





"""simple docstring"""





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

import pytest
from parameterized import 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.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 650, """eval_accuracy""": 0.6, """eval_loss""": 0.9},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.3, """eval_loss""": 0.9},
        },
    ]       )
class 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       def      snake_case						(    self      ):



         '''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=lowercase_    , )
         assert hasattr(self    , 'env'      )






       def      snake_case						(    self    , __UpperCAmelCase=1      ):



         '''simple docstring'''

         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}-single"    , instance_count=lowercase_    , instance_type=self.instance_type    , debugger_hook_config=lowercase_    , hyperparameters={**self.env.hyperparameters, 'model_name_or_path': self.model_name_or_path}    , metric_definitions=self.env.metric_definitions    , py_version='py36'    , )






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         TrainingJobAnalytics(lowercase_      ).export_csv(F"{self.env.test_path}/{job_name}_metrics.csv"      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Any											=							self.create_estimator()

         # run training
         estimator.fit()

         # result dataframe
         lowerCAmelCase__			:Any											=							TrainingJobAnalytics(estimator.latest_training_job.name      ).dataframe()

         # extract kpis
         lowerCAmelCase__			:Any											=							list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']      )
         lowerCAmelCase__			:str											=							list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']      )
         # get train time from SageMaker job, this includes starting, preprocessing, stopping
         lowerCAmelCase__			:int											=							(
             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}    , lowercase_      )



"""simple docstring"""





import os
from collections.abc import Iterator

def   __SCREAMING_SNAKE_CASE  (      A_ = "." ):
	for dir_path, dir_names, filenames in os.walk(A_ ):
		lowerCAmelCase__	:						str  				=	[d for d in dir_names if d != '''scripts''' and d[0] not in '''._''']
		for filename in filenames:
			if filename == "__init__.py":
				continue
			if os.path.splitext(A_ )[1] in (".py", ".ipynb"):
				yield os.path.join(A_						,     A_ ).lstrip('''./''' )

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	return f'{i * "  "}*' if i else "\n##"

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	lowerCAmelCase__	:						Optional[Any]  				=	old_path.split(os.sep )
	for i, new_part in enumerate(new_path.split(os.sep ) ):
		if (i + 1 > len(A_ ) or old_parts[i] != new_part) and new_part:
			print(f'{md_prefix(A_ )} {new_part.replace("_"						,     " " ).title()}' )
	return new_path

def   __SCREAMING_SNAKE_CASE  (      A_ = "." ):
	lowerCAmelCase__	:						Any  				=	''''''
	for filepath in sorted(good_file_paths(A_ ) ):
		lowerCAmelCase__				,lowerCAmelCase__	:						str  				=	os.path.split(A_ )
		if filepath != old_path:
			lowerCAmelCase__	:						str  				=	print_path(A_						,     A_ )
		lowerCAmelCase__	:						str  				=	(filepath.count(os.sep ) + 1) if filepath else 0
		lowerCAmelCase__	:						Union[str, Any]  				=	f'{filepath}/{filename}'.replace(''' '''						,     '''%20''' )
		lowerCAmelCase__	:						List[str]  				=	os.path.splitext(filename.replace('''_'''						,     ''' ''' ).title() )[0]
		print(f'{md_prefix(A_ )} [{filename}]({url})' )


if __name__ == "__main__":
							print_directory_md('''.''')




