




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      :			List[str]      ,				lowercase      :			List[Any]=()      ,				lowercase      :			Any=None      ,				lowercase      :			str="no"      ,				lowercase      :			int="29500"	):


      '''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'      ,				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:
                  lowerCamelCase_							=       8

            lowerCamelCase_							=       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	):
                        lowerCamelCase_							=       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():
                        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(*lowercase	)






def      _SCREAMING_SNAKE_CASE     (				lowercase      :			List[str]      ,				lowercase      :			Tuple=()      ,				lowercase      :			Any=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=lowercase      ,				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(lowercase      ,				debug=lowercase	)
                  start_processes(lowercase      ,				args=lowercase      ,				nprocs=lowercase      ,				start_method='fork'	)







'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCAmelCase     :  int											=    {
    """configuration_owlvit""": [
        """OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """OwlViTConfig""",
        """OwlViTOnnxConfig""",
        """OwlViTTextConfig""",
        """OwlViTVisionConfig""",
    ],
    """processing_owlvit""": ["""OwlViTProcessor"""],
}


try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Dict											=    ["""OwlViTFeatureExtractor"""]
       lowerCAmelCase     :  int											=    ["""OwlViTImageProcessor"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Optional[Any]											=    [
           """OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """OwlViTModel""",
           """OwlViTPreTrainedModel""",
           """OwlViTTextModel""",
           """OwlViTVisionModel""",
           """OwlViTForObjectDetection""",
       ]

if TYPE_CHECKING:
       from .configuration_owlvit import (
           OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           OwlViTConfig,
           OwlViTOnnxConfig,
           OwlViTTextConfig,
           OwlViTVisionConfig,
       )
       from .processing_owlvit import OwlViTProcessor

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_owlvit import OwlViTFeatureExtractor
              from .image_processing_owlvit import OwlViTImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_owlvit import (
                  OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  OwlViTForObjectDetection,
                  OwlViTModel,
                  OwlViTPreTrainedModel,
                  OwlViTTextModel,
                  OwlViTVisionModel,
              )

else:
       import sys

       lowerCAmelCase     :  Union[str, Any]											=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

