




'''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      UpperCamelCase_(	snake_case							:       List[Any]     ):

							'''simple docstring'''






							return EnvironmentCommand()


def      UpperCamelCase_(	snake_case							:       Tuple     ):

							'''simple docstring'''






							return EnvironmentCommand(args.accelerate_config_file     )




class  _snake_case		(	lowercase_							):




			@staticmethod
			def    lowerCAmelCase__				(					a__							)				->				List[str]:

										'''simple docstring'''




										snake_case_						=      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				,							a__				,							*a__							)				->				None:

										'''simple docstring'''




										snake_case_						=      accelerate_config_file




			def    lowerCAmelCase__				(					self							)				->				str:

										'''simple docstring'''




										snake_case_						=      "not installed"
										if is_safetensors_available():
																	import safetensors

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

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

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

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

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

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

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

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

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

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

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

										snake_case_						=      {
										    "`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    lowerCAmelCase__				(					a__							)				->				Optional[int]:

										'''simple docstring'''




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







import math

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    int			):



     '''simple docstring'''





     lowercase__    :   Optional[Any]      =       []
     lowercase__    :   str      =       2
     lowercase__    :   Optional[Any]      =       int(math.sqrt(SCREAMING_SNAKE_CASE_			)			)  # Size of every segment
     lowercase__    :   Dict      =       [True] * (end + 1)
     lowercase__    :   Union[str, Any]      =       []

     while start <= end:
          if temp[start] is True:
               in_prime.append(SCREAMING_SNAKE_CASE_			)
               for i in range(start * start	,     end + 1	,     SCREAMING_SNAKE_CASE_			):
                    lowercase__    :   int      =       False
          start += 1
     prime += in_prime

     lowercase__    :   Optional[int]      =       end + 1
     lowercase__    :   List[str]      =       min(2 * end	,     SCREAMING_SNAKE_CASE_			)

     while low <= n:
          lowercase__    :   str      =       [True] * (high - low + 1)
          for each in in_prime:
               lowercase__    :   str      =       math.floor(low / each			) * each
               if t < low:
                    t += each

               for j in range(SCREAMING_SNAKE_CASE_	,     high + 1	,     SCREAMING_SNAKE_CASE_			):
                    lowercase__    :   Optional[Any]      =       False

          for j in range(len(SCREAMING_SNAKE_CASE_			)			):
               if temp[j] is True:
                    prime.append(j + low			)

          lowercase__    :   Optional[Any]      =       high + 1
          lowercase__    :   Optional[int]      =       min(high + end	,     SCREAMING_SNAKE_CASE_			)

     return prime

print(sieve(10**6))

