




import os
import sys
import tempfile

import torch

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







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_=()			,    lowercase_=None			,    lowercase_="no"			,    lowercase_="29500"						)       ->	Any:



       """simple docstring"""

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

       try:
              A__							=   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'''			,    _lowercase						) 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:
                     A__							=   8

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







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_=()			,    lowercase_=2						)       ->	Dict:



       """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=_lowercase			,    master_addr='''127.0.01'''			,    master_port='''29500'''			,    accelerate_mixed_precision='''no'''			,    accelerate_debug_rdv_file=tmp_file.name			,    accelerate_use_cpu='''yes'''			,    ):
                     A__							=   PrepareForLaunch(_lowercase			,    debug=_lowercase						)
                     start_processes(_lowercase			,    args=_lowercase			,    nprocs=_lowercase			,    start_method='''fork'''						)







def SCREAMING_SNAKE_CASE    (	lowercase_ = 50						)       ->	int:



       """simple docstring"""

       A__							=   [1] * (length + 1)

       for row_length in range(3			,    length + 1						):
              for block_length in range(3			,    row_length + 1						):
                     for block_start in range(row_length - block_length						):
                            ways_number[row_length] += ways_number[
                                row_length - block_start - block_length - 1
                            ]

                     ways_number[row_length] += 1

       return ways_number[length]


if __name__ == "__main__":
  print(F'''{solution() = }''')


