

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_(_UpperCamelCase			,	_UpperCamelCase=()			,	_UpperCamelCase=None			,	_UpperCamelCase="no"			,	_UpperCamelCase="29500"					)							->     Optional[int]:



		"""simple docstring"""






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

		try:
				_snake_case						=							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'''			,	_UpperCamelCase					) 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						=							8

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



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase=()			,	_UpperCamelCase=2					)							->     Tuple:



		"""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=_UpperCamelCase			,	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						=							PrepareForLaunch(_UpperCamelCase			,	debug=_UpperCamelCase					)
						start_processes(_UpperCamelCase			,	args=_UpperCamelCase			,	nprocs=_UpperCamelCase			,	start_method='''fork'''					)




import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


__A      		=     logging.get_logger(__name__)



class   lowercase_   (  __lowercase					):



      def __init__(      self				:    Optional[Any]      , *A__				:    List[Any]      , **A__				:    int		)   ->					None:
        warnings.warn(
            '''The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
            ''' Please use DeformableDetrImageProcessor instead.'''      , A__      , )
        super().__init__(*A__      , **A__		)

