




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''': 1600, '''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''': 1600, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
    ]       )
class   __a(					unittest.TestCase       ):






    """simple docstring"""





    def 							a__				(				self  )			->		Union[str, Any]:
         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=_SCREAMING_SNAKE_CASE       ,)
         assert hasattr(self       ,'''env'''  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Optional[int]:
         # configuration for running training on smdistributed Model Parallel
         UpperCAmelCase_      :							Tuple       =      {
             '''enabled''': True,
             '''processes_per_host''': 8,
         }
         UpperCAmelCase_      :							Optional[int]       =      {
             '''enabled''': True,
             '''parameters''': {
                 '''microbatches''': 4,
                 '''placement_strategy''': '''spread''',
                 '''pipeline''': '''interleaved''',
                 '''optimize''': '''speed''',
                 '''partitions''': 4,
                 '''ddp''': True,
             },
         }

         UpperCAmelCase_      :							Union[str, Any]       =      {'''smdistributed''': {'''modelparallel''': smp_options}, '''mpi''': mpi_options}

         UpperCAmelCase_      :							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=_SCREAMING_SNAKE_CASE       ,instance_type=self.instance_type       ,debugger_hook_config=_SCREAMING_SNAKE_CASE       ,hyperparameters={
                 **self.env.hyperparameters,
                 '''model_name_or_path''': self.model_name_or_path,
                 '''max_steps''': 500,
             }       ,metric_definitions=self.env.metric_definitions       ,distribution=_SCREAMING_SNAKE_CASE       ,py_version='''py36'''       ,)





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Optional[int]:
         TrainingJobAnalytics(_SCREAMING_SNAKE_CASE  ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''  )





    @parameterized.expand([(1,)]  )
    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Optional[int]:
         # create estimator
         UpperCAmelCase_      :							Any       =      self.create_estimator(_SCREAMING_SNAKE_CASE  )

         # run training
         estimator.fit()

         # result dataframe
         UpperCAmelCase_      :							Tuple       =      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[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_      :							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}       ,_SCREAMING_SNAKE_CASE  )





import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

__a          =			logging.getLogger()







def lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							List[str]       =      argparse.ArgumentParser()
     parser.add_argument('''-f'''				)
     UpperCAmelCase_      :							Dict       =      parser.parse_args()
     return args.f



class   __a(					_a       ):






    """simple docstring"""





    def 							a__				(				self  )			->		None:
         UpperCAmelCase_      :							int       =      logging.StreamHandler(sys.stdout  )
         logger.addHandler(_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		str:
         UpperCAmelCase_      :							int       =      get_gpu_count()

         if n_gpu > 1:
              pass
              # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
              # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
              # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
              # cmd = [sys.executable] + distributed_args + args
              # execute_subprocess_async(cmd, env=self.get_env())
              # XXX: test the results - need to save them first into .json file
         else:
              args.insert(0       ,'''run_glue_deebert.py'''  )
              with patch.object(_SCREAMING_SNAKE_CASE       ,'''argv'''       ,_SCREAMING_SNAKE_CASE  ):
                   UpperCAmelCase_      :							List[str]       =      run_glue_deebert.main()
                   for value in result.values():
                        self.assertGreaterEqual(_SCREAMING_SNAKE_CASE       ,0.6_66  )





    @slow
    @require_torch_non_multi_gpu
    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							List[Any]       =      '''
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            '''.split()
         self.run_and_check(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Union[str, Any]       =      '''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Any       =      '''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(_SCREAMING_SNAKE_CASE  )