


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


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

snake_case							:       Tuple     			=      {
    '''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    Any          =			'''roc_bert'''
					def __init__(      self   :Optional[Any]			,__snake_case   :Optional[Any]=3_05_22			,__snake_case   :int=7_68			,__snake_case   :int=12			,__snake_case   :Optional[int]=12			,__snake_case   :Union[str, Any]=30_72			,__snake_case   :List[Any]="gelu"			,__snake_case   :Union[str, Any]=0.1			,__snake_case   :Optional[int]=0.1			,__snake_case   :str=5_12			,__snake_case   :Tuple=2			,__snake_case   :int=0.02			,__snake_case   :Tuple=1E-12			,__snake_case   :Union[str, Any]=True			,__snake_case   :int=0			,__snake_case   :int="absolute"			,__snake_case   :Optional[Any]=None			,__snake_case   :Dict=True			,__snake_case   :List[str]=True			,__snake_case   :List[Any]=7_68			,__snake_case   :Optional[Any]=9_10			,__snake_case   :Dict=5_12			,__snake_case   :Optional[int]=2_48_58			,__snake_case   :Dict=True			,**__snake_case   :str			,)					->    Any:
												a__             =     vocab_size
												a__             =     max_position_embeddings
												a__             =     hidden_size
												a__             =     num_hidden_layers
												a__             =     num_attention_heads
												a__             =     intermediate_size
												a__             =     hidden_act
												a__             =     hidden_dropout_prob
												a__             =     attention_probs_dropout_prob
												a__             =     initializer_range
												a__             =     type_vocab_size
												a__             =     layer_norm_eps
												a__             =     use_cache
												a__             =     enable_pronunciation
												a__             =     enable_shape
												a__             =     pronunciation_embed_dim
												a__             =     pronunciation_vocab_size
												a__             =     shape_embed_dim
												a__             =     shape_vocab_size
												a__             =     concat_input
												a__             =     position_embedding_type
												a__             =     classifier_dropout
												super().__init__(pad_token_id=__snake_case			,**__snake_case      )








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        snake_case_   (unittest.TestCase      ):
					def  lowerCamelCase__(      self   :Dict      )					->    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=__snake_case			,)
												assert hasattr(self			,'env'      )
					def  lowerCamelCase__(      self   :List[Any]			,__snake_case   :Optional[Any]=1      )					->    int:
												# 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=__snake_case			,instance_type=self.instance_type			,debugger_hook_config=__snake_case			,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   :Dict			,__snake_case   :str      )					->    Optional[int]:
												TrainingJobAnalytics(__snake_case      ).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv'      )



					def  lowerCamelCase__(      self   :Tuple      )					->    Union[str, Any]:
												# create estimator
												a__             =     self.create_estimator()

												# 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'			,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}			,__snake_case      )




