


"""simple docstring"""

import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
     from sagemaker import Session, TrainingJobAnalytics
     from sagemaker.huggingface import HuggingFace





@pytest.mark.skipif(
    literal_eval(os.getenv("""TEST_SAGEMAKER"""			,   """False"""    )    ) is not True			,   reason="""Skipping test because should only be run when releasing minor transformers version"""			,   )
@pytest.mark.usefixtures("""sm_env"""    )
@parameterized_class(
    [
        {
            """framework""": """pytorch""",
            """script""": """run_glue_model_parallelism.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 16_00, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 16_00, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
    ]    )
class    lowerCamelCase__						(		unittest.TestCase    ):
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       if self.framework == "pytorch":
        subprocess.run(
            F'''cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'''.split()      ,       encoding="""utf-8"""      ,       check=_UpperCAmelCase      ,       )
       assert hasattr(self      ,       """env"""							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple      ,       A_	:	Tuple							):






       '''simple docstring'''


       __lowercase									=	{
           """enabled""": True,
           """processes_per_host""": 8,
       }
       __lowercase									=	{
           """enabled""": True,
           """parameters""": {
               """microbatches""": 4,
               """placement_strategy""": """spread""",
               """pipeline""": """interleaved""",
               """optimize""": """speed""",
               """partitions""": 4,
               """ddp""": True,
           },
       }

       __lowercase									=	{"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

       __lowercase									=	"""trainer""" if self.script == """run_glue.py""" else """smtrainer"""
       # creates estimator
       return HuggingFace(
           entry_point=self.script      ,       source_dir=self.env.test_path      ,       role=self.env.role      ,       image_uri=self.env.image_uri      ,       base_job_name=F'''{self.env.base_job_name}-{instance_count}-smp-{name_extension}'''      ,       instance_count=_UpperCAmelCase      ,       instance_type=self.instance_type      ,       debugger_hook_config=_UpperCAmelCase      ,       hyperparameters={
               **self.env.hyperparameters,
               """model_name_or_path""": self.model_name_or_path,
               """max_steps""": 5_0_0,
           }      ,       metric_definitions=self.env.metric_definitions      ,       distribution=_UpperCAmelCase      ,       py_version="""py36"""      ,       )
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]      ,       A_	:	Any							):






       '''simple docstring'''


       TrainingJobAnalytics(_UpperCAmelCase							).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''							)






      @parameterized.expand([(1,)]							)
      def SCREAMING_SNAKE_CASE_	(		self	:	str      ,       A_	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	self.create_estimator(_UpperCAmelCase							)

       # run training
       estimator.fit()

       # result dataframe
       __lowercase									=	TrainingJobAnalytics(estimator.latest_training_job.name							).dataframe()

       # extract kpis
       __lowercase									=	list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]							)
       __lowercase									=	list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]							)
       # get train time from SageMaker job, this includes starting, preprocessing, stopping
       __lowercase									=	(
           Session().describe_training_job(estimator.latest_training_job.name							).get("""TrainingTimeInSeconds"""      ,       9_9_9_9_9_9							)
       )

       # assert kpis
       assert train_runtime <= self.results["train_runtime"]
       assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy							)
       assert all(t <= self.results["""eval_loss"""] for t in eval_loss							)

       # dump tests result into json file to share in PR
       with open(F'''{estimator.latest_training_job.name}.json'''      ,       """w"""							) as outfile:
        json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss}      ,       _UpperCAmelCase							)




"""simple docstring"""

def lowerCAmelCase_      (				UpperCamelCase__	: str				,   UpperCamelCase__	: str ):






 """simple docstring"""







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

  return "".join(UpperCamelCase__ )

 # matching characters
 __lowercase									=	get_matched_characters(UpperCamelCase__				,   UpperCamelCase__ )
 __lowercase									=	get_matched_characters(UpperCamelCase__				,   UpperCamelCase__ )
 __lowercase									=	len(UpperCamelCase__ )

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

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

 # common prefix up to 4 characters
 __lowercase									=	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"))
