
"""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': 6_50, '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': 6_00, 'eval_accuracy': 0.3, 'eval_loss': 0.9},
        },
    ]     )
class    _UpperCAmelCase(   unittest.TestCase     ):



		def 						UpperCAmelCase							( self)	->	List[str]:






							'''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=__a    ,		)
							assert hasattr(self    ,		'''env''')



		def 						UpperCAmelCase							( self    ,		__a=1)	->	Dict:






							'''simple docstring'''
							# 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=__a    ,		instance_type=self.instance_type    ,		debugger_hook_config=__a    ,		hyperparameters={**self.env.hyperparameters, '''model_name_or_path''': self.model_name_or_path}    ,		metric_definitions=self.env.metric_definitions    ,		py_version='''py36'''    ,		)



		def 						UpperCAmelCase							( self    ,		__a)	->	Union[str, Any]:






							'''simple docstring'''
							TrainingJobAnalytics(__a).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv''')




		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							# create estimator
							_UpperCamelCase										=  self.create_estimator()

							# run training
							estimator.fit()

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

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








"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


_a														=		{
    """configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
    """processing_trocr""": ["""TrOCRProcessor"""],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a														=		[
				    """TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TrOCRForCausalLM""",
				    """TrOCRPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
				from .processing_trocr import TrOCRProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
				import sys

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






