

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': 6_50, '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': 6_00, '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': 6_00, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
        },
    ] )
class  A						(							unittest.TestCase ):







    """simple docstring"""





    def 						snake_case__     (      self :       Optional[int]		)->    Union[str, 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=lowercase_,)
        assert hasattr(self,'env'		)


    def 						snake_case__     (      self :       Tuple,lowercase_ :       Optional[int]		)->    Dict:





        '''simple docstring'''







        A__          =   F'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
        # distributed data settings
        A__          =   {'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=lowercase_,instance_count=lowercase_,instance_type=self.instance_type,debugger_hook_config=lowercase_,hyperparameters={**self.env.distributed_hyperparameters, 'model_name_or_path': self.model_name_or_path},metric_definitions=self.env.metric_definitions,distribution=lowercase_,py_version='py36',)


    def 						snake_case__     (      self :       Optional[Any],lowercase_ :       Tuple		)->    Any:





        '''simple docstring'''







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


    @parameterized.expand([(2,)]		)
    def 						snake_case__     (      self :       Dict,lowercase_ :       Optional[Any]		)->    int:





        '''simple docstring'''







        A__          =   self.create_estimator(lowercase_		)

        # run training
        estimator.fit()

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

        # extract kpis
        A__          =   list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']		)
        A__          =   list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']		)
        # get train time from SageMaker job, this includes starting, preprocessing, stopping
        A__          =   (
            Session().describe_training_job(estimator.latest_training_job.name		).get('TrainingTimeInSeconds',9_9_9_9_9_9		)
        )

        # 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},lowercase_		)




import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]      )							->      Union[str, Any]:





    '''simple docstring'''






    return EnvironmentCommand()


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    @staticmethod
    def 						snake_case__     (      lowercase_ :       ArgumentParser		)->    Dict:





        '''simple docstring'''







        A__          =   parser.add_parser('env'		)
        download_parser.set_defaults(func=lowercase_		)


    def 						snake_case__     (      self :       List[Any]		)->    List[str]:





        '''simple docstring'''







        A__          =   huggingface_hub.__version__

        A__          =   'not installed'
        A__          =   'NA'
        if is_torch_available():
            import torch

            A__          =   torch.__version__
            A__          =   torch.cuda.is_available()

        A__          =   'not installed'
        if is_transformers_available():
            import transformers

            A__          =   transformers.__version__

        A__          =   'not installed'
        if is_accelerate_available():
            import accelerate

            A__          =   accelerate.__version__

        A__          =   'not installed'
        if is_xformers_available():
            import xformers

            A__          =   xformers.__version__

        A__          =   {
            '`diffusers` version': version,
            'Platform': platform.platform(),
            'Python version': platform.python_version(),
            'PyTorch version (GPU?)': F'{pt_version} ({pt_cuda_available})',
            'Huggingface_hub version': hub_version,
            'Transformers version': transformers_version,
            'Accelerate version': accelerate_version,
            'xFormers version': xformers_version,
            'Using GPU in script?': '<fill in>',
            'Using distributed or parallel set-up in script?': '<fill in>',
        }

        print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'		)
        print(self.format_dict(lowercase_		)		)

        return info


    @staticmethod
    def 						snake_case__     (      lowercase_ :       int		)->    Optional[Any]:





        '''simple docstring'''







        return "\n".join([F'- {prop}: {val}' for prop, val in d.items()]		) + "\n"

