

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_model_parallelism.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 16_00, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 16_00, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]	)
class 	UpperCAmelCase__		(	unittest.TestCase	):






		"""simple docstring"""


		def 				lowercase_		(							self						:		List[Any] )		->							Union[str, Any]:
							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 				lowercase_		(							self						:		Optional[int]     ,       __lowerCamelCase						:		Tuple )		->							List[str]:
							# configuration for running training on smdistributed Model Parallel
							SCREAMING_SNAKE_CASE__   =   {
							    '''enabled''': True,
							    '''processes_per_host''': 8,
							}
							SCREAMING_SNAKE_CASE__   =   {
							    '''enabled''': True,
							    '''parameters''': {
							        '''microbatches''': 4,
							        '''placement_strategy''': '''spread''',
							        '''pipeline''': '''interleaved''',
							        '''optimize''': '''speed''',
							        '''partitions''': 4,
							        '''ddp''': True,
							    },
							}

							SCREAMING_SNAKE_CASE__   =   {'''smdistributed''': {'''modelparallel''': smp_options}, '''mpi''': mpi_options}

							SCREAMING_SNAKE_CASE__   =   '''trainer''' if self.script == '''run_glue.py''' else '''smtrainer'''
							# 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}-{instance_count}-smp-{name_extension}'''     ,       instance_count=lowerCamelCase__     ,       instance_type=self.instance_type     ,       debugger_hook_config=lowerCamelCase__     ,       hyperparameters={
							        **self.env.hyperparameters,
							        '''model_name_or_path''': self.model_name_or_path,
							        '''max_steps''': 500,
							    }     ,       metric_definitions=self.env.metric_definitions     ,       distribution=lowerCamelCase__     ,       py_version='''py36'''     ,       )






		def 				lowercase_		(							self						:		Union[str, Any]     ,       __lowerCamelCase						:		str )		->							str:
							TrainingJobAnalytics(lowerCamelCase__ ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv''' )







		@parameterized.expand([(1,)] )
		def 				lowercase_		(							self						:		int     ,       __lowerCamelCase						:		List[Any] )		->							Union[str, Any]:
							# create estimator
							SCREAMING_SNAKE_CASE__   =   self.create_estimator(lowerCamelCase__ )

							# run training
							estimator.fit()

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

							# extract kpis
							SCREAMING_SNAKE_CASE__   =   list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value'''] )
							SCREAMING_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
							SCREAMING_SNAKE_CASE__   =   (
							    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}     ,       lowerCamelCase__ )





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
       from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline


