






"""simple docstring"""





from __future__ import annotations

from collections.abc import Callable

UpperCamelCase_	:				Tuple										= list[list[float | int]]



def     __lowercase					(    a :				Matrix       ,		a :				Matrix )					->					Matrix:
      __snake_case      :    int  			=len(a )
      __snake_case      :    Matrix  			=[[0 for _ in range(size + 1 )] for _ in range(a )]
      __snake_case      :    int
      __snake_case      :    int
      __snake_case      :    int
      __snake_case      :    int
      __snake_case      :    int
      __snake_case      :    float

      for row in range(a ):
            for col in range(a ):
                  __snake_case      :    Optional[Any]  			=matrix[row][col]

            __snake_case      :    List[str]  			=vector[row][0]

      __snake_case      :    Optional[int]  			=0
      __snake_case      :    Union[str, Any]  			=0
      while row < size and col < size:
            # pivoting
            __snake_case      :    List[str]  			=max((abs(augmented[rowa][col] ), rowa) for rowa in range(a       ,		a ) )[
                1
            ]
            if augmented[pivot_row][col] == 0:
                  col += 1
                  continue
            else:
                  __snake_case			,				__snake_case      :    Optional[Any]  			=augmented[pivot_row], augmented[row]

            for rowa in range(row + 1       ,		a ):
                  __snake_case      :    List[Any]  			=augmented[rowa][col] / augmented[row][col]
                  __snake_case      :    List[str]  			=0
                  for cola in range(col + 1       ,		size + 1 ):
                        augmented[rowa][cola] -= augmented[row][cola] * ratio

            row += 1
            col += 1

      # back substitution
      for col in range(1       ,		a ):
            for row in range(a ):
                  __snake_case      :    List[Any]  			=augmented[row][col] / augmented[col][col]
                  for cola in range(a       ,		size + 1 ):
                        augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
      return [
          [round(augmented[row][size] / augmented[row][row]       ,		10 )] for row in range(a )
      ]



def     __lowercase					(    a :				list[int] )					->					Callable[[int], int]:
      __snake_case      :    int  			=len(a )
      __snake_case      :    Matrix  			=[[0 for _ in range(a )] for _ in range(a )]
      __snake_case      :    Matrix  			=[[0] for _ in range(a )]
      __snake_case      :    Matrix
      __snake_case      :    int
      __snake_case      :    int
      __snake_case      :    int

      for x_val, y_val in enumerate(a ):
            for col in range(a ):
                  __snake_case      :    List[Any]  			=(x_val + 1) ** (size - col - 1)
            __snake_case      :    Optional[int]  			=y_val

      __snake_case      :    Dict  			=solve(a       ,		a )

      def interpolated_func(a :				int ) -> int:
            return sum(
                round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
                for x_val in range(a ) )

      return interpolated_func



def     __lowercase					(    a :				int )					->					int:
      return (
          1
          - variable
          + variable**2
          - variable**3
          + variable**4
          - variable**5
          + variable**6
          - variable**7
          + variable**8
          - variable**9
          + variable**10
      )



def     __lowercase					(    a :				Callable[[int], int] = question_function       ,		a :				int = 10 )					->					int:
      __snake_case      :    list[int]  			=[func(a ) for x_val in range(1       ,		order + 1 )]

      __snake_case      :    list[Callable[[int], int]]  			=[
          interpolate(data_points[:max_coeff] ) for max_coeff in range(1       ,		order + 1 )
      ]

      __snake_case      :    int  			=0
      __snake_case      :    Callable[[int], int]
      __snake_case      :    int

      for poly in polynomials:
            __snake_case      :    Optional[Any]  			=1
            while func(a ) == poly(a ):
                  x_val += 1

            ret += poly(a )

      return ret


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










"""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					(    a :				Dict )					->					str:
      return EnvironmentCommand()



def     __lowercase					(    a :				Optional[int] )					->					List[Any]:
      return EnvironmentCommand(args.accelerate_config_file )


class      _lowercase (     lowerCAmelCase						):


      @staticmethod
      def _UpperCamelCase						(    a				:	ArgumentParser						):





            """simple docstring"""







            __snake_case      :    Optional[int]  			=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				:	Any							,   a				:	str							,   *a				:	Any						):





            """simple docstring"""







            __snake_case      :    int  			=accelerate_config_file


      def _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







            __snake_case      :    List[str]  			='''not installed'''
            if is_safetensors_available():
                  import safetensors

                  __snake_case      :    int  			=safetensors.__version__
            elif importlib.util.find_spec('''safetensors'''						) is not None:
                  import safetensors

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

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

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

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

            __snake_case      :    int  			='''not installed'''
            __snake_case      :    Any  			='''NA'''
            if is_torch_available():
                  import torch

                  __snake_case      :    Tuple  			=torch.__version__
                  __snake_case      :    Optional[Any]  			=torch.cuda.is_available()

            __snake_case      :    Optional[Any]  			='''not installed'''
            __snake_case      :    Any  			='''NA'''
            if is_tf_available():
                  import tensorflow as tf

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

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

                  __snake_case      :    Tuple  			=flax.__version__
                  __snake_case      :    Optional[Any]  			=jax.__version__
                  __snake_case      :    str  			=jaxlib.__version__
                  __snake_case      :    Tuple  			=jax.lib.xla_bridge.get_backend().platform

            __snake_case      :    List[str]  			={
                '''`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 _UpperCamelCase						(    a				:	List[str]						):





            """simple docstring"""







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


