





_UpperCAmelCase        =				'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Dict:
       # Make sure the supplied data is a bytes-like object
       if not isinstance(_lowerCAmelCase					,		_lowerCAmelCase  ):
              UpperCamelCase_          =  F'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
              raise TypeError(_lowerCAmelCase  )

       UpperCamelCase_          =  "".join(bin(_lowerCAmelCase  )[2:].zfill(8  ) for byte in data  )

       UpperCamelCase_          =  len(_lowerCAmelCase  ) % 6 != 0

       if padding_needed:
              # The padding that will be added later
              UpperCamelCase_          =  b"=" * ((6 - len(_lowerCAmelCase  ) % 6) // 2)

              # Append binary_stream with arbitrary binary digits (0's by default) to make its
              # length a multiple of 6.
              binary_stream += "0" * (6 - len(_lowerCAmelCase  ) % 6)
       else:
              UpperCamelCase_          =  b""

       # Encode every 6 binary digits to their corresponding Base64 character
       return (
           "".join(
               B64_CHARSET[int(binary_stream[index : index + 6]					,		2  )]
               for index in range(0					,		len(_lowerCAmelCase  )					,		6  )  ).encode()
           + padding
       )




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		List[Any]:
       # Make sure encoded_data is either a string or a bytes-like object
       if not isinstance(_lowerCAmelCase					,		_lowerCAmelCase  ) and not isinstance(_lowerCAmelCase					,		_lowerCAmelCase  ):
              UpperCamelCase_          =  (
                  "argument should be a bytes-like object or ASCII string, "
                  F'''not \'{encoded_data.__class__.__name__}\''''
              )
              raise TypeError(_lowerCAmelCase  )

       # In case encoded_data is a bytes-like object, make sure it contains only
       # ASCII characters so we convert it to a string object
       if isinstance(_lowerCAmelCase					,		_lowerCAmelCase  ):
              try:
                     UpperCamelCase_          =  encoded_data.decode("utf-8"  )
              except UnicodeDecodeError:
                     raise ValueError("base64 encoded data should only contain ASCII characters"  )

       UpperCamelCase_          =  encoded_data.count("="  )

       # Check if the encoded string contains non base64 characters
       if padding:
              assert all(
                  char in B64_CHARSET for char in encoded_data[:-padding]  ), "Invalid base64 character(s) found."
       else:
              assert all(
                  char in B64_CHARSET for char in encoded_data  ), "Invalid base64 character(s) found."

       # Check the padding
       assert len(_lowerCAmelCase  ) % 4 == 0 and padding < 3, "Incorrect padding"

       if padding:
              # Remove padding if there is one
              UpperCamelCase_          =  encoded_data[:-padding]

              UpperCamelCase_          =  "".join(
                  bin(B64_CHARSET.index(_lowerCAmelCase  )  )[2:].zfill(6  ) for char in encoded_data  )[: -padding * 2]
       else:
              UpperCamelCase_          =  "".join(
                  bin(B64_CHARSET.index(_lowerCAmelCase  )  )[2:].zfill(6  ) for char in encoded_data  )

       UpperCamelCase_          =  [
           int(binary_stream[index : index + 8]					,		2  )
           for index in range(0					,		len(_lowerCAmelCase  )					,		8  )
       ]

       return bytes(_lowerCAmelCase  )


if __name__ == "__main__":
       import doctest

       doctest.testmod()








import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_=()					,		UpperCamelCase_=None					,		UpperCamelCase_="no"					,		UpperCamelCase_="29500"  )     ->		Optional[Any]:
       UpperCamelCase_          =  False
       UpperCamelCase_          =  False
       if any(key.startswith("KAGGLE"  ) for key in os.environ.keys()  ):
              UpperCamelCase_          =  True
       elif "IPython" in sys.modules:
              UpperCamelCase_          =  "google.colab" in str(sys.modules["IPython"].get_ipython()  )

       try:
              UpperCamelCase_          =  PrecisionType(mixed_precision.lower()  )
       except ValueError:
              raise ValueError(
                  F'''Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'''  )

       if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME"					,		UpperCamelCase_  ) is not None):
              # TPU launch
              import torch_xla.distributed.xla_multiprocessing as xmp

              if len(AcceleratorState._shared_state  ) > 0:
                     raise ValueError(
                         "To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
                         "your training function. Restart your notebook and make sure no cells initializes an "
                         "`Accelerator`."  )
              if num_processes is None:
                     UpperCamelCase_          =  8

              UpperCamelCase_          =  PrepareForLaunch(UpperCamelCase_					,		distributed_type="TPU"  )
              print(F'''Launching a training on {num_processes} TPU cores.'''  )
              xmp.spawn(UpperCamelCase_					,		args=UpperCamelCase_					,		nprocs=UpperCamelCase_					,		start_method="fork"  )
       elif in_colab:
              # No need for a distributed launch otherwise as it's either CPU or one GPU.
              if torch.cuda.is_available():
                     print("Launching training on one GPU."  )
              else:
                     print("Launching training on one CPU."  )
              function(*UpperCamelCase_  )
       else:
              if num_processes is None:
                     raise ValueError(
                         "You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."  )

              if num_processes > 1:
                     # Multi-GPU launch
                     from torch.multiprocessing import start_processes
                     from torch.multiprocessing.spawn import ProcessRaisedException

                     if len(AcceleratorState._shared_state  ) > 0:
                            raise ValueError(
                                "To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
                                "inside your training function. Restart your notebook and make sure no cells initializes an "
                                "`Accelerator`."  )

                     if torch.cuda.is_initialized():
                            raise ValueError(
                                "To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
                                "using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
                                "function."  )

                     # torch.distributed will expect a few environment variable to be here. We set the ones common to each
                     # process here (the other ones will be set be the launcher).
                     with patch_environment(
                         world_size=UpperCamelCase_					,		master_addr="127.0.01"					,		master_port=UpperCamelCase_					,		mixed_precision=UpperCamelCase_  ):
                            UpperCamelCase_          =  PrepareForLaunch(UpperCamelCase_					,		distributed_type="MULTI_GPU"  )
                            print(F'''Launching training on {num_processes} GPUs.'''  )
                            try:
                                   start_processes(UpperCamelCase_					,		args=UpperCamelCase_					,		nprocs=UpperCamelCase_					,		start_method="fork"  )
                            except ProcessRaisedException as e:
                                   if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
                                          raise RuntimeError(
                                              "CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
                                              "This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
                                              "Please review your imports and test them when running the `notebook_launcher()` to identify "
                                              "which one is problematic."  ) from e

              else:
                     # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
                     if is_mps_available():
                            UpperCamelCase_          =  "1"
                            print("Launching training on MPS."  )
                     elif torch.cuda.is_available():
                            print("Launching training on one GPU."  )
                     else:
                            print("Launching training on CPU."  )
                     function(*UpperCamelCase_  )







def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_=()					,		UpperCamelCase_=2  )     ->		Optional[Any]:
       from torch.multiprocessing import start_processes

       with tempfile.NamedTemporaryFile() as tmp_file:
              # torch.distributed will expect a few environment variable to be here. We set the ones common to each
              # process here (the other ones will be set be the launcher).
              with patch_environment(
                  world_size=UpperCamelCase_					,		master_addr="127.0.01"					,		master_port="29500"					,		accelerate_mixed_precision="no"					,		accelerate_debug_rdv_file=tmp_file.name					,		accelerate_use_cpu="yes"					,		):
                     UpperCamelCase_          =  PrepareForLaunch(UpperCamelCase_					,		debug=UpperCamelCase_  )
                     start_processes(UpperCamelCase_					,		args=UpperCamelCase_					,		nprocs=UpperCamelCase_					,		start_method="fork"  )

