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_600, '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_600, 'eval_accuracy': 0.3, 'eval_loss': 1.2},
        },
    ] )
class  UpperCamelCase				( unittest.TestCase ):

     '''simple docstring'''

     def 		UpperCamelCase_						(					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 		UpperCamelCase_						(					self				,				A_						) ->				Union[str, Any]:






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

           _lowerCamelCase			    =				{'''smdistributed''': {'''modelparallel''': smp_options}, '''mpi''': mpi_options}

           _lowerCamelCase			    =				'''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''': 5_00,
               }				,				metric_definitions=self.env.metric_definitions				,				distribution=A_				,				py_version='''py36'''				,				)



     def 		UpperCamelCase_						(					self				,				A_						) ->				Tuple:






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





     @parameterized.expand([(1,)]						)
     def 		UpperCamelCase_						(					self				,				A_						) ->				List[str]:






           """simple docstring"""
           # create estimator
           _lowerCamelCase			    =				self.create_estimator(A_						)

           # run training
           estimator.fit()

           # result dataframe
           _lowerCamelCase			    =				TrainingJobAnalytics(estimator.latest_training_job.name						).dataframe()

           # extract kpis
           _lowerCamelCase			    =				list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']						)
           _lowerCamelCase			    =				list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']						)
           # get train time from SageMaker job, this includes starting, preprocessing, stopping
           _lowerCamelCase			    =				(
               Session().describe_training_job(estimator.latest_training_job.name						).get('''TrainingTimeInSeconds'''				,				99_99_99						)
           )

           # 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_						)
import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


snake_case__				= logging.get_logger(__name__)



class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     def __init__(					self				,				*A_				,				**A_						) ->				None:






           """simple docstring"""
           warnings.warn(
               '''The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
               ''' use DPTImageProcessor instead.'''				,				A_				,				)
           super().__init__(*A_				,				**A_						)