






'''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 				lowercase       (							unittest.TestCase      ):


    '''simple docstring'''

    def    a__				(					self     :     List[Any]     )							->							Optional[int]:

     '''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=__lowerCamelCase				,     )
     assert hasattr(self				,     "env"     )

    def    a__				(					self     :     Union[str, Any]				,     __lowerCamelCase     :     List[Any]     )							->							Optional[Any]:

     '''simple docstring'''




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

    def    a__				(					self     :     Optional[int]				,     __lowerCamelCase     :     Union[str, Any]     )							->							List[Any]:

     '''simple docstring'''




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




    @parameterized.expand([(2,)]     )
    def    a__				(					self     :     str				,     __lowerCamelCase     :     int     )							->							List[str]:

     '''simple docstring'''




     lowerCamelCase__  =     self.create_estimator(__lowerCamelCase     )

     # 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}				,     __lowerCamelCase     )







'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A							:							Tuple  							=		{
    """configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaConfig""", """DebertaOnnxConfig"""],
    """tokenization_deberta""": ["""DebertaTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							int  							=		["""DebertaTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							str  							=		[
     """DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """DebertaForMaskedLM""",
     """DebertaForQuestionAnswering""",
     """DebertaForSequenceClassification""",
     """DebertaForTokenClassification""",
     """DebertaModel""",
     """DebertaPreTrainedModel""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							Union[str, Any]  							=		[
     """TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFDebertaForMaskedLM""",
     """TFDebertaForQuestionAnswering""",
     """TFDebertaForSequenceClassification""",
     """TFDebertaForTokenClassification""",
     """TFDebertaModel""",
     """TFDebertaPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
 from .tokenization_deberta import DebertaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_deberta_fast import DebertaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deberta import (
      DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      DebertaForMaskedLM,
      DebertaForQuestionAnswering,
      DebertaForSequenceClassification,
      DebertaForTokenClassification,
      DebertaModel,
      DebertaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deberta import (
      TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDebertaForMaskedLM,
      TFDebertaForQuestionAnswering,
      TFDebertaForSequenceClassification,
      TFDebertaForTokenClassification,
      TFDebertaModel,
      TFDebertaPreTrainedModel,
  )


else:
 import sys

 __A							:							List[Any]  							=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

