
"""simple docstring"""



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": 6_5_0, "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": 6_0_0, "eval_accuracy": 0.3, "eval_loss": 0.9},
        },
    ]							)
class        _SCREAMING_SNAKE_CASE  (  unittest.TestCase							):






				'''simple docstring'''



				def 				__lowerCamelCase	(					self	:			List[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=UpperCAmelCase_				,     )
											assert hasattr(self				,     'env'  )


				def 				__lowerCamelCase	(					self	:			int				,     UpperCAmelCase_	:			Any=1  )		->  str:







											"""simple docstring"""




											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 				__lowerCamelCase	(					self	:			Any				,     UpperCAmelCase_	:			Dict  )		->  Dict:







											"""simple docstring"""




											TrainingJobAnalytics(UpperCAmelCase_  ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""  )







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







											"""simple docstring"""




											_lowerCAmelCase  	=			self.create_estimator()

											# run training
											estimator.fit()

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

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








"""simple docstring"""



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


_snake_case          =						logging.get_logger(__name__)

_snake_case          =						{
    '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json''',
}
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Dict							=					"gpt_neox_japanese"


				def __init__(					self	:			Optional[Any]				,     UpperCAmelCase_	:			Any=32_000				,     UpperCAmelCase_	:			Dict=2_560				,     UpperCAmelCase_	:			Union[str, Any]=32				,     UpperCAmelCase_	:			Any=32				,     UpperCAmelCase_	:			Optional[Any]=4				,     UpperCAmelCase_	:			int="gelu"				,     UpperCAmelCase_	:			Union[str, Any]=1.00				,     UpperCAmelCase_	:			Any=10_000				,     UpperCAmelCase_	:			Optional[int]=2_048				,     UpperCAmelCase_	:			Optional[Any]=0.02				,     UpperCAmelCase_	:			Tuple=1E-5				,     UpperCAmelCase_	:			int=True				,     UpperCAmelCase_	:			Tuple=31_996				,     UpperCAmelCase_	:			List[Any]=31_999				,     UpperCAmelCase_	:			str=0.1				,     UpperCAmelCase_	:			List[Any]=0.0				,     **UpperCAmelCase_	:			str				,     )		->  int:







											"""simple docstring"""




											super().__init__(bos_token_id=UpperCAmelCase_				,     eos_token_id=UpperCAmelCase_				,     **UpperCAmelCase_  )
											_lowerCAmelCase  	=			vocab_size
											_lowerCAmelCase  	=			max_position_embeddings
											_lowerCAmelCase  	=			hidden_size
											_lowerCAmelCase  	=			num_hidden_layers
											_lowerCAmelCase  	=			num_attention_heads
											_lowerCAmelCase  	=			intermediate_multiple_size
											_lowerCAmelCase  	=			hidden_act
											_lowerCAmelCase  	=			rotary_pct
											_lowerCAmelCase  	=			rotary_emb_base
											_lowerCAmelCase  	=			initializer_range
											_lowerCAmelCase  	=			layer_norm_eps
											_lowerCAmelCase  	=			use_cache
											_lowerCAmelCase  	=			attention_dropout
											_lowerCAmelCase  	=			hidden_dropout






