


'''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_  (       A      ):
   '''simple docstring'''





   return EnvironmentCommand()


def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





   return EnvironmentCommand(args.accelerate_config_file      )







class 					a			(				snake_case__  ):
 '''simple docstring'''




 @staticmethod
 def   __UpperCamelCase   (    lowerCamelCase_      )      ->    int:
    _a					:  Union[str, Any]						=  parser.add_parser('env'      )
    download_parser.set_defaults(func=lowerCamelCase_      )
    download_parser.add_argument(
        '--accelerate-config_file'		,      default=lowerCamelCase_		,      help='The accelerate config file to use for the default values in the launching script.'		,      )
    download_parser.set_defaults(func=lowerCamelCase_      )




 def __init__(    self		,      lowerCamelCase_		,      *lowerCamelCase_      )      ->    None:
    _a					:  str						=  accelerate_config_file




 def   __UpperCamelCase   (    self      )      ->    Tuple:
    _a					:  Any						=  'not installed'
    if is_safetensors_available():
       import safetensors

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

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

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

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

       _a					:  Union[str, Any]						=  (
           '\n'.join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]      )
           if isinstance(lowerCamelCase_		,      lowerCamelCase_      )
           else F'''\t{accelerate_config}'''
       )

    _a					:  int						=  'not installed'
    _a					:  Optional[int]						=  'NA'
    if is_torch_available():
       import torch

       _a					:  Tuple						=  torch.__version__
       _a					:  Optional[int]						=  torch.cuda.is_available()

    _a					:  Optional[int]						=  'not installed'
    _a					:  List[str]						=  'NA'
    if is_tf_available():
       import tensorflow as tf

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

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

       _a					:  Dict						=  flax.__version__
       _a					:  List[Any]						=  jax.__version__
       _a					:  Any						=  jaxlib.__version__
       _a					:  int						=  jax.lib.xla_bridge.get_backend().platform

    _a					:  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(lowerCamelCase_      )      )

    return info




 @staticmethod
 def   __UpperCamelCase   (    lowerCamelCase_      )      ->    Any:
    return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]      ) + "\n"






'''simple docstring'''




import mpmath  # for roots of unity
import numpy as np







class 					a			:
 '''simple docstring'''




 def __init__(    self		,      lowerCamelCase_=None		,      lowerCamelCase_=None      )      ->    Tuple:
    # Input as list
    _a					:  Optional[int]						=  list(poly_a or [0]      )[:]
    _a					:  List[str]						=  list(poly_b or [0]      )[:]

    # Remove leading zero coefficients
    while self.polyA[-1] == 0:
       self.polyA.pop()
    _a					:  str						=  len(self.polyA      )

    while self.polyB[-1] == 0:
       self.polyB.pop()
    _a					:  Optional[int]						=  len(self.polyB      )

    # Add 0 to make lengths equal a power of 2
    _a					:  str						=  int(
        2 ** np.ceil(np.loga(len(self.polyA      ) + len(self.polyB      ) - 1      )      )      )

    while len(self.polyA      ) < self.c_max_length:
       self.polyA.append(0      )
    while len(self.polyB      ) < self.c_max_length:
       self.polyB.append(0      )
    # A complex root used for the fourier transform
    _a					:  Optional[int]						=  complex(mpmath.root(x=1		,      n=self.c_max_length		,      k=1      )      )

    # The product
    _a					:  Union[str, Any]						=  self.__multiply()




 def   __UpperCamelCase   (    self		,      lowerCamelCase_      )      ->    Dict:
    _a					:  Dict						=  [[x] for x in self.polyA] if which == 'A' else [[x] for x in self.polyB]
    # Corner case
    if len(lowerCamelCase_      ) <= 1:
       return dft[0]
    #
    _a					:  List[str]						=  self.c_max_length // 2
    while next_ncol > 0:
       _a					:  Tuple						=  [[] for i in range(lowerCamelCase_      )]
       _a					:  Tuple						=  self.root**next_ncol

       # First half of next step
       _a					:  Optional[Any]						=  1
       for j in range(self.c_max_length // (next_ncol * 2)      ):
          for i in range(lowerCamelCase_      ):
             new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]      )
          current_root *= root
       # Second half of next step
       _a					:  int						=  1
       for j in range(self.c_max_length // (next_ncol * 2)      ):
          for i in range(lowerCamelCase_      ):
             new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]      )
          current_root *= root
       # Update
       _a					:  Union[str, Any]						=  new_dft
       _a					:  List[Any]						=  next_ncol // 2
    return dft[0]




 def   __UpperCamelCase   (    self      )      ->    List[Any]:
    _a					:  Tuple						=  self.__dft('A'      )
    _a					:  Union[str, Any]						=  self.__dft('B'      )
    _a					:  List[Any]						=  [[dft_a[i] * dft_b[i] for i in range(self.c_max_length      )]]
    del dft_a
    del dft_b

    # Corner Case
    if len(inverce_c[0]      ) <= 1:
       return inverce_c[0]
    # Inverse DFT
    _a					:  Optional[Any]						=  2
    while next_ncol <= self.c_max_length:
       _a					:  Optional[int]						=  [[] for i in range(lowerCamelCase_      )]
       _a					:  List[str]						=  self.root ** (next_ncol // 2)
       _a					:  int						=  1
       # First half of next step
       for j in range(self.c_max_length // next_ncol      ):
          for i in range(next_ncol // 2      ):
             # Even positions
             new_inverse_c[i].append(
                 (
                     inverce_c[i][j]
                     + inverce_c[i][j + self.c_max_length // next_ncol]
                 )
                 / 2      )
             # Odd positions
             new_inverse_c[i + next_ncol // 2].append(
                 (
                     inverce_c[i][j]
                     - inverce_c[i][j + self.c_max_length // next_ncol]
                 )
                 / (2 * current_root)      )
          current_root *= root
       # Update
       _a					:  Dict						=  new_inverse_c
       next_ncol *= 2
    # Unpack
    _a					:  Union[str, Any]						=  [round(x[0].real		,      8      ) + round(x[0].imag		,      8      ) * 1J for x in inverce_c]

    # Remove leading 0's
    while inverce_c[-1] == 0:
       inverce_c.pop()
    return inverce_c




 def __str__(    self      )      ->    Dict:
    _a					:  Optional[int]						=  'A = ' + ' + '.join(
        F'''{coef}*x^{i}''' for coef, i in enumerate(self.polyA[: self.len_A]      )      )
    _a					:  Dict						=  'B = ' + ' + '.join(
        F'''{coef}*x^{i}''' for coef, i in enumerate(self.polyB[: self.len_B]      )      )
    _a					:  Tuple						=  'A*B = ' + ' + '.join(
        F'''{coef}*x^{i}''' for coef, i in enumerate(self.product      )      )

    return F'''{a}\n{b}\n{c}'''


# Unit tests
if __name__ == "__main__":
 import doctest

 doctest.testmod()


