






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''': 16_00, '''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''': 16_00, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
    ]		)
class 	_UpperCAmelCase  (  unittest.TestCase		):




      """simple docstring"""



      def 			snake_case_      (   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 			snake_case_      (   self    ,	_lowerCAmelCase		):



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

          lowerCAmelCase__   :Dict      							=				{"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

          lowerCAmelCase__   :Dict      							=				"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=_lowerCAmelCase    ,	instance_type=self.instance_type    ,	debugger_hook_config=_lowerCAmelCase    ,	hyperparameters={
                  **self.env.hyperparameters,
                  "model_name_or_path": self.model_name_or_path,
                  "max_steps": 500,
              }    ,	metric_definitions=self.env.metric_definitions    ,	distribution=_lowerCAmelCase    ,	py_version="py36"    ,	)
      def 			snake_case_      (   self    ,	_lowerCAmelCase		):



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



      @parameterized.expand([(1,)]		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          # create estimator
          lowerCAmelCase__   :int      							=				self.create_estimator(_lowerCAmelCase		)

          # run training
          estimator.fit()

          # result dataframe
          lowerCAmelCase__   :List[str]      							=				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__   :Tuple      							=				list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]		)
          # get train time from SageMaker job, this includes starting, preprocessing, stopping
          lowerCAmelCase__   :List[str]      							=				(
              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}    ,	_lowerCAmelCase		)











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_a    :   str        =			{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _a    :   Optional[Any]        =			[
     """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """WavLMForAudioFrameClassification""",
     """WavLMForCTC""",
     """WavLMForSequenceClassification""",
     """WavLMForXVector""",
     """WavLMModel""",
     """WavLMPreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_wavlm import (
      WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      WavLMForAudioFrameClassification,
      WavLMForCTC,
      WavLMForSequenceClassification,
      WavLMForXVector,
      WavLMModel,
      WavLMPreTrainedModel,
  )

else:
 import sys

 _a    :   str        =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



