"""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     lowercase__( __SCREAMING_SNAKE_CASE							:  Tuple   ):
     return EnvironmentCommand()



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[str]   ):
     return EnvironmentCommand(args.accelerate_config_file   )







class  UpperCamelCase	(    lowercase_						):
   @staticmethod
   def     _UpperCAmelCase     (    __UpperCamelCase    )			->   Optional[int]:


        '''simple docstring'''


        lowercase_			:       int									=			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  ,__UpperCamelCase  ,*__UpperCamelCase    )			->   None:


        '''simple docstring'''


        lowercase_			:       Optional[int]									=			accelerate_config_file
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


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

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

             lowercase_			:       List[str]									=			f'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

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

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

             lowercase_			:       Optional[Any]									=			(
                 '\n'.join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]    )
                 if isinstance(__UpperCamelCase  ,__UpperCamelCase    )
                 else f'''\t{accelerate_config}'''
             )

        lowercase_			:       Dict									=			'not installed'
        lowercase_			:       Union[str, Any]									=			'NA'
        if is_torch_available():
             import torch

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

        lowercase_			:       Union[str, Any]									=			'not installed'
        lowercase_			:       Dict									=			'NA'
        if is_tf_available():
             import tensorflow as tf

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

        lowercase_			:       List[Any]									=			'not installed'
        lowercase_			:       Any									=			'not installed'
        lowercase_			:       Dict									=			'not installed'
        lowercase_			:       int									=			'NA'
        if is_flax_available():
             import flax
             import jax
             import jaxlib

             lowercase_			:       Optional[int]									=			flax.__version__
             lowercase_			:       str									=			jax.__version__
             lowercase_			:       Union[str, Any]									=			jaxlib.__version__
             lowercase_			:       List[str]									=			jax.lib.xla_bridge.get_backend().platform

        lowercase_			:       int									=			{
            '`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     _UpperCAmelCase     (    __UpperCamelCase    )			->   Any:


        '''simple docstring'''


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


"""simple docstring"""







from random import randint, random



def     lowercase__( __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  bool = False  ,    __SCREAMING_SNAKE_CASE							:  bool = False  ,    __SCREAMING_SNAKE_CASE							:  int = 5  ,    ):
     lowercase_			:       Union[str, Any]									=			[[-1] * number_of_cells]  # Create a highway without any car
     lowercase_			:       str									=			0
     lowercase_			:       List[Any]									=			max(__SCREAMING_SNAKE_CASE  ,    0   )
     while i < number_of_cells:
          lowercase_			:       Union[str, Any]									=			(
              randint(0  ,    __SCREAMING_SNAKE_CASE   ) if random_speed else initial_speed
          )  # Place the cars
          i += (
              randint(1  ,    max_speed * 2   ) if random_frequency else frequency
          )  # Arbitrary number, may need tuning
     return highway



def     lowercase__( __SCREAMING_SNAKE_CASE							:  list  ,    __SCREAMING_SNAKE_CASE							:  int   ):
     lowercase_			:       Union[str, Any]									=			0
     lowercase_			:       Dict									=			highway_now[car_index + 1 :]
     for cell in range(len(__SCREAMING_SNAKE_CASE   )   ):  # May need a better name for this
          if cells[cell] != -1:  # If the cell is not empty then
               return distance  # we have the distance we wanted
          distance += 1
     # Here if the car is near the end of the highway
     return distance + get_distance(__SCREAMING_SNAKE_CASE  ,    -1   )



def     lowercase__( __SCREAMING_SNAKE_CASE							:  list  ,    __SCREAMING_SNAKE_CASE							:  float  ,    __SCREAMING_SNAKE_CASE							:  int   ):
     lowercase_			:       List[Any]									=			len(__SCREAMING_SNAKE_CASE   )
     # Beforce calculations, the highway is empty
     lowercase_			:       Dict									=			[-1] * number_of_cells

     for car_index in range(__SCREAMING_SNAKE_CASE   ):
          if highway_now[car_index] != -1:
               # Add 1 to the current speed of the car and cap the speed
               lowercase_			:       Any									=			min(highway_now[car_index] + 1  ,    __SCREAMING_SNAKE_CASE   )
               # Number of empty cell before the next car
               lowercase_			:       List[Any]									=			get_distance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ) - 1
               # We can't have the car causing an accident
               lowercase_			:       Dict									=			min(next_highway[car_index]  ,    __SCREAMING_SNAKE_CASE   )
               if random() < probability:
                    # Randomly, a driver will slow down
                    lowercase_			:       Optional[Any]									=			max(next_highway[car_index] - 1  ,    0   )
     return next_highway



def     lowercase__( __SCREAMING_SNAKE_CASE							:  list  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  float  ,    __SCREAMING_SNAKE_CASE							:  int   ):
     lowercase_			:       Union[str, Any]									=			len(highway[0]   )

     for i in range(__SCREAMING_SNAKE_CASE   ):
          lowercase_			:       Optional[Any]									=			update(highway[i]  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
          lowercase_			:       List[str]									=			[-1] * number_of_cells

          for car_index in range(__SCREAMING_SNAKE_CASE   ):
               lowercase_			:       Optional[Any]									=			next_speeds_calculated[car_index]
               if speed != -1:
                    # Change the position based on the speed (with % to create the loop)
                    lowercase_			:       Any									=			(car_index + speed) % number_of_cells
                    # Commit the change of position
                    lowercase_			:       Optional[Any]									=			speed
          highway.append(__SCREAMING_SNAKE_CASE   )

     return highway


if __name__ == "__main__":
       import doctest

       doctest.testmod()

