
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 	UpperCAmelCase(self   :					List[str]     )		->		Union[str, Any]:
         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 	UpperCAmelCase(self   :					Tuple       ,					_A   :					Optional[int]     )		->		Any:
         # configuration for running training on smdistributed Model Parallel
         snake_case				  =      {
             "enabled": True,
             "processes_per_host": 8,
         }
         snake_case				  =      {
             "enabled": True,
             "parameters": {
                 "microbatches": 4,
                 "placement_strategy": "spread",
                 "pipeline": "interleaved",
                 "optimize": "speed",
                 "partitions": 4,
                 "ddp": True,
             },
         }

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

         snake_case				  =      "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=_A       ,					instance_type=self.instance_type       ,					debugger_hook_config=_A       ,					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=_A       ,					py_version="py36"       ,					)





     def 	UpperCAmelCase(self   :					Tuple       ,					_A   :					Optional[int]     )		->		Any:
         TrainingJobAnalytics(_A     ).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv'     )





     @parameterized.expand([(1,)]     )
     def 	UpperCAmelCase(self   :					Dict       ,					_A   :					Optional[Any]     )		->		Dict:
         # create estimator
         snake_case				  =      self.create_estimator(_A     )

         # run training
         estimator.fit()

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

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



def 		lowercase_    (     A__				,	A__	)							->							List[Any]:
    """simple docstring"""

    snake_case				  =      [0 for i in range(r + 1	)]
    # nc0 = 1
    snake_case				  =      1
    for i in range(1				,	n + 1	):
        # to compute current row from previous row.
        snake_case				  =      min(A__				,	A__	)
        while j > 0:
            c[j] += c[j - 1]
            j -= 1
    return c[r]





print(binomial_coefficient(n=10, r=5))

