'''simple docstring'''




import json
import logging
import os
import sys
from time import time
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, require_torch_tpu


logging.basicConfig(level=logging.DEBUG)

__SCREAMING_SNAKE_CASE       :     Dict     =  logging.getLogger()

def 	UpperCamelCase_							(					_UpperCAmelCase  :      str    )    ->    Dict:


       """simple docstring"""







       _UpperCAmelCase     :  List[Any]							      =    {}
       _UpperCAmelCase     :  Union[str, Any]							      =    os.path.join(_UpperCAmelCase			,	"all_results.json"    )
       if os.path.exists(_UpperCAmelCase    ):
              with open(_UpperCAmelCase			,	"r"    ) as f:
                     _UpperCAmelCase     :  Optional[Any]							      =    json.load(_UpperCAmelCase    )
       else:
              raise ValueError(F"""can't find {path}"""    )
       return results


__SCREAMING_SNAKE_CASE       :     Optional[Any]     =  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


@require_torch_tpu
class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def      _A	(       self						:				List[str]       ):
             import xla_spawn

             _UpperCAmelCase     :  List[Any]							      =    self.get_auto_remove_tmp_dir()
             _UpperCAmelCase     :  Union[str, Any]							      =    F"""
            ./examples/pytorch/text-classification/run_glue.py
            --num_cores=8
            ./examples/pytorch/text-classification/run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --debug tpu_metrics_debug
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

             with patch.object(A						,					"argv"						,					A       ):
                    _UpperCAmelCase     :  Dict							      =    time()
                    xla_spawn.main()
                    _UpperCAmelCase     :  List[str]							      =    time()

                    _UpperCAmelCase     :  List[Any]							      =    get_results(A       )
                    self.assertGreaterEqual(result["eval_accuracy"]						,					0.75       )

                    # Assert that the script takes less than 500 seconds to make sure it doesn't hang.
                    self.assertLess(end - start						,					500       )





      def      _A	(       self						:				int       ):
             import xla_spawn

             _UpperCAmelCase     :  int							      =    "\n            ./tests/test_trainer_tpu.py\n            --num_cores=8\n            ./tests/test_trainer_tpu.py\n            ".split()
             with patch.object(A						,					"argv"						,					A       ):
                    xla_spawn.main()


'''simple docstring'''




def 	UpperCamelCase_							(					_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      str    )    ->    float:


       """simple docstring"""







       def get_matched_characters(_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      str    ) -> str:
              _UpperCAmelCase     :  Tuple							      =    []
              _UpperCAmelCase     :  Dict							      =    min(len(_stra    )			,	len(_stra    )    ) // 2
              for i, l in enumerate(_stra    ):
                     _UpperCAmelCase     :  int							      =    int(max(0			,	i - limit    )    )
                     _UpperCAmelCase     :  Any							      =    int(min(i + limit + 1			,	len(_stra    )    )    )
                     if l in _stra[left:right]:
                            matched.append(_UpperCAmelCase    )
                            _UpperCAmelCase     :  List[Any]							      =    F"""{_stra[0:_stra.index(_UpperCAmelCase    )]} {_stra[_stra.index(_UpperCAmelCase    ) + 1:]}"""

              return "".join(_UpperCAmelCase    )

       # matching characters
       _UpperCAmelCase     :  Union[str, Any]							      =    get_matched_characters(_UpperCAmelCase			,	_UpperCAmelCase    )
       _UpperCAmelCase     :  Tuple							      =    get_matched_characters(_UpperCAmelCase			,	_UpperCAmelCase    )
       _UpperCAmelCase     :  Tuple							      =    len(_UpperCAmelCase    )

       # transposition
       _UpperCAmelCase     :  Optional[Any]							      =    (
           len([(ca, ca) for ca, ca in zip(_UpperCAmelCase			,	_UpperCAmelCase    ) if ca != ca]    ) // 2
       )

       if not match_count:
              _UpperCAmelCase     :  Dict							      =    0.0
       else:
              _UpperCAmelCase     :  Optional[int]							      =    (
                  1
                  / 3
                  * (
                      match_count / len(_UpperCAmelCase    )
                      + match_count / len(_UpperCAmelCase    )
                      + (match_count - transpositions) / match_count
                  )
              )

       # common prefix up to 4 characters
       _UpperCAmelCase     :  str							      =    0
       for ca, ca in zip(stra[:4]			,	stra[:4]    ):
              if ca == ca:
                     prefix_len += 1
              else:
                     break

       return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(jaro_winkler("""hello""", """world"""))

