


'''simple docstring'''







import os
import sys
import tempfile

import torch

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






def       lowercase					(_A ,    _A=() ,    _A=None ,    _A="no" ,    _A="29500"       ):







       """simple docstring"""







       _lowerCAmelCase     :				Dict       =							False
       _lowerCAmelCase     :				int       =							False
       if any(key.startswith('KAGGLE'       ) for key in os.environ.keys()       ):
              _lowerCAmelCase     :				str       =							True
       elif "IPython" in sys.modules:
              _lowerCAmelCase     :				Tuple       =							'google.colab' in str(sys.modules['IPython'].get_ipython()       )

       try:
              _lowerCAmelCase     :				str       =							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' ,    __snake_case       ) 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:
                     _lowerCAmelCase     :				Dict       =							8

              _lowerCAmelCase     :				Union[str, Any]       =							PrepareForLaunch(__snake_case ,    distributed_type='TPU'       )
              print(f'Launching a training on {num_processes} TPU cores.'       )
              xmp.spawn(__snake_case ,    args=__snake_case ,    nprocs=__snake_case ,    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(*__snake_case       )
       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=__snake_case ,    master_addr='127.0.01' ,    master_port=__snake_case ,    mixed_precision=__snake_case       ):
                            _lowerCAmelCase     :				Optional[Any]       =							PrepareForLaunch(__snake_case ,    distributed_type='MULTI_GPU'       )
                            print(f'Launching training on {num_processes} GPUs.'       )
                            try:
                                   start_processes(__snake_case ,    args=__snake_case ,    nprocs=__snake_case ,    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():
                            _lowerCAmelCase     :				Union[str, Any]       =							'1'
                            print('Launching training on MPS.'       )
                     elif torch.cuda.is_available():
                            print('Launching training on one GPU.'       )
                     else:
                            print('Launching training on CPU.'       )
                     function(*__snake_case       )






def       lowercase					(_A ,    _A=() ,    _A=2       ):







       """simple docstring"""







       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=__snake_case ,    master_addr='127.0.01' ,    master_port='29500' ,    accelerate_mixed_precision='no' ,    accelerate_debug_rdv_file=tmp_file.name ,    accelerate_use_cpu='yes' ,    ):
                     _lowerCAmelCase     :				List[str]       =							PrepareForLaunch(__snake_case ,    debug=__snake_case       )
                     start_processes(__snake_case ,    args=__snake_case ,    nprocs=__snake_case ,    start_method='fork'       )


'''simple docstring'''

import math
import sys



def snake_case_      (					__snake_case  :							str)			->					str:
   lowerCAmelCase_			=      ''''''
   try:
      with open(__snake_case	,   '''rb''') as binary_file:
         lowerCAmelCase_			=      binary_file.read()
      for dat in data:
         lowerCAmelCase_			=      F'''{dat:08b}'''
         result += curr_byte
      return result
   except OSError:
      print('''File not accessible''')
      sys.exit()



def snake_case_      (					__snake_case  :							str)			->					str:
   lowerCAmelCase_			=      {'''0''': '''0''', '''1''': '''1'''}
   lowerCAmelCase_	,lowerCAmelCase_			=      '''''', ''''''
   lowerCAmelCase_			=      len(__snake_case)

   for i in range(len(__snake_case)):
      curr_string += data_bits[i]
      if curr_string not in lexicon:
         continue

      lowerCAmelCase_			=      lexicon[curr_string]
      result += last_match_id
      lowerCAmelCase_			=      last_match_id + '''0'''

      if math.loga(__snake_case).is_integer():
         lowerCAmelCase_			=      {}
         for curr_key in list(__snake_case):
            lowerCAmelCase_			=      lexicon.pop(__snake_case)
         lowerCAmelCase_			=      new_lex

      lowerCAmelCase_			=      last_match_id + '''1'''
      index += 1
      lowerCAmelCase_			=      ''''''
   return result



def snake_case_      (					__snake_case  :							str	,   __snake_case  :							str)			->					None:
   lowerCAmelCase_			=      8
   try:
      with open(__snake_case	,   '''wb''') as opened_file:
         lowerCAmelCase_			=      [
             to_write[i : i + byte_length]
             for i in range(0	,   len(__snake_case)	,   __snake_case)
         ]

         if len(result_byte_array[-1]) % byte_length == 0:
            result_byte_array.append('''10000000''')
         else:
            result_byte_array[-1] += "1" + "0" * (
                byte_length - len(result_byte_array[-1]) - 1
            )

         for elem in result_byte_array[:-1]:
            opened_file.write(int(__snake_case	,   2).to_bytes(1	,   byteorder='''big'''))
   except OSError:
      print('''File not accessible''')
      sys.exit()



def snake_case_      (					__snake_case  :							str)			->					str:
   lowerCAmelCase_			=      0
   for letter in data_bits:
      if letter == "1":
         break
      counter += 1

   lowerCAmelCase_			=      data_bits[counter:]
   lowerCAmelCase_			=      data_bits[counter + 1 :]
   return data_bits



def snake_case_      (					__snake_case  :							str	,   __snake_case  :							str)			->					None:
   lowerCAmelCase_			=      read_file_binary(__snake_case)
   lowerCAmelCase_			=      remove_prefix(__snake_case)
   lowerCAmelCase_			=      decompress_data(__snake_case)
   write_file_binary(__snake_case	,   __snake_case)


if __name__ == "__main__":
 compress(sys.argv[1], sys.argv[2])





