


'''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       a_				(	UpperCamelCase_						,  UpperCamelCase_=()						,  UpperCamelCase_=None						,  UpperCamelCase_="no"						,  UpperCamelCase_="29500" ):
							A_			    =		False
							A_			    =		False
							if any(key.startswith("KAGGLE" ) for key in os.environ.keys() ):
														A_			    =		True
							elif "IPython" in sys.modules:
														A_			    =		"google.colab" in str(sys.modules["IPython"].get_ipython() )

							try:
														A_			    =		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:
																					A_			    =		8

														A_			    =		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_ ):
																												A_			    =		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():
																												A_			    =		"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       a_				(	UpperCamelCase_						,  UpperCamelCase_=()						,  UpperCamelCase_=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=UpperCamelCase_						,  master_addr="127.0.01"						,  master_port="29500"						,  accelerate_mixed_precision="no"						,  accelerate_debug_rdv_file=tmp_file.name						,  accelerate_use_cpu="yes"						,  ):
																					A_			    =		PrepareForLaunch(UpperCamelCase_						,  debug=UpperCamelCase_ )
																					start_processes(UpperCamelCase_						,  args=UpperCamelCase_						,  nprocs=UpperCamelCase_						,  start_method="fork" )









'''simple docstring'''
__SCREAMING_SNAKE_CASE					:				Optional[int]									=		{
    '''a''': '''AAAAA''',
    '''b''': '''AAAAB''',
    '''c''': '''AAABA''',
    '''d''': '''AAABB''',
    '''e''': '''AABAA''',
    '''f''': '''AABAB''',
    '''g''': '''AABBA''',
    '''h''': '''AABBB''',
    '''i''': '''ABAAA''',
    '''j''': '''BBBAA''',
    '''k''': '''ABAAB''',
    '''l''': '''ABABA''',
    '''m''': '''ABABB''',
    '''n''': '''ABBAA''',
    '''o''': '''ABBAB''',
    '''p''': '''ABBBA''',
    '''q''': '''ABBBB''',
    '''r''': '''BAAAA''',
    '''s''': '''BAAAB''',
    '''t''': '''BAABA''',
    '''u''': '''BAABB''',
    '''v''': '''BBBAB''',
    '''w''': '''BABAA''',
    '''x''': '''BABAB''',
    '''y''': '''BABBA''',
    '''z''': '''BABBB''',
    ''' ''': ''' ''',
}


__SCREAMING_SNAKE_CASE					:				Tuple									=		{value: key for key, value in encode_dict.items()}







def       a_				(	UpperCamelCase_ ):
							A_			    =		""
							for letter in word.lower():
														if letter.isalpha() or letter == " ":
																					encoded += encode_dict[letter]
														else:
																					raise Exception("encode() accepts only letters of the alphabet and spaces" )
							return encoded







def       a_				(	UpperCamelCase_ ):
							if set(UpperCamelCase_ ) - {"A", "B", " "} != set():
														raise Exception("decode() accepts only 'A', 'B' and spaces" )
							A_			    =		""
							for word in coded.split():
														while len(UpperCamelCase_ ) != 0:
																					decoded += decode_dict[word[:5]]
																					A_			    =		word[5:]
														decoded += " "
							return decoded.strip()


if __name__ == "__main__":
							from doctest import testmod

							testmod()





