






'''simple docstring'''







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 _lowercase			(       unittest.TestCase	):




							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														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 	lowerCamelCase_       (							self:       Any    ,							UpperCamelCase__:       int=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=UpperCamelCase__    ,							instance_type=self.instance_type    ,							debugger_hook_config=UpperCamelCase__    ,							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:       Optional[Any]    ,							UpperCamelCase__:       int  ):
														TrainingJobAnalytics(UpperCamelCase__  ).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''  )




							def 	lowerCamelCase_       (							self:       int  ):
														# create estimator
														lowerCamelCase__      :			Union[str, Any] 						= self.create_estimator()

														# run training
														estimator.fit()

														# result dataframe
														lowerCamelCase__      :			Any 						= TrainingJobAnalytics(estimator.latest_training_job.name  ).dataframe()

														# extract kpis
														lowerCamelCase__      :			List[Any] 						= list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]  )
														lowerCamelCase__      :			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
														lowerCamelCase__      :			Any 						= (
														    Session().describe_training_job(estimator.latest_training_job.name  ).get("""TrainingTimeInSeconds"""    ,							999_999  )
														)

														# 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'''







_A			:    List[str]					  ='''0.21.0'''

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
			from .utils import rich

