





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:   Union[str, Any] )	->						Tuple:







      '''simple docstring'''






      return EnvironmentCommand()



def _a				(	lowerCamelCase:   Tuple )	->						int:







      '''simple docstring'''






      return EnvironmentCommand(args.accelerate_config_file )







class A_					(			_lowerCamelCase   ):




   @staticmethod
   def _lowerCAmelCase      (_UpperCamelCase	:ArgumentParser )->					Dict:
         __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	:Tuple    ,   _UpperCamelCase	:Dict    ,   *_UpperCamelCase	:Optional[int] )->					None:
         __A          =				accelerate_config_file




   def _lowerCAmelCase      (self	:Dict )->					List[str]:
         __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 _lowerCAmelCase      (_UpperCamelCase	:Union[str, Any] )->					List[str]:
         return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()] ) + "\n"












import os

snake_case__					:							Any													=     {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}



def _a				(	lowerCamelCase:   str )	->						int:







      '''simple docstring'''






      __A          =				0

      __A          =				0
      while index < len(lowerCamelCase ) - 1:
            __A          =				SYMBOLS[numerals[index]]
            __A          =				SYMBOLS[numerals[index + 1]]
            if current_value < next_value:
                  total_value -= current_value
            else:
                  total_value += current_value
            index += 1
      total_value += SYMBOLS[numerals[index]]

      return total_value



def _a				(	lowerCamelCase:   int )	->						str:







      '''simple docstring'''






      __A          =				''''''

      __A          =				num // 10_00
      numerals += m_count * "M"
      num %= 10_00

      __A          =				num // 1_00
      if c_count == 9:
            numerals += "CM"
            c_count -= 9
      elif c_count == 4:
            numerals += "CD"
            c_count -= 4
      if c_count >= 5:
            numerals += "D"
            c_count -= 5
      numerals += c_count * "C"
      num %= 1_00

      __A          =				num // 10
      if x_count == 9:
            numerals += "XC"
            x_count -= 9
      elif x_count == 4:
            numerals += "XL"
            x_count -= 4
      if x_count >= 5:
            numerals += "L"
            x_count -= 5
      numerals += x_count * "X"
      num %= 10

      if num == 9:
            numerals += "IX"
            num -= 9
      elif num == 4:
            numerals += "IV"
            num -= 4
      if num >= 5:
            numerals += "V"
            num -= 5
      numerals += num * "I"

      return numerals



def _a				(	lowerCamelCase:   str = "/p089_roman.txt" )	->						int:







      '''simple docstring'''






      __A          =				0

      with open(os.path.dirname(lowerCamelCase ) + roman_numerals_filename ) as filea:
            __A          =				filea.readlines()

      for line in lines:
            __A          =				line.strip()
            __A          =				parse_roman_numerals(lowerCamelCase )
            __A          =				generate_roman_numerals(lowerCamelCase )
            savings += len(lowerCamelCase ) - len(lowerCamelCase )

      return savings


if __name__ == "__main__":
       print(f'{solution() = }')





