



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": 650, "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": 600, "eval_accuracy": 0.3, "eval_loss": 0.9},
        },
    ]   )
class SCREAMING_SNAKE_CASE__					(		unittest.TestCase   ):




    '''simple docstring'''






    def       _lowerCAmelCase (	self		):
        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=SCREAMING_SNAKE_CASE_,		)
        assert hasattr(self,		"""env"""		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__=1		):
        # 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}-single''',		instance_count=SCREAMING_SNAKE_CASE_,		instance_type=self.instance_type,		debugger_hook_config=SCREAMING_SNAKE_CASE_,		hyperparameters={**self.env.hyperparameters, """model_name_or_path""": self.model_name_or_path},		metric_definitions=self.env.metric_definitions,		py_version="""py36""",		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        TrainingJobAnalytics(SCREAMING_SNAKE_CASE_		).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''		)






    def       _lowerCAmelCase (	self		):
        # create estimator
        A			:	List[Any]										=       self.create_estimator()

        # run training
        estimator.fit()

        # result dataframe
        A			:	List[Any]										=       TrainingJobAnalytics(estimator.latest_training_job.name		).dataframe()

        # extract kpis
        A			:	List[str]										=       list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]		)
        A			:	Optional[int]										=       list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]		)
        # get train time from SageMaker job, this includes starting, preprocessing, stopping
        A			:	Any										=       (
            Session().describe_training_job(estimator.latest_training_job.name		).get("""TrainingTimeInSeconds""",		99_9999		)
        )

        # 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},		SCREAMING_SNAKE_CASE_		)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_:Optional[int]					=						{
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Optional[int]					=						[
         """TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TapasForMaskedLM""",
         """TapasForQuestionAnswering""",
         """TapasForSequenceClassification""",
         """TapasModel""",
         """TapasPreTrainedModel""",
         """load_tf_weights_in_tapas""",
     ]
try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Optional[Any]					=						[
         """TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFTapasForMaskedLM""",
         """TFTapasForQuestionAnswering""",
         """TFTapasForSequenceClassification""",
         """TFTapasModel""",
         """TFTapasPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
     from .tokenization_tapas import TapasTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tapas import (
              TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
              TapasForMaskedLM,
              TapasForQuestionAnswering,
              TapasForSequenceClassification,
              TapasModel,
              TapasPreTrainedModel,
              load_tf_weights_in_tapas,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_tapas import (
              TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFTapasForMaskedLM,
              TFTapasForQuestionAnswering,
              TFTapasForSequenceClassification,
              TFTapasModel,
              TFTapasPreTrainedModel,
          )


else:
     import sys

     SCREAMING_SNAKE_CASE_:str					=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


