

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       SCREAMING_SNAKE_CASE__					(unittest.TestCase     ):





    def       snake_case_				(      self):
         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       snake_case_				(      self						,   a):
         lowercase__    :   Any      =       f"""{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}"""
         # distributed data settings
         lowercase__    :   List[str]      =       {'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       snake_case_				(      self						,   a):
         TrainingJobAnalytics(a).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv""")





    @parameterized.expand([(2,)])
    def       snake_case_				(      self						,   a):
         # create estimator
         lowercase__    :   Union[str, Any]      =       self.create_estimator(a)

         # run training
         estimator.fit()

         # result dataframe
         lowercase__    :   List[Any]      =       TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

         # extract kpis
         lowercase__    :   Dict      =       list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value'])
         lowercase__    :   str      =       list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value'])
         # get train time from SageMaker job, this includes starting, preprocessing, stopping
         lowercase__    :   List[str]      =       (
             Session().describe_training_job(estimator.latest_training_job.name).get('TrainingTimeInSeconds'						,   99_9999)
         )

         # 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 argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
       # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
       # setup instructions, if using on-demand hardware

       # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
       # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
       # Throw an error if user passes both BYO and on-demand cluster args
       # Otherwise, use default values
       snake_case_			  = argparse.ArgumentParser()
       parser.add_argument('''--user''', type=str, default='''ubuntu''')
       parser.add_argument('''--host''', type=str, default='''localhost''')
       parser.add_argument('''--key_path''', type=str, default=None)
       parser.add_argument('''--instance''', type=str, default='''V100:1''')
       parser.add_argument('''--provider''', type=str, default='''cheapest''')
       parser.add_argument('''--use_spot''', type=bool, default=False)
       parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
       snake_case_  ,		snake_case_			  = parser.parse_known_args()
       if args.host != "localhost":
              if args.instance != "V100:1" or args.provider != "cheapest":
                     raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
              snake_case_			  = rh.cluster(
                  name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
              )
       else:
              snake_case_			  = rh.cluster(
                  name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
              )
       snake_case_			  = args.example.rsplit('''/''', 1)[0]

       # Set up remote environment
       cluster.install_packages(['''pip:./'''])  # Installs transformers from local source
       # Note transformers is copied into the home directory on the remote machine, so we can install from there
       cluster.run([F'''pip install -r transformers/examples/{example_dir}/requirements.txt'''])
       cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])

       # Run example. You can bypass the CLI wrapper and paste your own code here.
       cluster.run([F'''python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}'''])

       # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
       # from my_script... import train
       # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
       # launch_train_gpu = rh.function(fn=train,
       #                                system=gpu,
       #                                reqs=reqs,
       #                                name='train_bert_glue')
       #
       # We can pass in arguments just like we would to a function:
       # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
       #                  stream_logs=True)

