


'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand





def 					a__       (					lowerCAmelCase__						)    ->	int:
    return EnvironmentCommand()





def 					a__       (					lowerCAmelCase__						)    ->	Dict:
    return EnvironmentCommand(args.accelerate_config_file						)







class 			lowerCamelCase_   (	__a      ):






      @staticmethod
      def lowercase_							(					_A				:    ArgumentParser ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				parser.add_parser('''env''' )
          download_parser.set_defaults(func=_A )
          download_parser.add_argument(
              '''--accelerate-config_file'''						,						default=_A						,						help='''The accelerate config file to use for the default values in the launching script.'''						,						)
          download_parser.set_defaults(func=_A )






      def __init__(					self				:    Any						,						_A				:    List[str]						,						*_A				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				accelerate_config_file






      def lowercase_							(					self				:    Optional[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[Any]													=				'''not installed'''
          if is_safetensors_available():
              import safetensors

              UpperCAmelCase__			:		List[str]													=				safetensors.__version__
          elif importlib.util.find_spec('''safetensors''' ) is not None:
              import safetensors

              UpperCAmelCase__			:		Union[str, Any]													=				f"""{safetensors.__version__} but is ignored because of PyTorch version too old."""

          UpperCAmelCase__			:		Optional[int]													=				'''not installed'''
          UpperCAmelCase__			:		str													=				'''not found'''
          if is_accelerate_available():
              import accelerate
              from accelerate.commands.config import default_config_file, load_config_from_file

              UpperCAmelCase__			:		List[str]													=				accelerate.__version__
              # Get the default from the config file.
              if self._accelerate_config_file is not None or os.path.isfile(_A ):
                  UpperCAmelCase__			:		List[Any]													=				load_config_from_file(self._accelerate_config_file ).to_dict()

              UpperCAmelCase__			:		List[str]													=				(
                  '''\n'''.join([f"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()] )
                  if isinstance(_A						,						_A )
                  else f"""\t{accelerate_config}"""
              )

          UpperCAmelCase__			:		Optional[int]													=				'''not installed'''
          UpperCAmelCase__			:		str													=				'''NA'''
          if is_torch_available():
              import torch

              UpperCAmelCase__			:		Optional[Any]													=				torch.__version__
              UpperCAmelCase__			:		Dict													=				torch.cuda.is_available()

          UpperCAmelCase__			:		List[Any]													=				'''not installed'''
          UpperCAmelCase__			:		str													=				'''NA'''
          if is_tf_available():
              import tensorflow as tf

              UpperCAmelCase__			:		Dict													=				tf.__version__
              try:
                  # deprecated in v2.1
                  UpperCAmelCase__			:		List[str]													=				tf.test.is_gpu_available()
              except AttributeError:
                  # returns list of devices, convert to bool
                  UpperCAmelCase__			:		List[str]													=				bool(tf.config.list_physical_devices('''GPU''' ) )

          UpperCAmelCase__			:		Union[str, Any]													=				'''not installed'''
          UpperCAmelCase__			:		Dict													=				'''not installed'''
          UpperCAmelCase__			:		Any													=				'''not installed'''
          UpperCAmelCase__			:		int													=				'''NA'''
          if is_flax_available():
              import flax
              import jax
              import jaxlib

              UpperCAmelCase__			:		Optional[int]													=				flax.__version__
              UpperCAmelCase__			:		Optional[Any]													=				jax.__version__
              UpperCAmelCase__			:		Dict													=				jaxlib.__version__
              UpperCAmelCase__			:		Dict													=				jax.lib.xla_bridge.get_backend().platform

          UpperCAmelCase__			:		Dict													=				{
              '''`transformers` version''': version,
              '''Platform''': platform.platform(),
              '''Python version''': platform.python_version(),
              '''Huggingface_hub version''': huggingface_hub.__version__,
              '''Safetensors version''': f"""{safetensors_version}""",
              '''Accelerate version''': f"""{accelerate_version}""",
              '''Accelerate config''': f"""{accelerate_config_str}""",
              '''PyTorch version (GPU?)''': f"""{pt_version} ({pt_cuda_available})""",
              '''Tensorflow version (GPU?)''': f"""{tf_version} ({tf_cuda_available})""",
              '''Flax version (CPU?/GPU?/TPU?)''': f"""{flax_version} ({jax_backend})""",
              '''Jax version''': f"""{jax_version}""",
              '''JaxLib version''': f"""{jaxlib_version}""",
              '''Using GPU in script?''': '''<fill in>''',
              '''Using distributed or parallel set-up in script?''': '''<fill in>''',
          }

          print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n''' )
          print(self.format_dict(_A ) )

          return info






      @staticmethod
      def lowercase_							(					_A				:    List[str] ):


          '''simple docstring'''


          return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()] ) + "\n"




'''simple docstring'''
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
  UpperCamelCase__    					=      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''')
  UpperCamelCase__					,   UpperCamelCase__    					=      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''')
    UpperCamelCase__    					=      rh.cluster(
        name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
    )
  else:
    UpperCamelCase__    					=      rh.cluster(
        name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
    )
  UpperCamelCase__    					=      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)
