





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 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:str   )      ->   Dict:
 return EnvironmentCommand()






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Optional[Any]   )      ->   List[Any]:
 return EnvironmentCommand(args.accelerate_config_file   )







class        snake_case_    (		lowerCamelCase__						):






  @staticmethod
  def 				UpperCAmelCase__						(							_snake_case			:					ArgumentParser      )->List[str]:






   '''simple docstring'''

   __lowerCAmelCase					:	str      =       parser.add_parser("""env"""      )
   download_parser.set_defaults(func=_snake_case      )
   download_parser.add_argument(
       """--accelerate-config_file"""       ,  default=_snake_case       ,  help="""The accelerate config file to use for the default values in the launching script."""       ,  )
   download_parser.set_defaults(func=_snake_case      )






  def __init__(							self			:					Union[str, Any]       ,  _snake_case			:					Dict       ,  *_snake_case			:					Dict      )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Dict      =       accelerate_config_file






  def 				UpperCAmelCase__						(							self			:					Tuple      )->Dict:






   '''simple docstring'''

   __lowerCAmelCase					:	Tuple      =       "not installed"
   if is_safetensors_available():
    import safetensors

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

    __lowerCAmelCase					:	Any      =       F'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

   __lowerCAmelCase					:	Optional[Any]      =       "not installed"
   __lowerCAmelCase					:	List[Any]      =       "not found"
   if is_accelerate_available():
    import accelerate
    from accelerate.commands.config import default_config_file, load_config_from_file

    __lowerCAmelCase					:	Optional[Any]      =       accelerate.__version__
    # Get the default from the config file.
    if self._accelerate_config_file is not None or os.path.isfile(_snake_case      ):
     __lowerCAmelCase					:	Tuple      =       load_config_from_file(self._accelerate_config_file      ).to_dict()

    __lowerCAmelCase					:	str      =       (
        "\n".join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]      )
        if isinstance(_snake_case       ,  _snake_case      )
        else F'''\t{accelerate_config}'''
    )

   __lowerCAmelCase					:	str      =       "not installed"
   __lowerCAmelCase					:	Union[str, Any]      =       "NA"
   if is_torch_available():
    import torch

    __lowerCAmelCase					:	List[Any]      =       torch.__version__
    __lowerCAmelCase					:	List[str]      =       torch.cuda.is_available()

   __lowerCAmelCase					:	Tuple      =       "not installed"
   __lowerCAmelCase					:	int      =       "NA"
   if is_tf_available():
    import tensorflow as tf

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

   __lowerCAmelCase					:	List[Any]      =       "not installed"
   __lowerCAmelCase					:	List[Any]      =       "not installed"
   __lowerCAmelCase					:	Any      =       "not installed"
   __lowerCAmelCase					:	str      =       "NA"
   if is_flax_available():
    import flax
    import jax
    import jaxlib

    __lowerCAmelCase					:	Tuple      =       flax.__version__
    __lowerCAmelCase					:	Union[str, Any]      =       jax.__version__
    __lowerCAmelCase					:	Dict      =       jaxlib.__version__
    __lowerCAmelCase					:	Tuple      =       jax.lib.xla_bridge.get_backend().platform

   __lowerCAmelCase					:	Any      =       {
       "`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(_snake_case      )      )

   return info






  @staticmethod
  def 				UpperCAmelCase__						(							_snake_case			:					Tuple      )->List[str]:






   '''simple docstring'''

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






import random






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list   ,   SCREAMING_SNAKE_CASE		:Dict   )      ->   tuple:
 __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	List[str]      =       [], [], []
 for element in data:
  if element < pivot:
   less.append(SCREAMING_SNAKE_CASE   )
  elif element > pivot:
   greater.append(SCREAMING_SNAKE_CASE   )
  else:
   equal.append(SCREAMING_SNAKE_CASE   )
 return less, equal, greater







def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list   ,   SCREAMING_SNAKE_CASE		:int   )      ->   Dict:
 # index = len(items) // 2 when trying to find the median
 #   (value of index when items is sorted)

 # invalid input
 if index >= len(SCREAMING_SNAKE_CASE   ) or index < 0:
  return None

 __lowerCAmelCase					:	Union[str, Any]      =       items[random.randint(0   ,   len(SCREAMING_SNAKE_CASE   ) - 1   )]
 __lowerCAmelCase					:	int      =       0
 __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	Tuple      =       _partition(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Optional[int]      =       len(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	List[str]      =       len(SCREAMING_SNAKE_CASE   )

 # index is the pivot
 if m <= index < m + count:
  return pivot
 # must be in smaller
 elif m > index:
  return quick_select(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 # must be in larger
 else:
  return quick_select(SCREAMING_SNAKE_CASE   ,   index - (m + count)   )