





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


_UpperCAmelCase        =				logging.get_logger(__name__)

_UpperCAmelCase        =				{
    'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Optional[int]         =   '''mra'''

       def __init__(		self: Tuple					,   _SCREAMING_SNAKE_CASE: Tuple=50265					,   _SCREAMING_SNAKE_CASE: List[str]=768					,   _SCREAMING_SNAKE_CASE: Any=12					,   _SCREAMING_SNAKE_CASE: Optional[int]=12					,   _SCREAMING_SNAKE_CASE: Optional[int]=3072					,   _SCREAMING_SNAKE_CASE: List[str]="gelu"					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=0.1					,   _SCREAMING_SNAKE_CASE: Optional[Any]=0.1					,   _SCREAMING_SNAKE_CASE: int=512					,   _SCREAMING_SNAKE_CASE: Dict=1					,   _SCREAMING_SNAKE_CASE: Optional[Any]=0.02					,   _SCREAMING_SNAKE_CASE: Dict=1e-5					,   _SCREAMING_SNAKE_CASE: int="absolute"					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=4					,   _SCREAMING_SNAKE_CASE: List[str]="full"					,   _SCREAMING_SNAKE_CASE: List[Any]=0					,   _SCREAMING_SNAKE_CASE: Optional[int]=0					,   _SCREAMING_SNAKE_CASE: Any=1					,   _SCREAMING_SNAKE_CASE: List[Any]=0					,   _SCREAMING_SNAKE_CASE: Dict=2					,   **_SCREAMING_SNAKE_CASE: str					,   )       -> List[str]:




              """simple docstring"""


              super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE					,   bos_token_id=_SCREAMING_SNAKE_CASE					,   eos_token_id=_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  vocab_size
              UpperCamelCase_          =  max_position_embeddings
              UpperCamelCase_          =  hidden_size
              UpperCamelCase_          =  num_hidden_layers
              UpperCamelCase_          =  num_attention_heads
              UpperCamelCase_          =  intermediate_size
              UpperCamelCase_          =  hidden_act
              UpperCamelCase_          =  hidden_dropout_prob
              UpperCamelCase_          =  attention_probs_dropout_prob
              UpperCamelCase_          =  initializer_range
              UpperCamelCase_          =  type_vocab_size
              UpperCamelCase_          =  layer_norm_eps
              UpperCamelCase_          =  position_embedding_type
              UpperCamelCase_          =  block_per_row
              UpperCamelCase_          =  approx_mode
              UpperCamelCase_          =  initial_prior_first_n_blocks
              UpperCamelCase_          =  initial_prior_diagonal_n_blocks








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 lowercase (		self: Union[str, Any]		)       -> List[str]:




              """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 lowercase (		self: List[str]					,   _SCREAMING_SNAKE_CASE: Dict		)       -> str:




              """simple docstring"""


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

       def lowercase (		self: List[str]					,   _SCREAMING_SNAKE_CASE: Any		)       -> List[Any]:




              """simple docstring"""


              TrainingJobAnalytics(_SCREAMING_SNAKE_CASE		).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''		)



       @parameterized.expand([(2,)]		)
       def lowercase (		self: str					,   _SCREAMING_SNAKE_CASE: Tuple		)       -> Optional[Any]:




              """simple docstring"""


              UpperCamelCase_          =  self.create_estimator(_SCREAMING_SNAKE_CASE		)

              # run training
              estimator.fit()

              # result dataframe
              UpperCamelCase_          =  TrainingJobAnalytics(estimator.latest_training_job.name		).dataframe()

              # extract kpis
              UpperCamelCase_          =  list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]		)
              UpperCamelCase_          =  list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]		)
              # get train time from SageMaker job, this includes starting, preprocessing, stopping
              UpperCamelCase_          =  (
                  Session().describe_training_job(estimator.latest_training_job.name		).get("TrainingTimeInSeconds"					,   999999		)
              )

              # 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		)

