
def a__    (       A_,     A_					):






  '''simple docstring'''





  def get_matched_characters(A_,     A_					) -> str:
    __magic_name__				     =       []
    __magic_name__				     =       min(len(_stra					),     len(_stra					)					) // 2
    for i, l in enumerate(_stra					):
      __magic_name__				     =       int(max(0,     i - limit					)					)
      __magic_name__				     =       int(min(i + limit + 1,     len(_stra					)					)					)
      if l in _stra[left:right]:
        matched.append(A_					)
        __magic_name__				     =       f'''{_stra[0:_stra.index(A_					)]} {_stra[_stra.index(A_					) + 1:]}'''

    return "".join(A_					)

  # matching characters
  __magic_name__				     =       get_matched_characters(A_,     A_					)
  __magic_name__				     =       get_matched_characters(A_,     A_					)
  __magic_name__				     =       len(A_					)

  # transposition
  __magic_name__				     =       (
      len([(ca, ca) for ca, ca in zip(A_,     A_					) if ca != ca]					) // 2
  )

  if not match_count:
    __magic_name__				     =       0.0
  else:
    __magic_name__				     =       (
        1
        / 3
        * (
            match_count / len(A_					)
            + match_count / len(A_					)
            + (match_count - transpositions) / match_count
        )
    )

  # common prefix up to 4 characters
  __magic_name__				     =       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'))



import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
    import tensorflow as tf

    from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments




@require_tf
class        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  def 							_lowercase							( self			: List[str]			,							UpperCamelCase__			: int    )			->	str:







    """simple docstring"""






    for model_result in results.values():
      for batch_size, sequence_length in zip(model_result["""bs"""]			,							model_result["""ss"""]    ):
        __magic_name__				     =       model_result["""result"""][batch_size][sequence_length]
        self.assertIsNotNone(UpperCamelCase__    )
  def 							_lowercase							( self			: Union[str, Any]    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							eager_mode=UpperCamelCase__			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result    )
    self.check_results_dict_not_empty(results.memory_inference_result    )
  def 							_lowercase							( self			: Optional[Any]    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       """sgugger/tiny-distilbert-classification"""
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							only_pretrain_model=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result    )
    self.check_results_dict_not_empty(results.memory_inference_result    )
  def 							_lowercase							( self			: Any    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result    )
    self.check_results_dict_not_empty(results.memory_inference_result    )
  def 							_lowercase							( self			: Tuple    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       AutoConfig.from_pretrained(UpperCamelCase__    )
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							eager_mode=UpperCamelCase__			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__			,							[config]    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result    )
    self.check_results_dict_not_empty(results.memory_inference_result    )
  def 							_lowercase							( self			: Any    )			->	Union[str, Any]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       AutoConfig.from_pretrained(UpperCamelCase__    )
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__			,							[config]    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result    )
    self.check_results_dict_not_empty(results.memory_inference_result    )
  def 							_lowercase							( self			: List[Any]    )			->	Dict:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_train_result    )
    self.check_results_dict_not_empty(results.memory_train_result    )
  def 							_lowercase							( self			: Optional[Any]    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       AutoConfig.from_pretrained(UpperCamelCase__    )
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__			,							[config]    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_train_result    )
    self.check_results_dict_not_empty(results.memory_train_result    )
  def 							_lowercase							( self			: Union[str, Any]    )			->	Any:







    """simple docstring"""






    __magic_name__				     =       """patrickvonplaten/t5-tiny-random"""
    __magic_name__				     =       AutoConfig.from_pretrained(UpperCamelCase__    )
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__			,							configs=[config]    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result    )
    self.check_results_dict_not_empty(results.memory_inference_result    )
  @unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("""GPU"""    )    ) == 0			,							"""Cannot do xla on CPU."""    )
  def 							_lowercase							( self			: Tuple    )			->	int:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    __magic_name__				     =       TensorFlowBenchmarkArguments(
        models=[MODEL_ID]			,							training=UpperCamelCase__			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							use_xla=UpperCamelCase__			,							multi_process=UpperCamelCase__			,							)
    __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
    __magic_name__				     =       benchmark.run()
    self.check_results_dict_not_empty(results.time_inference_result    )
    self.check_results_dict_not_empty(results.memory_inference_result    )
  def 							_lowercase							( self			: Union[str, Any]    )			->	Dict:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""
    with tempfile.TemporaryDirectory() as tmp_dir:
      __magic_name__				     =       TensorFlowBenchmarkArguments(
          models=[MODEL_ID]			,							inference=UpperCamelCase__			,							save_to_csv=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							inference_time_csv_file=os.path.join(UpperCamelCase__			,							"""inf_time.csv"""    )			,							inference_memory_csv_file=os.path.join(UpperCamelCase__			,							"""inf_mem.csv"""    )			,							env_info_csv_file=os.path.join(UpperCamelCase__			,							"""env.csv"""    )			,							multi_process=UpperCamelCase__			,							)
      __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
      benchmark.run()
      self.assertTrue(Path(os.path.join(UpperCamelCase__			,							"""inf_time.csv"""    )    ).exists()    )
      self.assertTrue(Path(os.path.join(UpperCamelCase__			,							"""inf_mem.csv"""    )    ).exists()    )
      self.assertTrue(Path(os.path.join(UpperCamelCase__			,							"""env.csv"""    )    ).exists()    )





  def 							_lowercase							( self			: int    )			->	Optional[Any]:







    """simple docstring"""






    __magic_name__				     =       """sshleifer/tiny-gpt2"""

    def _check_summary_is_not_empty(UpperCamelCase__			: Dict    ):
      self.assertTrue(hasattr(UpperCamelCase__			,							"""sequential"""    )    )
      self.assertTrue(hasattr(UpperCamelCase__			,							"""cumulative"""    )    )
      self.assertTrue(hasattr(UpperCamelCase__			,							"""current"""    )    )
      self.assertTrue(hasattr(UpperCamelCase__			,							"""total"""    )    )

    with tempfile.TemporaryDirectory() as tmp_dir:
      __magic_name__				     =       TensorFlowBenchmarkArguments(
          models=[MODEL_ID]			,							inference=UpperCamelCase__			,							sequence_lengths=[8]			,							batch_sizes=[1]			,							log_filename=os.path.join(UpperCamelCase__			,							"""log.txt"""    )			,							log_print=UpperCamelCase__			,							trace_memory_line_by_line=UpperCamelCase__			,							eager_mode=UpperCamelCase__			,							multi_process=UpperCamelCase__			,							)
      __magic_name__				     =       TensorFlowBenchmark(UpperCamelCase__    )
      __magic_name__				     =       benchmark.run()
      _check_summary_is_not_empty(result.inference_summary    )
      self.assertTrue(Path(os.path.join(UpperCamelCase__			,							"""log.txt"""    )    ).exists()    )

