


'''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""": 650, """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""": 600, """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""": 600, """eval_accuracy""": 0.6, """eval_loss""": 0.7},
        },
    ]    )
class 	__lowerCAmelCase		(							unittest.TestCase    ):


  '''simple docstring'''







  def        _a							(							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        _a							(							self :       Optional[Any]	,_a :       Dict ):

        '''simple docstring'''




        A_						:  str          =				f'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
        # distributed data settings
        A_						:  Dict          =				{"""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        _a							(							self :       Tuple	,_a :       Tuple ):

        '''simple docstring'''




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






  @parameterized.expand([(2,)] )
  def        _a							(							self :       Any	,_a :       str ):

        '''simple docstring'''




        A_						:  List[Any]          =				self.create_estimator(_a )

        # run training
        estimator.fit()

        # result dataframe
        A_						:  Optional[int]          =				TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()

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




'''simple docstring'''


import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


__magic_name__			=      logging.get_logger(__name__)



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  def __init__(							self :       Union[str, Any]	,*_a :       Optional[Any]	,**_a :       Optional[int] ):

        '''simple docstring'''




        warnings.warn(
            """The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
            """ use OwlViTImageProcessor instead."""	,_a	,)
        super().__init__(*_a	,**_a )
