )
             lowerCamelCase__	:       int					=						PyTorchBenchmarkArguments(
                 models=[MODEL_ID]      ,   training=__lowerCamelCase      ,   inference=__lowerCamelCase      ,   sequence_lengths=[8]      ,   batch_sizes=[1]      ,   multi_process=__lowerCamelCase      ,   )
             lowerCamelCase__	:       List[Any]					=						PyTorchBenchmark(__lowerCamelCase      ,   configs=[config] )
             lowerCamelCase__	:       str					=						benchmark.run()
             self.check_results_dict_not_empty(results.time_inference_result )
             self.check_results_dict_not_empty(results.memory_inference_result )





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						"sshleifer/tinier_bart"
             lowerCamelCase__	:       Dict					=						AutoConfig.from_pretrained(__lowerCamelCase )
             lowerCamelCase__	:       int					=						PyTorchBenchmarkArguments(
                 models=[MODEL_ID]      ,   training=__lowerCamelCase      ,   inference=__lowerCamelCase      ,   sequence_lengths=[8]      ,   batch_sizes=[1]      ,   multi_process=__lowerCamelCase      ,   )
             lowerCamelCase__	:       str					=						PyTorchBenchmark(__lowerCamelCase      ,   configs=[config] )
             lowerCamelCase__	:       List[str]					=						benchmark.run()
             self.check_results_dict_not_empty(results.time_inference_result )
             self.check_results_dict_not_empty(results.memory_inference_result )





       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						"sshleifer/tiny-gpt2"
             lowerCamelCase__	:       Optional[int]					=						AutoConfig.from_pretrained(__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						PyTorchBenchmarkArguments(
                 models=[MODEL_ID]      ,   training=__lowerCamelCase      ,   inference=__lowerCamelCase      ,   sequence_lengths=[8]      ,   batch_sizes=[1]      ,   multi_process=__lowerCamelCase      ,   )
             lowerCamelCase__	:       Any					=						PyTorchBenchmark(__lowerCamelCase      ,   configs=[config] )
             lowerCamelCase__	:       int					=						benchmark.run()
             self.check_results_dict_not_empty(results.time_train_result )
             self.check_results_dict_not_empty(results.memory_train_result )





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						"sshleifer/tinier_bart"
             lowerCamelCase__	:       Union[str, Any]					=						AutoConfig.from_pretrained(__lowerCamelCase )
             lowerCamelCase__	:       List[str]					=						PyTorchBenchmarkArguments(
                 models=[MODEL_ID]      ,   training=__lowerCamelCase      ,   inference=__lowerCamelCase      ,   sequence_lengths=[8]      ,   batch_sizes=[1]      ,   multi_process=__lowerCamelCase      ,   )
             lowerCamelCase__	:       Dict					=						PyTorchBenchmark(__lowerCamelCase      ,   configs=[config] )
             lowerCamelCase__	:       int					=						benchmark.run()
             self.check_results_dict_not_empty(results.time_train_result )
             self.check_results_dict_not_empty(results.memory_train_result )





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						"sshleifer/tiny-gpt2"
             with tempfile.TemporaryDirectory() as tmp_dir:
                   lowerCamelCase__	:       List[str]					=						PyTorchBenchmarkArguments(
                       models=[MODEL_ID]      ,   training=__lowerCamelCase      ,   inference=__lowerCamelCase      ,   save_to_csv=__lowerCamelCase      ,   sequence_lengths=[8]      ,   batch_sizes=[1]      ,   inference_time_csv_file=os.path.join(__lowerCamelCase      ,   "inf_time.csv" )      ,   train_memory_csv_file=os.path.join(__lowerCamelCase      ,   "train_mem.csv" )      ,   inference_memory_csv_file=os.path.join(__lowerCamelCase      ,   "inf_mem.csv" )      ,   train_time_csv_file=os.path.join(__lowerCamelCase      ,   "train_time.csv" )      ,   env_info_csv_file=os.path.join(__lowerCamelCase      ,   "env.csv" )      ,   multi_process=__lowerCamelCase      ,   )
                   lowerCamelCase__	:       Union[str, Any]					=						PyTorchBenchmark(__lowerCamelCase )
                   benchmark.run()
                   self.assertTrue(Path(os.path.join(__lowerCamelCase      ,   "inf_time.csv" ) ).exists() )
                   self.assertTrue(Path(os.path.join(__lowerCamelCase      ,   "train_time.csv" ) ).exists() )
                   self.assertTrue(Path(os.path.join(__lowerCamelCase      ,   "inf_mem.csv" ) ).exists() )
                   self.assertTrue(Path(os.path.join(__lowerCamelCase      ,   "train_mem.csv" ) ).exists() )
                   self.assertTrue(Path(os.path.join(__lowerCamelCase      ,   "env.csv" ) ).exists() )





       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						"sshleifer/tiny-gpt2"

             def _check_summary_is_not_empty(__lowerCamelCase							:    str ):
                   self.assertTrue(hasattr(__lowerCamelCase      ,   "sequential" ) )
                   self.assertTrue(hasattr(__lowerCamelCase      ,   "cumulative" ) )
                   self.assertTrue(hasattr(__lowerCamelCase      ,   "current" ) )
                   self.assertTrue(hasattr(__lowerCamelCase      ,   "total" ) )

             with tempfile.TemporaryDirectory() as tmp_dir:
                   lowerCamelCase__	:       Tuple					=						PyTorchBenchmarkArguments(
                       models=[MODEL_ID]      ,   training=__lowerCamelCase      ,   inference=__lowerCamelCase      ,   sequence_lengths=[8]      ,   batch_sizes=[1]      ,   log_filename=os.path.join(__lowerCamelCase      ,   "log.txt" )      ,   log_print=__lowerCamelCase      ,   trace_memory_line_by_line=__lowerCamelCase      ,   multi_process=__lowerCamelCase      ,   )
                   lowerCamelCase__	:       str					=						PyTorchBenchmark(__lowerCamelCase )
                   lowerCamelCase__	:       Union[str, Any]					=						benchmark.run()
                   _check_summary_is_not_empty(result.inference_summary )
                   _check_summary_is_not_empty(result.train_summary )
                   self.assertTrue(Path(os.path.join(__lowerCamelCase      ,   "log.txt" ) ).exists() )






import os

def    lowercase_	(	_A    :					str = "input.txt"		):

      """simple docstring"""




      with open(os.path.join(os.path.dirname(_A		)					,      _A		)		) as input_file:
            lowerCamelCase__	:       List[Any]					=						[
                [int(_A		) for element in line.split(","		)]
                for line in input_file.readlines()
            ]

      lowerCamelCase__	:       Optional[Any]					=						len(_A		)
      lowerCamelCase__	:       Union[str, Any]					=						len(matrix[0]		)

      lowerCamelCase__	:       Union[str, Any]					=						[[-1 for _ in range(_A		)] for _ in range(_A		)]
      for i in range(_A		):
            lowerCamelCase__	:       Optional[Any]					=						matrix[i][0]

      for j in range(1					,      _A		):
            for i in range(_A		):
                  lowerCamelCase__	:       int					=						minimal_path_sums[i][j - 1] + matrix[i][j]

            for i in range(1					,      _A		):
                  lowerCamelCase__	:       Tuple					=						min(
                      minimal_path_sums[i][j]					,      minimal_path_sums[i - 1][j] + matrix[i][j]		)

            for i in range(rows - 2					,      -1					,      -1		):
                  lowerCamelCase__	:       str					=						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() = }')


