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_50, '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_00, 'eval_accuracy': 0.3, 'eval_loss': 0.9},
        },
    ]					)
class 					A							(    unittest.TestCase					):


   def       lowercase_				(self					:						str		)						->    Union[str, 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       lowercase_				(self					:						int	,  __UpperCAmelCase					:						List[str]=1		)						->    Optional[Any]:
         """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=__UpperCAmelCase	,  instance_type=self.instance_type	,  debugger_hook_config=__UpperCAmelCase	,  hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}	,  metric_definitions=self.env.metric_definitions	,  py_version="py36"	,  )


   def       lowercase_				(self					:						Dict	,  __UpperCAmelCase					:						List[str]		)						->    Dict:
         """simple docstring"""



         TrainingJobAnalytics(__UpperCAmelCase		).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""		)





   def       lowercase_				(self					:						Union[str, Any]		)						->    Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  self.create_estimator()

         # run training
         estimator.fit()

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

         # extract kpis
         UpperCAmelCase__								=  list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]		)
         UpperCAmelCase__								=  list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]		)
         # get train time from SageMaker job, this includes starting, preprocessing, stopping
         UpperCAmelCase__								=  (
             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		)




from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCamelCase__    							= {'tokenization_bertweet': ['BertweetTokenizer']}


if TYPE_CHECKING:
      from .tokenization_bertweet import BertweetTokenizer

else:
      import sys

      UpperCamelCase__    							= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



