


"""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						(				_a:     List[str]		,   _a:     Dict=()		,   _a:     List[Any]=None		,   _a:     List[Any]="no"		,   _a:     Optional[Any]="29500"							)->		Optional[int]:
      '''simple docstring'''


      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'		,   _a							) 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(_a		,   distributed_type='TPU'							)
            print(F'Launching a training on {num_processes} TPU cores.'							)
            xmp.spawn(_a		,   args=_a		,   nprocs=_a		,   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(*_a							)
      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=_a		,   master_addr='127.0.01'		,   master_port=_a		,   mixed_precision=_a							):
                        lowerCamelCase__           =		PrepareForLaunch(_a		,   distributed_type='MULTI_GPU'							)
                        print(F'Launching training on {num_processes} GPUs.'							)
                        try:
                              start_processes(_a		,   args=_a		,   nprocs=_a		,   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(*_a							)






def   snake_case						(				_a:     Tuple		,   _a:     Union[str, Any]=()		,   _a:     List[Any]=2							)->		Union[str, Any]:
      '''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=_a		,   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(_a		,   debug=_a							)
                  start_processes(_a		,   args=_a		,   nprocs=_a		,   start_method='fork'							)







"""simple docstring"""


import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    "The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
    " StableDiffusionImg2ImgPipeline` instead."
)



