





'''simple docstring'''


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.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 650, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "pytorch",
            "script": "run_ddp.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 600, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "tensorflow",
            "script": "run_tf_dist.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 600, "eval_accuracy": 0.6, "eval_loss": 0.7},
        },
    ]		)
class    lowerCAmelCase_		(		unittest.TestCase		):




 def 							_snake_case   (	self		)    ->							Optional[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=UpperCamelCase__			,     )
     assert hasattr(self			,     "env"		)




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Any:
     _lowerCAmelCase				=      f'''{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}'''
     # distributed data settings
     _lowerCAmelCase				=      {"smdistributed": {"dataparallel": {"enabled": True}}} if self.script != "run_ddp.py" else None

     # 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=UpperCamelCase__			,     instance_count=UpperCamelCase__			,     instance_type=self.instance_type			,     debugger_hook_config=UpperCamelCase__			,     hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}			,     metric_definitions=self.env.metric_definitions			,     distribution=UpperCamelCase__			,     py_version="py36"			,     )




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Optional[int]:
     TrainingJobAnalytics(UpperCamelCase__		).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''		)





 @parameterized.expand([(2,)]		)
 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							int:
     _lowerCAmelCase				=      self.create_estimator(UpperCamelCase__		)

     # run training
     estimator.fit()

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

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






'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE	 =							{
    "configuration_bigbird_pegasus": [
        "BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BigBirdPegasusConfig",
        "BigBirdPegasusOnnxConfig",
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							[
           "BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BigBirdPegasusForCausalLM",
           "BigBirdPegasusForConditionalGeneration",
           "BigBirdPegasusForQuestionAnswering",
           "BigBirdPegasusForSequenceClassification",
           "BigBirdPegasusModel",
           "BigBirdPegasusPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_bigbird_pegasus import (
           BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
           BigBirdPegasusConfig,
           BigBirdPegasusOnnxConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_bigbird_pegasus import (
                  BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BigBirdPegasusForCausalLM,
                  BigBirdPegasusForConditionalGeneration,
                  BigBirdPegasusForQuestionAnswering,
                  BigBirdPegasusForSequenceClassification,
                  BigBirdPegasusModel,
                  BigBirdPegasusPreTrainedModel,
              )


else:
       import sys

       _SCREAMING_SNAKE_CASE	 =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


