






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": 6_5_0, "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": 6_0_0, "eval_accuracy": 0.3, "eval_loss": 0.9},
        },
    ]			)
class 							A_    (						unittest.TestCase			):






 '''simple docstring'''




 def  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=_A							,  )
     assert hasattr(self							,  '''env''')


 def  snake_case__				(	self							,  _A=1) ->		Dict:

     """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=_A							,  instance_type=self.instance_type							,  debugger_hook_config=_A							,  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							,  _A) ->		List[Any]:

     """simple docstring"""



     TrainingJobAnalytics(_A).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv''')


 def  snake_case__				(	self) ->		List[str]:

     """simple docstring"""



     _UpperCAmelCase				:      List[Any]			  =				self.create_estimator()

     # run training
     estimator.fit()

     # result dataframe
     _UpperCAmelCase				:      Union[str, Any]			  =				TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

     # extract kpis
     _UpperCAmelCase				:      Optional[int]			  =				list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value'''])
     _UpperCAmelCase				:      Optional[Any]			  =				list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value'''])
     # get train time from SageMaker job, this includes starting, preprocessing, stopping
     _UpperCAmelCase				:      Any			  =				(
         Session().describe_training_job(estimator.latest_training_job.name).get('''TrainingTimeInSeconds'''							,  999999)
     )

     # 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}							,  _A)












import os
from collections.abc import Iterator

def      _lowerCamelCase	(						__A    :							str = "." )      ->					Iterator[str]:
    for dir_path, dir_names, filenames in os.walk(__A ):
        _UpperCAmelCase				:      List[Any]			  =				[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      _lowerCamelCase	(						__A    :							Dict )      ->					List[Any]:
    return f'''{i * '  '}*''' if i else "\n##"

def      _lowerCamelCase	(						__A    :							str		,		__A    :							str )      ->					str:
    _UpperCAmelCase				:      int			  =				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      _lowerCamelCase	(						__A    :							str = "." )      ->					None:
    _UpperCAmelCase				:      List[str]			  =				''''''
    for filepath in sorted(good_file_paths(__A ) ):
        _UpperCAmelCase  , _UpperCAmelCase				:      Optional[Any]			  =				os.path.split(__A )
        if filepath != old_path:
            _UpperCAmelCase				:      Optional[int]			  =				print_path(__A		,		__A )
        _UpperCAmelCase				:      Dict			  =				(filepath.count(os.sep ) + 1) if filepath else 0
        _UpperCAmelCase				:      Union[str, Any]			  =				f'''{filepath}/{filename}'''.replace(''' '''		,		'''%20''' )
        _UpperCAmelCase				:      List[Any]			  =				os.path.splitext(filename.replace('''_'''		,		''' ''' ).title() )[0]
        print(f'''{md_prefix(__A )} [{filename}]({url})''' )


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




