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': 650, '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': 600, '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': 600, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
        },
    ]    )
class      __lowercase					(    unittest.TestCase    ):

						def        __a		(	self				:      Tuple   )							->		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=__lowerCamelCase							,							)
									assert hasattr(self							,							'''env'''   )

						def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      List[str]   )							->		List[str]:


									'''simple docstring'''

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

						def        __a		(	self				:      int							,							__lowerCamelCase				:      Optional[Any]   )							->		Dict:


									'''simple docstring'''

									TrainingJobAnalytics(__lowerCamelCase   ).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv'   )



						@parameterized.expand([(2,)]   )
						def        __a		(	self				:      Any							,							__lowerCamelCase				:      List[str]   )							->		Any:


									'''simple docstring'''

									lowercase							=		self.create_estimator(__lowerCamelCase   )

									# run training
									estimator.fit()

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

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


# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


A_      =      re.compile(R"^(?P<major>\d+)" R"\.(?P<minor>\d+)" R"\.(?P<patch>\d+)$")






@total_ordering
@dataclass
class      __lowercase					:
						lowercase					 =     42
						lowercase					 =     None
						lowercase					 =     None
						lowercase					 =     None
						lowercase					 =     None

						def        __a		(	self				:      int   )							->		str:


									'''simple docstring'''

									lowercase		,lowercase		,lowercase							=		_str_to_version_tuple(self.version_str   )

						def __repr__(	self				:      Union[str, Any]   )							->		int:


									'''simple docstring'''

									return f'{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'

						@property
						def        __a		(	self				:      Dict   )							->		Union[str, Any]:


									'''simple docstring'''

									return self.major, self.minor, self.patch

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      Any   )							->		Dict:


									'''simple docstring'''

									if isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
												return Version(__lowerCamelCase   )
									elif isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
												return other
									raise TypeError(f'{other} (type {type(__lowerCamelCase   )}) cannot be compared to version.'   )

						def __eq__(	self				:      Tuple							,							__lowerCamelCase				:      Tuple   )							->		List[Any]:


									'''simple docstring'''

									try:
												lowercase							=		self._validate_operand(__lowerCamelCase   )
									except (TypeError, ValueError):
												return False
									else:
												return self.tuple == other.tuple

						def __lt__(	self				:      Any							,							__lowerCamelCase				:      Union[str, Any]   )							->		int:


									'''simple docstring'''

									lowercase							=		self._validate_operand(__lowerCamelCase   )
									return self.tuple < other.tuple

						def __hash__(	self				:      int   )							->		str:


									'''simple docstring'''

									return hash(_version_tuple_to_str(self.tuple   )   )

						@classmethod
						def        __a		(	cls				:      List[str]							,							__lowerCamelCase				:      Any   )							->		int:


									'''simple docstring'''

									lowercase							=		{f.name for f in dataclasses.fields(cls   )}
									return cls(**{k: v for k, v in dic.items() if k in field_names}   )



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


									'''simple docstring'''

									return self.version_str






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



			"""simple docstring"""







			lowercase							=		_VERSION_REG.match(UpperCAmelCase       )
			if not res:
						raise ValueError(f'Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.'       )
			return tuple(int(UpperCAmelCase       ) for v in [res.group('''major'''       ), res.group('''minor'''       ), res.group('''patch'''       )]       )






def        __UpperCAmelCase    (       UpperCAmelCase       )->       int:



			"""simple docstring"""







			return ".".join(str(UpperCAmelCase       ) for v in version_tuple       )





