import argparse
import os
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available


def    a__       (						__UpperCamelCase=None ):





      if subparsers is not None:
            SCREAMING_SNAKE_CASE_      =     subparsers.add_parser("env" )
      else:
            SCREAMING_SNAKE_CASE_      =     argparse.ArgumentParser("Accelerate env command" )

      parser.add_argument(
          "--config_file"   ,					default=__UpperCamelCase   ,					help="The config file to use for the default values in the launching script." )

      if subparsers is not None:
            parser.set_defaults(func=__UpperCamelCase )
      return parser


def    a__       (						__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     torch.__version__
      SCREAMING_SNAKE_CASE_      =     torch.cuda.is_available()
      SCREAMING_SNAKE_CASE_      =     is_xpu_available()
      SCREAMING_SNAKE_CASE_      =     is_npu_available()

      SCREAMING_SNAKE_CASE_      =     """Not found"""
      # Get the default from the config file.
      if args.config_file is not None or os.path.isfile(__UpperCamelCase ):
            SCREAMING_SNAKE_CASE_      =     load_config_from_file(args.config_file ).to_dict()

      SCREAMING_SNAKE_CASE_      =     {
          """`Accelerate` version""": version,
          """Platform""": platform.platform(),
          """Python version""": platform.python_version(),
          """Numpy version""": np.__version__,
          """PyTorch version (GPU?)""": F'''{pt_version} ({pt_cuda_available})''',
          """PyTorch XPU available""": str(__UpperCamelCase ),
          """PyTorch NPU available""": str(__UpperCamelCase ),
          """System RAM""": F'''{psutil.virtual_memory().total / 1_0_2_4 ** 3:.2f} GB''',
      }
      if pt_cuda_available:
            SCREAMING_SNAKE_CASE_      =     torch.cuda.get_device_name()

      print("\nCopy-and-paste the text below in your GitHub issue\n" )
      print("\n".join([F'''- {prop}: {val}''' for prop, val in info.items()] ) )

      print("- `Accelerate` default config:" if args.config_file is None else "- `Accelerate` config passed:" )
      SCREAMING_SNAKE_CASE_      =     (
          """\n""".join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()] )
          if isinstance(__UpperCamelCase   ,					__UpperCamelCase )
          else F'''\t{accelerate_config}'''
      )
      print(__UpperCamelCase )

      SCREAMING_SNAKE_CASE_      =     accelerate_config

      return info


def    a__       (						):
      SCREAMING_SNAKE_CASE_      =     env_command_parser()
      SCREAMING_SNAKE_CASE_      =     parser.parse_args()
      env_command(__UpperCamelCase )
      return 0


if __name__ == "__main__":
      raise SystemExit(main())







'''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     ):
  '''simple docstring'''




  def 							UpperCAmelCase_	(    self      )    ->		Optional[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=__UpperCAmelCase  ,)
      assert hasattr(self  ,"""env"""      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Optional[Any]:
      lowerCAmelCase__ :  Optional[int]							      =					F"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
      # distributed data settings
      lowerCAmelCase__ :  Any							      =					{"""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=__UpperCAmelCase  ,instance_count=__UpperCAmelCase  ,instance_type=self.instance_type  ,debugger_hook_config=__UpperCAmelCase  ,hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path}  ,metric_definitions=self.env.metric_definitions  ,distribution=__UpperCAmelCase  ,py_version="""py36"""  ,)




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Optional[Any]:
      TrainingJobAnalytics(__UpperCAmelCase      ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""      )






  @parameterized.expand([(2,)]      )
  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Any:
      # create estimator
      lowerCAmelCase__ :  List[Any]							      =					self.create_estimator(__UpperCAmelCase      )

      # run training
      estimator.fit()

      # result dataframe
      lowerCAmelCase__ :  Any							      =					TrainingJobAnalytics(estimator.latest_training_job.name      ).dataframe()

      # extract kpis
      lowerCAmelCase__ :  int							      =					list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]      )
      lowerCAmelCase__ :  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
      lowerCAmelCase__ :  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}  ,__UpperCAmelCase      )


