





import math




def    __UpperCAmelCase(      lowercase_ ,    lowercase_      ):
       _lowerCamelCase							:     List[str]	=		len(lowercase_      )
       _lowerCamelCase							:     Optional[int]	=		int(math.floor(math.sqrt(lowercase_      )      )      )
       _lowerCamelCase							:     Tuple	=		0
       while arr[min(lowercase_ ,    lowercase_      ) - 1] < x:
              _lowerCamelCase							:     Optional[Any]	=		step
              step += int(math.floor(math.sqrt(lowercase_      )      )      )
              if prev >= n:
                     return -1

       while arr[prev] < x:
              _lowerCamelCase							:     int	=		prev + 1
              if prev == min(lowercase_ ,    lowercase_      ):
                     return -1
       if arr[prev] == x:
              return prev
       return -1


if __name__ == "__main__":
       _lowerCamelCase			       =      input('Enter numbers separated by a comma:\n').strip()
       _lowerCamelCase			       =      [int(item) for item in user_input.split(',')]
       _lowerCamelCase			       =      int(input('Enter the number to be searched:\n'))
       _lowerCamelCase			       =      jump_search(arr, x)
       if res == -1:
              print('Number not found!')
       else:
              print(F'''Number {x} is at index {res}''')








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(      lowercase_      ):
       return EnvironmentCommand()




def    __UpperCAmelCase(      lowercase_      ):
       return EnvironmentCommand(args.accelerate_config_file      )







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      @staticmethod
      def 				__snake_case			(		a__):



             """simple docstring"""





             _lowerCamelCase							:     List[Any]	=		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__):



             """simple docstring"""





             _lowerCamelCase							:     str	=		accelerate_config_file


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		'''not installed'''
             if is_safetensors_available():
                    import safetensors

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

                    _lowerCamelCase							:     Optional[int]	=		F"""{safetensors.__version__} but is ignored because of PyTorch version too old."""

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

                    _lowerCamelCase							:     Optional[int]	=		accelerate.__version__
                    # Get the default from the config file.
                    if self._accelerate_config_file is not None or os.path.isfile(a__):
                           _lowerCamelCase							:     Optional[int]	=		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(a__      ,							a__)
                        else F"""\t{accelerate_config}"""
                    )

             _lowerCamelCase							:     List[Any]	=		'''not installed'''
             _lowerCamelCase							:     Tuple	=		'''NA'''
             if is_torch_available():
                    import torch

                    _lowerCamelCase							:     int	=		torch.__version__
                    _lowerCamelCase							:     List[str]	=		torch.cuda.is_available()

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

                    _lowerCamelCase							:     List[str]	=		tf.__version__
                    try:
                           # deprecated in v2.1
                           _lowerCamelCase							:     Optional[int]	=		tf.test.is_gpu_available()
                    except AttributeError:
                           # returns list of devices, convert to bool
                           _lowerCamelCase							:     Optional[int]	=		bool(tf.config.list_physical_devices('''GPU'''))

             _lowerCamelCase							:     str	=		'''not installed'''
             _lowerCamelCase							:     List[Any]	=		'''not installed'''
             _lowerCamelCase							:     List[Any]	=		'''not installed'''
             _lowerCamelCase							:     Optional[int]	=		'''NA'''
             if is_flax_available():
                    import flax
                    import jax
                    import jaxlib

                    _lowerCamelCase							:     Any	=		flax.__version__
                    _lowerCamelCase							:     str	=		jax.__version__
                    _lowerCamelCase							:     Any	=		jaxlib.__version__
                    _lowerCamelCase							:     int	=		jax.lib.xla_bridge.get_backend().platform

             _lowerCamelCase							:     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(a__))

             return info



      @staticmethod
      def 				__snake_case			(		a__):



             """simple docstring"""





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

