






import os
import sys
import tempfile

import torch

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

def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			int=()  ,      _a       :			List[Any]=None  ,      _a       :			List[Any]="no"  ,      _a       :			Union[str, Any]="29500"    ):
		snake_case_				:							str						 =       False
		snake_case_				:							List[str]						 =       False
		if any(key.startswith("KAGGLE"    ) for key in os.environ.keys()    ):
				snake_case_				:							Tuple						 =       True
		elif "IPython" in sys.modules:
				snake_case_				:							Optional[Any]						 =       "google.colab" in str(sys.modules["IPython"].get_ipython()    )

		try:
				snake_case_				:							List[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"  ,      _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:
						snake_case_				:							int						 =       8

				snake_case_				:							int						 =       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    ):
								snake_case_				:							Union[str, Any]						 =       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():
								snake_case_				:							Tuple						 =       "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      lowerCAmelCase__	(    _a       :			Optional[Any]  ,      _a       :			str=()  ,      _a       :			int=2    ):
		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"  ,      ):
						snake_case_				:							Tuple						 =       PrepareForLaunch(_a  ,      debug=_a    )
						start_processes(_a  ,      args=_a  ,      nprocs=_a  ,      start_method="fork"    )








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase  :							str			  =    {
    '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
    '''tokenization_biogpt''': ['''BioGptTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							List[str]			  =    [
							    '''BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''BioGptForCausalLM''',
							    '''BioGptForTokenClassification''',
							    '''BioGptForSequenceClassification''',
							    '''BioGptModel''',
							    '''BioGptPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
							from .tokenization_biogpt import BioGptTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_biogpt import (
														    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BioGptForCausalLM,
														    BioGptForSequenceClassification,
														    BioGptForTokenClassification,
														    BioGptModel,
														    BioGptPreTrainedModel,
														)


else:
							import sys

							lowercase  :							Tuple			  =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
