




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__     )












import argparse
import os
import re


SCREAMING_SNAKE_CASE__       :      List[Any]  =   'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE__       :      Optional[int]  =   re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
SCREAMING_SNAKE_CASE__       :      Tuple  =   re.compile(r'\s*\(\s*"(\S[^"]+)"')

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = False    )	-> int:
       with open(_SCREAMING_SNAKE_CASE ,"r" ,encoding="utf-8"    ) as f:
              lowerCamelCase   :      List[Any]			 =					f.read()

       lowerCamelCase   :      str			 =					content.split("\n"    )
       lowerCamelCase   :      int			 =					[]
       lowerCamelCase   :      List[Any]			 =					0
       while line_idx < len(_SCREAMING_SNAKE_CASE    ):
              if _re_intro_mapping.search(lines[line_idx]    ) is not None:
                     lowerCamelCase   :      Optional[int]			 =					len(re.search(r"^(\s*)\S" ,lines[line_idx]    ).groups()[0]    ) + 8
                     # Start of a new mapping!
                     while not lines[line_idx].startswith(" " * indent + "("    ):
                            new_lines.append(lines[line_idx]    )
                            line_idx += 1

                     lowerCamelCase   :      Optional[int]			 =					[]
                     while lines[line_idx].strip() != "]":
                            # Blocks either fit in one line or not
                            if lines[line_idx].strip() == "(":
                                   lowerCamelCase   :      List[str]			 =					line_idx
                                   while not lines[line_idx].startswith(" " * indent + ")"    ):
                                          line_idx += 1
                                   blocks.append("\n".join(lines[start_idx : line_idx + 1]    )    )
                            else:
                                   blocks.append(lines[line_idx]    )
                            line_idx += 1

                     # Sort blocks by their identifiers
                     lowerCamelCase   :      Union[str, Any]			 =					sorted(_SCREAMING_SNAKE_CASE ,key=lambda _SCREAMING_SNAKE_CASE    : _re_identifier.search(_SCREAMING_SNAKE_CASE    ).groups()[0]    )
                     new_lines += blocks
              else:
                     new_lines.append(lines[line_idx]    )
                     line_idx += 1

       if overwrite:
              with open(_SCREAMING_SNAKE_CASE ,"w" ,encoding="utf-8"    ) as f:
                     f.write("\n".join(_SCREAMING_SNAKE_CASE    )    )
       elif "\n".join(_SCREAMING_SNAKE_CASE    ) != content:
              return True

def      A      (      _SCREAMING_SNAKE_CASE = False    )	-> List[str]:
       lowerCamelCase   :      str			 =					[os.path.join(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    ) for f in os.listdir(_SCREAMING_SNAKE_CASE    ) if f.endswith(".py"    )]
       lowerCamelCase   :      Union[str, Any]			 =					[sort_auto_mapping(_SCREAMING_SNAKE_CASE ,overwrite=_SCREAMING_SNAKE_CASE    ) for fname in fnames]

       if not overwrite and any(_SCREAMING_SNAKE_CASE    ):
              lowerCamelCase   :      str			 =					[f for f, d in zip(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    ) if d]
              raise ValueError(
                  f'''The following files have auto mappings that need sorting: {", ".join(_SCREAMING_SNAKE_CASE    )}. Run `make style` to fix'''
                  " this."    )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      List[str]  =   argparse.ArgumentParser()
       parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
       SCREAMING_SNAKE_CASE__       :      List[str]  =   parser.parse_args()

       sort_all_auto_mappings(not args.check_only)






