





"""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_50, """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_00, """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_00, """eval_accuracy""": 0.6, """eval_loss""": 0.7},
        },
    ]     )
class 		__snake_case					(		unittest.TestCase     ):





 """simple docstring"""


 def 					UpperCamelCase__(    self  ):



     '''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=__lowerCamelCase					,						)
     assert hasattr(self					,						'''env'''  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Dict						 =     F"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
     # distributed data settings
     __A    :						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=__lowerCamelCase					,						instance_count=__lowerCamelCase					,						instance_type=self.instance_type					,						debugger_hook_config=__lowerCamelCase					,						hyperparameters={**self.env.distributed_hyperparameters, '''model_name_or_path''': self.model_name_or_path}					,						metric_definitions=self.env.metric_definitions					,						distribution=__lowerCamelCase					,						py_version='''py36'''					,						)

 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




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







 @parameterized.expand([(2,)]  )
 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						List[str]						 =     self.create_estimator(__lowerCamelCase  )

     # run training
     estimator.fit()

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

     # extract kpis
     __A    :						Optional[Any]						 =     list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']  )
     __A    :						Tuple						 =     list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']  )
     # get train time from SageMaker job, this includes starting, preprocessing, stopping
     __A    :						Union[str, Any]						 =     (
         Session().describe_training_job(estimator.latest_training_job.name  ).get('''TrainingTimeInSeconds'''					,						99_9999  )
     )

     # 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}					,						__lowerCamelCase  )











"""simple docstring"""



import logging

from transformers.configuration_utils import PretrainedConfig


a_ =			logging.getLogger(__name__)






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     """masked_bert"""

 def __init__(    self					,						__lowerCamelCase=3_0522					,						__lowerCamelCase=768					,						__lowerCamelCase=12					,						__lowerCamelCase=12					,						__lowerCamelCase=3072					,						__lowerCamelCase="gelu"					,						__lowerCamelCase=0.1					,						__lowerCamelCase=0.1					,						__lowerCamelCase=512					,						__lowerCamelCase=2					,						__lowerCamelCase=0.0_2					,						__lowerCamelCase=1e-1_2					,						__lowerCamelCase=0					,						__lowerCamelCase="topK"					,						__lowerCamelCase="constant"					,						__lowerCamelCase=0.0					,						**__lowerCamelCase					,						):



     '''simple docstring'''




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

     __A    :						Dict						 =     vocab_size
     __A    :						Union[str, Any]						 =     hidden_size
     __A    :						Tuple						 =     num_hidden_layers
     __A    :						Tuple						 =     num_attention_heads
     __A    :						Optional[Any]						 =     hidden_act
     __A    :						List[str]						 =     intermediate_size
     __A    :						Any						 =     hidden_dropout_prob
     __A    :						Optional[Any]						 =     attention_probs_dropout_prob
     __A    :						Any						 =     max_position_embeddings
     __A    :						str						 =     type_vocab_size
     __A    :						List[Any]						 =     initializer_range
     __A    :						str						 =     layer_norm_eps
     __A    :						Optional[int]						 =     pruning_method
     __A    :						str						 =     mask_init
     __A    :						Any						 =     mask_scale




