"""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      _lowerCamelCase(     a							):
       return EnvironmentCommand()



def      _lowerCamelCase(     a							):
       return EnvironmentCommand(args.accelerate_config_file							)






class       snake_case__	(    UpperCAmelCase_    ):


 @staticmethod
 def 		a__					(      lowerCamelCase		):
        __a	      =  parser.add_parser("env"		)
        download_parser.set_defaults(func=__UpperCAmelCase		)
        download_parser.add_argument(
            "--accelerate-config_file"  ,						default=__UpperCAmelCase  ,						help="The accelerate config file to use for the default values in the launching script."  ,						)
        download_parser.set_defaults(func=__UpperCAmelCase		)


 def __init__(      self  ,						lowerCamelCase  ,						*lowerCamelCase		):
        __a	      =  accelerate_config_file


 def 		a__					(      self		):
        __a	      =  "not installed"
        if is_safetensors_available():
               import safetensors

               __a	      =  safetensors.__version__
        elif importlib.util.find_spec("safetensors"		) is not None:
               import safetensors

               __a	      =  F"{safetensors.__version__} but is ignored because of PyTorch version too old."

        __a	      =  "not installed"
        __a	      =  __a	      =  "not found"
        if is_accelerate_available():
               import accelerate
               from accelerate.commands.config import default_config_file, load_config_from_file

               __a	      =  accelerate.__version__
               # Get the default from the config file.
               if self._accelerate_config_file is not None or os.path.isfile(__UpperCAmelCase		):
                      __a	      =  load_config_from_file(self._accelerate_config_file		).to_dict()

               __a	      =  (
                   "\n".join([F"\t- {prop}: {val}" for prop, val in accelerate_config.items()]		)
                   if isinstance(__UpperCAmelCase  ,						__UpperCAmelCase		)
                   else F"\t{accelerate_config}"
               )

        __a	      =  "not installed"
        __a	      =  "NA"
        if is_torch_available():
               import torch

               __a	      =  torch.__version__
               __a	      =  torch.cuda.is_available()

        __a	      =  "not installed"
        __a	      =  "NA"
        if is_tf_available():
               import tensorflow as tf

               __a	      =  tf.__version__
               try:
                      # deprecated in v2.1
                      __a	      =  tf.test.is_gpu_available()
               except AttributeError:
                      # returns list of devices, convert to bool
                      __a	      =  bool(tf.config.list_physical_devices("GPU"		)		)

        __a	      =  "not installed"
        __a	      =  "not installed"
        __a	      =  "not installed"
        __a	      =  "NA"
        if is_flax_available():
               import flax
               import jax
               import jaxlib

               __a	      =  flax.__version__
               __a	      =  jax.__version__
               __a	      =  jaxlib.__version__
               __a	      =  jax.lib.xla_bridge.get_backend().platform

        __a	      =  {
            "`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(__UpperCAmelCase		)		)

        return info






 @staticmethod
 def 		a__					(      lowerCamelCase		):
        return "\n".join([F"- {prop}: {val}" for prop, val in d.items()]		) + "\n"

def    lowerCAmelCase_ (     __A,  __A							)     ->				None:







      '''simple docstring'''


      UpperCAmelCase__								=  len(__A							)
      print("The following activities are selected:"							)

      # The first activity is always selected
      UpperCAmelCase__								=  0
      print(__A,  end=","							)

      # Consider rest of the activities
      for j in range(__A							):
            # If this activity has start time greater than
            # or equal to the finish time of previously
            # selected activity, then select it
            if start[j] >= finish[i]:
                  print(__A,  end=","							)
                  UpperCAmelCase__								=  j


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      UpperCamelCase__											=					[1, 3, 0, 5, 8, 5]
      UpperCamelCase__											=					[2, 4, 6, 7, 9, 9]
      print_max_activities(start, finish)



