

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": 1_6_0_0, "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": 1_6_0_0, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]		)
class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def      _snake_case		(       self					:			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=__lowerCamelCase     , )
       assert hasattr(self     , "env"			)





   def      _snake_case		(       self					:			Optional[int]     , __lowerCamelCase					:			int			):
       # configuration for running training on smdistributed Model Parallel
       SCREAMING_SNAKE_CASE										=   {
           "enabled": True,
           "processes_per_host": 8,
       }
       SCREAMING_SNAKE_CASE										=   {
           "enabled": True,
           "parameters": {
               "microbatches": 4,
               "placement_strategy": "spread",
               "pipeline": "interleaved",
               "optimize": "speed",
               "partitions": 4,
               "ddp": True,
           },
       }

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

       SCREAMING_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=__lowerCamelCase     , instance_type=self.instance_type     , debugger_hook_config=__lowerCamelCase     , hyperparameters={
               **self.env.hyperparameters,
               "model_name_or_path": self.model_name_or_path,
               "max_steps": 500,
           }     , metric_definitions=self.env.metric_definitions     , distribution=__lowerCamelCase     , py_version="py36"     , )





   def      _snake_case		(       self					:			Optional[int]     , __lowerCamelCase					:			List[str]			):
       TrainingJobAnalytics(__lowerCamelCase			).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv"			)





   @parameterized.expand([(1,)]			)
   def      _snake_case		(       self					:			Dict     , __lowerCamelCase					:			Optional[int]			):
       # create estimator
       SCREAMING_SNAKE_CASE										=   self.create_estimator(__lowerCamelCase			)

       # run training
       estimator.fit()

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

       # extract kpis
       SCREAMING_SNAKE_CASE										=   list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]			)
       SCREAMING_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
       SCREAMING_SNAKE_CASE										=   (
           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}     , __lowerCamelCase			)



'''simple docstring'''





from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments



