



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     _A       (	unittest.TestCase    ):



       def 		_lowerCamelCase			(		self							:     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=__SCREAMING_SNAKE_CASE     ,		)
           assert hasattr(self     ,		'''env''')



       def 		_lowerCamelCase			(		self							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=1):


           '''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=__SCREAMING_SNAKE_CASE     ,		instance_type=self.instance_type     ,		debugger_hook_config=__SCREAMING_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     ,		__SCREAMING_SNAKE_CASE							:     Dict):


           '''simple docstring'''

           TrainingJobAnalytics(__SCREAMING_SNAKE_CASE).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv')





       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __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'''     ,		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}     ,		__SCREAMING_SNAKE_CASE)










import logging

from transformers.configuration_utils import PretrainedConfig


__snake_case						:Any      		=     logging.getLogger(__name__)

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Optional[Any]											=						'''masked_bert'''



       def __init__(		self							:     str     ,		__SCREAMING_SNAKE_CASE							:     int=30_522     ,		__SCREAMING_SNAKE_CASE							:     str=768     ,		__SCREAMING_SNAKE_CASE							:     List[str]=12     ,		__SCREAMING_SNAKE_CASE							:     Any=12     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=3_072     ,		__SCREAMING_SNAKE_CASE							:     List[Any]="gelu"     ,		__SCREAMING_SNAKE_CASE							:     int=0.1     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.1     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=512     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=2     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.02     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=1E-12     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=0     ,		__SCREAMING_SNAKE_CASE							:     List[str]="topK"     ,		__SCREAMING_SNAKE_CASE							:     List[Any]="constant"     ,		__SCREAMING_SNAKE_CASE							:     int=0.0     ,		**__SCREAMING_SNAKE_CASE							:     List[Any]     ,		):


           '''simple docstring'''

           super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)

           __a						       =				vocab_size
           __a						       =				hidden_size
           __a						       =				num_hidden_layers
           __a						       =				num_attention_heads
           __a						       =				hidden_act
           __a						       =				intermediate_size
           __a						       =				hidden_dropout_prob
           __a						       =				attention_probs_dropout_prob
           __a						       =				max_position_embeddings
           __a						       =				type_vocab_size
           __a						       =				initializer_range
           __a						       =				layer_norm_eps
           __a						       =				pruning_method
           __a						       =				mask_init
           __a						       =				mask_scale





