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

   try:
      lowerCAmelCase_			=      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_			=      8

      lowerCAmelCase_			=      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_			=      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_			=      '''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 snake_case_      (					__snake_case  :							Dict	,   __snake_case  :							List[Any]=()	,   __snake_case  :							List[Any]=2)			->					List[str]:
   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_			=      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])





