






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

	try:
		snake_case__			:				Union[str, Any]       =  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:
			snake_case__			:				Tuple       =  8

		snake_case__			:				Tuple       =  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       ):
				snake_case__			:				List[str]       =  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():
				snake_case__			:				Union[str, Any]       =  "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    (			snake_case_ :							int					,  snake_case_ :							int=()					,  snake_case_ :							str=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=_lowercase					,  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__			:				Union[str, Any]       =  PrepareForLaunch(_lowercase					,  debug=_lowercase       )
			start_processes(_lowercase					,  args=_lowercase					,  nprocs=_lowercase					,  start_method="fork"       )











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase :      Union[str, Any]										=				{
    """configuration_time_series_transformer""": [
        """TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TimeSeriesTransformerConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase :      Tuple										=				[
	    """TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TimeSeriesTransformerForPrediction""",
	    """TimeSeriesTransformerModel""",
	    """TimeSeriesTransformerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_time_series_transformer import (
	    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    TimeSeriesTransformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimeSeriesTransformerForPrediction,
		    TimeSeriesTransformerModel,
		    TimeSeriesTransformerPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase :      Tuple										=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



