"""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					):



				"""simple docstring"""

				def 		snake_case (				self    ):






										"""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 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			F"""{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}"""
										# distributed data settings
										snake_case								=			{'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 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										TrainingJobAnalytics(lowerCAmelCase    ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""    )

				@parameterized.expand([(2,)]    )
				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			self.create_estimator(lowerCAmelCase    )

										# 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'		,    99_99_99    )
										)

										# 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_torch_available


SCREAMING_SNAKE_CASE__      						=  {"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__      						=  [
					    "SEW_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "SEWForCTC",
					    "SEWForSequenceClassification",
					    "SEWModel",
					    "SEWPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_sew import (
										    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SEWForCTC,
										    SEWForSequenceClassification,
										    SEWModel,
										    SEWPreTrainedModel,
										)


else:
					import sys

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


