


'''simple docstring'''






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_5_0, '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_0_0, '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_0_0, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
        },
    ]							)
class 						__UpperCamelCase	(						unittest.TestCase							):





							def 		a__   (  self		:Union[str, 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		:List[str]  ,_UpperCamelCase		:str			):
									snake_case_		:							Any			       =						F'''{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'''
									# distributed data settings
									snake_case_		:							Any			       =						{"""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=_UpperCamelCase  ,instance_count=_UpperCamelCase  ,instance_type=self.instance_type  ,debugger_hook_config=_UpperCamelCase  ,hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path}  ,metric_definitions=self.env.metric_definitions  ,distribution=_UpperCamelCase  ,py_version="""py36"""  ,)





							def 		a__   (  self		:List[Any]  ,_UpperCamelCase		:Any			):
									TrainingJobAnalytics(_UpperCamelCase			).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''			)





							@parameterized.expand([(2,)]			)
							def 		a__   (  self		:List[str]  ,_UpperCamelCase		:str			):
									# create estimator
									snake_case_		:							str			       =						self.create_estimator(_UpperCamelCase			)

									# run training
									estimator.fit()

									# result dataframe
									snake_case_		:							Any			       =						TrainingJobAnalytics(estimator.latest_training_job.name			).dataframe()

									# extract kpis
									snake_case_		:							List[Any]			       =						list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]			)
									snake_case_		:							Optional[Any]			       =						list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]			)
									# get train time from SageMaker job, this includes starting, preprocessing, stopping
									snake_case_		:							List[Any]			       =						(
									    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}  ,_UpperCamelCase			)



'''simple docstring'''






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


__A	:	Optional[int]     =     logging.get_logger(__name__)

__A	:	List[Any]     =     {
    'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
    'studio-ousia/luke-large': 'https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json',
}
class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Union[str, Any]   		=  'luke'





							def __init__(  self		:List[str]  ,_UpperCamelCase		:Tuple=5_0_2_6_7  ,_UpperCamelCase		:Tuple=5_0_0_0_0_0  ,_UpperCamelCase		:str=7_6_8  ,_UpperCamelCase		:List[Any]=2_5_6  ,_UpperCamelCase		:List[str]=1_2  ,_UpperCamelCase		:str=1_2  ,_UpperCamelCase		:Union[str, Any]=3_0_7_2  ,_UpperCamelCase		:int="gelu"  ,_UpperCamelCase		:int=0.1  ,_UpperCamelCase		:Dict=0.1  ,_UpperCamelCase		:Dict=5_1_2  ,_UpperCamelCase		:Optional[Any]=2  ,_UpperCamelCase		:int=0.02  ,_UpperCamelCase		:Any=1E-1_2  ,_UpperCamelCase		:Tuple=True  ,_UpperCamelCase		:Any=None  ,_UpperCamelCase		:Any=1  ,_UpperCamelCase		:int=0  ,_UpperCamelCase		:List[Any]=2  ,**_UpperCamelCase		:str  ,):
									super().__init__(pad_token_id=_UpperCamelCase  ,bos_token_id=_UpperCamelCase  ,eos_token_id=_UpperCamelCase  ,**_UpperCamelCase			)

									snake_case_		:							Any			       =						vocab_size
									snake_case_		:							List[Any]			       =						entity_vocab_size
									snake_case_		:							Optional[Any]			       =						hidden_size
									snake_case_		:							List[Any]			       =						entity_emb_size
									snake_case_		:							Dict			       =						num_hidden_layers
									snake_case_		:							Union[str, Any]			       =						num_attention_heads
									snake_case_		:							Dict			       =						hidden_act
									snake_case_		:							Dict			       =						intermediate_size
									snake_case_		:							Any			       =						hidden_dropout_prob
									snake_case_		:							List[Any]			       =						attention_probs_dropout_prob
									snake_case_		:							Optional[Any]			       =						max_position_embeddings
									snake_case_		:							Any			       =						type_vocab_size
									snake_case_		:							int			       =						initializer_range
									snake_case_		:							Union[str, Any]			       =						layer_norm_eps
									snake_case_		:							Tuple			       =						use_entity_aware_attention
									snake_case_		:							Tuple			       =						classifier_dropout