

"""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_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   __lowercase      (     unittest.TestCase       ):

  def  __lowercase							(	self		:      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=A       ,)
   assert hasattr(self       ,"""env"""     )

  def  __lowercase							(	self		:      Any       ,A		:      Any     ):






   '''simple docstring'''
   # configuration for running training on smdistributed Model Parallel
   UpperCAmelCase__       :	Tuple        =       {
       """enabled""": True,
       """processes_per_host""": 8,
   }
   UpperCAmelCase__       :	str        =       {
       """enabled""": True,
       """parameters""": {
           """microbatches""": 4,
           """placement_strategy""": """spread""",
           """pipeline""": """interleaved""",
           """optimize""": """speed""",
           """partitions""": 4,
           """ddp""": True,
       },
   }

   UpperCAmelCase__       :	Optional[int]        =       {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

   UpperCAmelCase__       :	Union[str, Any]        =       """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=A       ,instance_type=self.instance_type       ,debugger_hook_config=A       ,hyperparameters={
           **self.env.hyperparameters,
           """model_name_or_path""": self.model_name_or_path,
           """max_steps""": 500,
       }       ,metric_definitions=self.env.metric_definitions       ,distribution=A       ,py_version="""py36"""       ,)

  def  __lowercase							(	self		:      Dict       ,A		:      Tuple     ):






   '''simple docstring'''
   TrainingJobAnalytics(A     ).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv"     )




  @parameterized.expand([(1,)]     )
  def  __lowercase							(	self		:      Tuple       ,A		:      Union[str, Any]     ):






   '''simple docstring'''
   # create estimator
   UpperCAmelCase__       :	Optional[int]        =       self.create_estimator(A     )

   # run training
   estimator.fit()

   # result dataframe
   UpperCAmelCase__       :	List[Any]        =       TrainingJobAnalytics(estimator.latest_training_job.name     ).dataframe()

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








from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_							:					int		 =   {
    '''configuration_informer''': [
        '''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InformerConfig''',
    ],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_							:					List[str]		 =   [
			    '''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''InformerForPrediction''',
			    '''InformerModel''',
			    '''InformerPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_informer import (
						    INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    InformerForPrediction,
						    InformerModel,
						    InformerPreTrainedModel,
						)

else:
			import sys

			UpperCAmelCase_							:					Optional[int]		 =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





