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        UpperCamelCase						(				unittest.TestCase				):





						def        A_							(self						)				->				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=__UpperCamelCase			,     )
												assert hasattr(self			,     """env"""						)





						def        A_							(self			,     __UpperCamelCase=1						)				->				Tuple:
												# 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        A_							(self			,     __UpperCamelCase						)				->				Dict:
												TrainingJobAnalytics(__UpperCamelCase						).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''						)





						def        A_							(self						)				->				str:
												# create estimator
												UpperCamelCase_		:							Optional[Any]  =		self.create_estimator()

												# run training
												estimator.fit()

												# result dataframe
												UpperCamelCase_		:							List[str]  =		TrainingJobAnalytics(estimator.latest_training_job.name						).dataframe()

												# extract kpis
												UpperCamelCase_		:							List[Any]  =		list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]						)
												UpperCamelCase_		:							Tuple  =		list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]						)
												# get train time from SageMaker job, this includes starting, preprocessing, stopping
												UpperCamelCase_		:							Union[str, Any]  =		(
												    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						)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE		:     int         =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	{
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}

class        UpperCamelCase						(				__a				):
						a__		:List[str]      =			'''megatron-bert'''





						def __init__(self			,     __UpperCamelCase=29_056			,     __UpperCamelCase=1_024			,     __UpperCamelCase=24			,     __UpperCamelCase=16			,     __UpperCamelCase=4_096			,     __UpperCamelCase="gelu"			,     __UpperCamelCase=0.1			,     __UpperCamelCase=0.1			,     __UpperCamelCase=512			,     __UpperCamelCase=2			,     __UpperCamelCase=0.02			,     __UpperCamelCase=1E-1_2			,     __UpperCamelCase=0			,     __UpperCamelCase="absolute"			,     __UpperCamelCase=True			,     **__UpperCamelCase			,     )				->				List[Any]:
												super().__init__(pad_token_id=__UpperCamelCase			,     **__UpperCamelCase						)

												UpperCamelCase_		:							Union[str, Any]  =		vocab_size
												UpperCamelCase_		:							Union[str, Any]  =		hidden_size
												UpperCamelCase_		:							Any  =		num_hidden_layers
												UpperCamelCase_		:							str  =		num_attention_heads
												UpperCamelCase_		:							List[Any]  =		hidden_act
												UpperCamelCase_		:							Dict  =		intermediate_size
												UpperCamelCase_		:							Optional[int]  =		hidden_dropout_prob
												UpperCamelCase_		:							Dict  =		attention_probs_dropout_prob
												UpperCamelCase_		:							Any  =		max_position_embeddings
												UpperCamelCase_		:							str  =		type_vocab_size
												UpperCamelCase_		:							Tuple  =		initializer_range
												UpperCamelCase_		:							List[str]  =		layer_norm_eps
												UpperCamelCase_		:							Tuple  =		position_embedding_type
												UpperCamelCase_		:							Tuple  =		use_cache






