




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_model_parallelism.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 1_6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 1_6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
    ]     )
class 		UpperCamelCase__	(unittest.TestCase     ):

    '''simple docstring'''



    def     _lowercase     (    self     )     ->      List[str]:
           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     _lowercase     (    self     ,  UpperCamelCase__     )     ->      Dict:
           # configuration for running training on smdistributed Model Parallel
           lowerCamelCase   :      Any			 =					{
               "enabled": True,
               "processes_per_host": 8,
           }
           lowerCamelCase   :      Union[str, Any]			 =					{
               "enabled": True,
               "parameters": {
                   "microbatches": 4,
                   "placement_strategy": "spread",
                   "pipeline": "interleaved",
                   "optimize": "speed",
                   "partitions": 4,
                   "ddp": True,
               },
           }

           lowerCamelCase   :      List[Any]			 =					{"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

           lowerCamelCase   :      Tuple			 =					"trainer" if self.script == "run_glue.py" else "smtrainer"
           # 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}-{instance_count}-smp-{name_extension}'''     ,  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,
                   "max_steps": 500,
               }     ,  metric_definitions=self.env.metric_definitions     ,  distribution=UpperCamelCase__     ,  py_version="py36"     ,  )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      Optional[Any]:
           TrainingJobAnalytics(UpperCamelCase__     ).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''     )







    @parameterized.expand([(1,)]     )
    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      Tuple:
           # create estimator
           lowerCamelCase   :      Optional[Any]			 =					self.create_estimator(UpperCamelCase__     )

           # run training
           estimator.fit()

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

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










'''simple docstring'''







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


A__:    Dict					    =       logging.get_logger(__name__)

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







class A__       (						UpperCAmelCase__      ):
      __UpperCamelCase       :  Tuple	       =							"roc_bert"






      def __init__( self			:Optional[int]     ,						SCREAMING_SNAKE_CASE			:Tuple=3_0_5_2_2     ,						SCREAMING_SNAKE_CASE			:List[str]=7_6_8     ,						SCREAMING_SNAKE_CASE			:Dict=1_2     ,						SCREAMING_SNAKE_CASE			:List[str]=1_2     ,						SCREAMING_SNAKE_CASE			:Tuple=3_0_7_2     ,						SCREAMING_SNAKE_CASE			:List[Any]="gelu"     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=0.1     ,						SCREAMING_SNAKE_CASE			:List[Any]=0.1     ,						SCREAMING_SNAKE_CASE			:int=5_1_2     ,						SCREAMING_SNAKE_CASE			:Optional[Any]=2     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=0.02     ,						SCREAMING_SNAKE_CASE			:Optional[Any]=1e-12     ,						SCREAMING_SNAKE_CASE			:Any=True     ,						SCREAMING_SNAKE_CASE			:List[Any]=0     ,						SCREAMING_SNAKE_CASE			:Optional[int]="absolute"     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=None     ,						SCREAMING_SNAKE_CASE			:List[Any]=True     ,						SCREAMING_SNAKE_CASE			:int=True     ,						SCREAMING_SNAKE_CASE			:Optional[int]=7_6_8     ,						SCREAMING_SNAKE_CASE			:Optional[Any]=9_1_0     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=5_1_2     ,						SCREAMING_SNAKE_CASE			:str=2_4_8_5_8     ,						SCREAMING_SNAKE_CASE			:List[Any]=True     ,						**SCREAMING_SNAKE_CASE			:Tuple     ,						)       ->					Optional[int]:




             '''simple docstring'''
             _a    : List[str]     					=vocab_size
             _a    : List[str]     					=max_position_embeddings
             _a    : Optional[Any]     					=hidden_size
             _a    : List[Any]     					=num_hidden_layers
             _a    : List[str]     					=num_attention_heads
             _a    : int     					=intermediate_size
             _a    : Any     					=hidden_act
             _a    : Dict     					=hidden_dropout_prob
             _a    : int     					=attention_probs_dropout_prob
             _a    : str     					=initializer_range
             _a    : Optional[int]     					=type_vocab_size
             _a    : Any     					=layer_norm_eps
             _a    : Any     					=use_cache
             _a    : Optional[int]     					=enable_pronunciation
             _a    : Optional[Any]     					=enable_shape
             _a    : Optional[Any]     					=pronunciation_embed_dim
             _a    : Tuple     					=pronunciation_vocab_size
             _a    : Union[str, Any]     					=shape_embed_dim
             _a    : Any     					=shape_vocab_size
             _a    : Tuple     					=concat_input
             _a    : List[str]     					=position_embedding_type
             _a    : List[str]     					=classifier_dropout
             super().__init__(pad_token_id=SCREAMING_SNAKE_CASE     ,						**SCREAMING_SNAKE_CASE	)


