
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 __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""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=_UpperCamelCase    ,				)
				assert hasattr(self    ,				'env'	)



		def __UpperCamelCase			(			self    ,				A_=1	)  -> List[Any]:
				"""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=_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 __UpperCamelCase			(			self    ,				A_	)  -> Union[str, Any]:
				"""simple docstring"""
				TrainingJobAnalytics(_UpperCamelCase	).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''	)



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""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'    ,				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	)


import logging
import os

from .state import PartialState


class     lowercase (		logging.LoggerAdapter       ):



		@staticmethod
		def __UpperCamelCase			(			A_	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					PartialState()
				return not main_process_only or (main_process_only and state.is_main_process)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				*A_    ,				**A_	)  -> Union[str, Any]:
				"""simple docstring"""
				if PartialState._shared_state == {}:
						raise RuntimeError(
						    'You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'	)
				UpperCamelCase          =					kwargs.pop('main_process_only'    ,				A_	)
				UpperCamelCase          =					kwargs.pop('in_order'    ,				A_	)

				if self.isEnabledFor(A_	):
						if self._should_log(A_	):
								UpperCamelCase     ,  UpperCamelCase          =					self.process(A_    ,				A_	)
								self.logger.log(A_    ,				A_    ,				*A_    ,				**A_	)

						elif in_order:
								UpperCamelCase          =					PartialState()
								for i in range(state.num_processes	):
										if i == state.process_index:
												UpperCamelCase     ,  UpperCamelCase          =					self.process(A_    ,				A_	)
												self.logger.log(A_    ,				A_    ,				*A_    ,				**A_	)
										state.wait_for_everyone()






def 				A	( lowercase   ,	lowercase = None			)       ->  Dict:
		'''simple docstring'''

		if log_level is None:
				UpperCamelCase          =					os.environ.get('ACCELERATE_LOG_LEVEL'   ,	lowercase			)
		UpperCamelCase          =					logging.getLogger(lowercase			)
		if log_level is not None:
				logger.setLevel(log_level.upper()			)
				logger.root.setLevel(log_level.upper()			)
		return MultiProcessAdapter(lowercase   ,	{}			)
