


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

							'''simple docstring'''



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

							try:
														__UpperCAmelCase   :						Optional[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"""       ,			_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:
																					__UpperCAmelCase   :						Union[str, Any]      							=					8

														__UpperCAmelCase   :						int      							=					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							):
																												__UpperCAmelCase   :						Union[str, Any]      							=					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():
																												__UpperCAmelCase   :						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(*_UpperCamelCase							)
def      lowerCamelCase       (     _UpperCamelCase		:      Dict       ,			_UpperCamelCase		:      Any=()       ,			_UpperCamelCase		:      List[str]=2							)		->       Optional[Any]:

							'''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"""       ,			):
																					__UpperCAmelCase   :						Optional[Any]      							=					PrepareForLaunch(_UpperCamelCase       ,			debug=_UpperCamelCase							)
																					start_processes(_UpperCamelCase       ,			args=_UpperCamelCase       ,			nprocs=_UpperCamelCase       ,			start_method="""fork"""							)




"""simple docstring"""






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_barthez import BarthezTokenizer
else:
							UpperCAmelCase    :     Union[str, Any]   						=		None

UpperCAmelCase    :     Union[str, Any]   						=		logging.get_logger(__name__)

UpperCAmelCase    :     Union[str, Any]   						=		{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase    :     List[str]   						=		{
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'
        ),
    },
}

UpperCAmelCase    :     Tuple   						=		{
    'moussaKam/mbarthez': 1024,
    'moussaKam/barthez': 1024,
    'moussaKam/barthez-orangesum-title': 1024,
}

UpperCAmelCase    :     str   						=		'▁'





class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					__a	       =							VOCAB_FILES_NAMES
					__a	       =							PRETRAINED_VOCAB_FILES_MAP
					__a	       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__a	       =							["""input_ids""", """attention_mask"""]
					__a	       =							BarthezTokenizer






					def __init__(       self    : Optional[int] ,       UpperCamelCase    : List[str]=None ,       UpperCamelCase    : List[str]=None ,       UpperCamelCase    : Union[str, Any]="<s>" ,       UpperCamelCase    : Any="</s>" ,       UpperCamelCase    : Tuple="</s>" ,       UpperCamelCase    : Tuple="<s>" ,       UpperCamelCase    : int="<unk>" ,       UpperCamelCase    : List[str]="<pad>" ,       UpperCamelCase    : int="<mask>" ,       **UpperCamelCase    : Any ,       ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					AddedToken(UpperCamelCase ,       lstrip=UpperCamelCase ,       rstrip=UpperCamelCase  ) if isinstance(UpperCamelCase ,       UpperCamelCase  ) else mask_token

												super().__init__(
												    UpperCamelCase ,       tokenizer_file=UpperCamelCase ,       bos_token=UpperCamelCase ,       eos_token=UpperCamelCase ,       unk_token=UpperCamelCase ,       sep_token=UpperCamelCase ,       cls_token=UpperCamelCase ,       pad_token=UpperCamelCase ,       mask_token=UpperCamelCase ,       **UpperCamelCase ,       )

												__UpperCAmelCase   :						List[Any]      							=					vocab_file
												__UpperCAmelCase   :						Tuple      							=					False if not self.vocab_file else True






					def lowerCamelCase__							(       self    : Any ,       UpperCamelCase    : List[int] ,       UpperCamelCase    : Optional[List[int]] = None  ):
												'''simple docstring'''






												if token_ids_a is None:
																			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												__UpperCAmelCase   :						List[Any]      							=					[self.cls_token_id]
												__UpperCAmelCase   :						Tuple      							=					[self.sep_token_id]
												return cls + token_ids_a + sep + sep + token_ids_a + sep






					def lowerCamelCase__							(       self    : Union[str, Any] ,       UpperCamelCase    : List[int] ,       UpperCamelCase    : Optional[List[int]] = None  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					[self.sep_token_id]
												__UpperCAmelCase   :						Union[str, Any]      							=					[self.cls_token_id]

												if token_ids_a is None:
																			return len(cls + token_ids_a + sep  ) * [0]
												return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]






					def lowerCamelCase__							(       self    : Tuple ,       UpperCamelCase    : str ,       UpperCamelCase    : Optional[str] = None  ):
												'''simple docstring'''






												if not self.can_save_slow_tokenizer:
																			raise ValueError(
																			    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
																			    """tokenizer."""  )

												if not os.path.isdir(UpperCamelCase  ):
																			logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
																			return
												__UpperCAmelCase   :						List[Any]      							=					os.path.join(
												    UpperCamelCase ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )

												if os.path.abspath(self.vocab_file  ) != os.path.abspath(UpperCamelCase  ):
																			copyfile(self.vocab_file ,       UpperCamelCase  )

												return (out_vocab_file,)
