


'''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.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      lowerCamelCase_  (      unittest.TestCase ):






      def lowercase_				(   self							:       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=_A		,		)
          assert hasattr(self		,		'''env'''					)






      def lowercase_				(   self							:       List[Any]		,		_A							:       Optional[Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						List[Any]						=							f"""{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}"""
          # distributed data settings
          UpperCAmelCase__     :						int						=							{'''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=_A		,		instance_count=_A		,		instance_type=self.instance_type		,		debugger_hook_config=_A		,		hyperparameters={**self.env.distributed_hyperparameters, '''model_name_or_path''': self.model_name_or_path}		,		metric_definitions=self.env.metric_definitions		,		distribution=_A		,		py_version='''py36'''		,		)






      def lowercase_				(   self							:       Optional[int]		,		_A							:       Any					):


          '''simple docstring'''


          TrainingJobAnalytics(_A					).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""					)






      @parameterized.expand([(2,)]					)
      def lowercase_				(   self							:       Optional[int]		,		_A							:       Optional[int]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Optional[Any]						=							self.create_estimator(_A					)

          # run training
          estimator.fit()

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

          # extract kpis
          UpperCAmelCase__     :						Dict						=							list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']					)
          UpperCAmelCase__     :						Optional[Any]						=							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					)







'''simple docstring'''






import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


__UpperCamelCase					    =	HUGGINGFACE_HUB_CACHE


__UpperCamelCase					    =	"config.json"
__UpperCamelCase					    =	"diffusion_pytorch_model.bin"
__UpperCamelCase					    =	"diffusion_flax_model.msgpack"
__UpperCamelCase					    =	"model.onnx"
__UpperCamelCase					    =	"diffusion_pytorch_model.safetensors"
__UpperCamelCase					    =	"weights.pb"
__UpperCamelCase					    =	"https://huggingface.co"
__UpperCamelCase					    =	default_cache_path
__UpperCamelCase					    =	"diffusers_modules"
__UpperCamelCase					    =	os.getenv("HF_MODULES_CACHE", os.path.join(hf_cache_home, "modules"))
__UpperCamelCase					    =	["fp16", "non-ema"]
__UpperCamelCase					    =	".self_attn"


