




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__     (   lowerCamelCase      ):
  return EnvironmentCommand()



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  return EnvironmentCommand(args.accelerate_config_file      )







class       __lowerCAmelCase				(							lowerCAmelCase):






       @staticmethod
       def     SCREAMING_SNAKE_CASE			(      _lowerCAmelCase:    ArgumentParser ):
         lowercase			:List[str]							=  parser.add_parser("env" )
         download_parser.set_defaults(func=__SCREAMING_SNAKE_CASE )
         download_parser.add_argument(
             "--accelerate-config_file"				,    default=__SCREAMING_SNAKE_CASE				,    help="The accelerate config file to use for the default values in the launching script."				,    )
         download_parser.set_defaults(func=__SCREAMING_SNAKE_CASE )






       def __init__(      self:    List[str]				,    _lowerCAmelCase:    Tuple				,    *_lowerCAmelCase:    int ):
         lowercase			:List[str]							=  accelerate_config_file






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[Any] ):
         lowercase			:Any							=  "not installed"
         if is_safetensors_available():
           import safetensors

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

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

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

           lowercase			:Tuple							=  accelerate.__version__
           # Get the default from the config file.
           if self._accelerate_config_file is not None or os.path.isfile(__SCREAMING_SNAKE_CASE ):
             lowercase			:List[Any]							=  load_config_from_file(self._accelerate_config_file ).to_dict()

           lowercase			:Optional[int]							=  (
               "\n".join([F"\t- {prop}: {val}" for prop, val in accelerate_config.items()] )
               if isinstance(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )
               else F"\t{accelerate_config}"
           )

         lowercase			:List[str]							=  "not installed"
         lowercase			:str							=  "NA"
         if is_torch_available():
           import torch

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

         lowercase			:Any							=  "not installed"
         lowercase			:int							=  "NA"
         if is_tf_available():
           import tensorflow as tf

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

         lowercase			:Tuple							=  "not installed"
         lowercase			:int							=  "not installed"
         lowercase			:int							=  "not installed"
         lowercase			:Any							=  "NA"
         if is_flax_available():
           import flax
           import jax
           import jaxlib

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

         lowercase			:Tuple							=  {
             "`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(__SCREAMING_SNAKE_CASE ) )

         return info






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






'''simple docstring'''



import numpy as np







def    a__       (  a__      ,    a__      ,    a__ = 1E-1_2      ,    a__ = 1_00      ,    ):




     """simple docstring"""


     assert np.shape(a__						)[0] == np.shape(a__						)[1]
     # Ensure proper dimensionality.
     assert np.shape(a__						)[0] == np.shape(a__						)[0]
     # Ensure inputs are either both complex or both real
     assert np.iscomplexobj(a__						) == np.iscomplexobj(a__						)
     __SCREAMING_SNAKE_CASE     =      np.iscomplexobj(a__						)
     if is_complex:
          # Ensure complex input_matrix is Hermitian
          assert np.array_equal(a__      ,    input_matrix.conj().T						)

     # Set convergence to False. Will define convergence when we exceed max_iterations
     # or when we have small changes from one iteration to next.

     __SCREAMING_SNAKE_CASE     =      False
     __SCREAMING_SNAKE_CASE     =      0
     __SCREAMING_SNAKE_CASE     =      0
     __SCREAMING_SNAKE_CASE     =      1E1_2

     while not convergence:
          # Multiple matrix by the vector.
          __SCREAMING_SNAKE_CASE     =      np.dot(a__      ,    a__						)
          # Normalize the resulting output vector.
          __SCREAMING_SNAKE_CASE     =      w / np.linalg.norm(a__						)
          # Find rayleigh quotient
          # (faster than usual b/c we know vector is normalized already)
          __SCREAMING_SNAKE_CASE     =      vector.conj().T if is_complex else vector.T
          __SCREAMING_SNAKE_CASE     =      np.dot(a__      ,    np.dot(a__      ,    a__						)						)

          # Check convergence.
          __SCREAMING_SNAKE_CASE     =      np.abs(lambda_ - lambda_previous						) / lambda_
          iterations += 1

          if error <= error_tol or iterations >= max_iterations:
               __SCREAMING_SNAKE_CASE     =      True

          __SCREAMING_SNAKE_CASE     =      lambda_

     if is_complex:
          __SCREAMING_SNAKE_CASE     =      np.real(lambda_						)

     return lambda_, vector







def    a__       (  ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]						)
     __SCREAMING_SNAKE_CASE     =      np.array([41, 4, 20]						)
     __SCREAMING_SNAKE_CASE     =      real_input_matrix.astype(np.complexaaa						)
     __SCREAMING_SNAKE_CASE     =      np.triu(1J * complex_input_matrix      ,    1						)
     complex_input_matrix += imag_matrix
     complex_input_matrix += -1 * imag_matrix.T
     __SCREAMING_SNAKE_CASE     =      np.array([41, 4, 20]						).astype(np.complexaaa						)

     for problem_type in ["real", "complex"]:
          if problem_type == "real":
               __SCREAMING_SNAKE_CASE     =      real_input_matrix
               __SCREAMING_SNAKE_CASE     =      real_vector
          elif problem_type == "complex":
               __SCREAMING_SNAKE_CASE     =      complex_input_matrix
               __SCREAMING_SNAKE_CASE     =      complex_vector

          # Our implementation.
          __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      power_iteration(a__      ,    a__						)

          # Numpy implementation.

          # Get eigenvalues and eigenvectors using built-in numpy
          # eigh (eigh used for symmetric or hermetian matrices).
          __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      np.linalg.eigh(a__						)
          # Last eigenvalue is the maximum one.
          __SCREAMING_SNAKE_CASE     =      eigen_values[-1]
          # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
          __SCREAMING_SNAKE_CASE     =      eigen_vectors[:, -1]

          # Check our implementation and numpy gives close answers.
          assert np.abs(eigen_value - eigen_value_max						) <= 1E-6
          # Take absolute values element wise of each eigenvector.
          # as they are only unique to a minus sign.
          assert np.linalg.norm(np.abs(a__						) - np.abs(a__						)						) <= 1E-6


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  test_power_iteration()






