






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_xlnet import XLNetTokenizer
else:
						A:      List[Any]  				=				None


A:      Dict  				=				logging.get_logger(__name__)

A:      Dict  				=				{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

A:      List[str]  				=				{
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
    },
}

A:      Optional[Any]  				=				{
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

A:      str  				=				"▁"

# Segments (not really needed)
A:      str  				=				0
A:      Any  				=				1
A:      Union[str, Any]  				=				2
A:      str  				=				3
A:      List[str]  				=				4






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      List[str]        =  VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_					:      Any        =  PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_					:      Optional[int]        =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_					:      Union[str, Any]        =  'left'
	SCREAMING_SNAKE_CASE_					:      Dict        =  XLNetTokenizer
	def __init__(							self  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=False  ,      _lowercase=True  ,      _lowercase=False  ,      _lowercase="<s>"  ,      _lowercase="</s>"  ,      _lowercase="<unk>"  ,      _lowercase="<sep>"  ,      _lowercase="<pad>"  ,      _lowercase="<cls>"  ,      _lowercase="<mask>"  ,      _lowercase=["<eop>", "<eod>"]  ,      **_lowercase  ,      )						->  int:
							# Mask token behave like a normal word, i.e. include the space before it
							lowercase_      :   Dict        =			AddedToken(_lowercase  ,      lstrip=_lowercase  ,      rstrip=_lowercase       ) if isinstance(_lowercase  ,      _lowercase       ) else mask_token

							super().__init__(
							    vocab_file=_lowercase  ,      tokenizer_file=_lowercase  ,      do_lower_case=_lowercase  ,      remove_space=_lowercase  ,      keep_accents=_lowercase  ,      bos_token=_lowercase  ,      eos_token=_lowercase  ,      unk_token=_lowercase  ,      sep_token=_lowercase  ,      pad_token=_lowercase  ,      cls_token=_lowercase  ,      mask_token=_lowercase  ,      additional_special_tokens=_lowercase  ,      **_lowercase  ,      )

							lowercase_      :   Optional[int]        =			3
							lowercase_      :   List[str]        =			do_lower_case
							lowercase_      :   Tuple        =			remove_space
							lowercase_      :   List[Any]        =			keep_accents
							lowercase_      :   int        =			vocab_file
							lowercase_      :   List[Any]        =			False if not self.vocab_file else True
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = None       )						->  List[int]:
							lowercase_      :   str        =			[self.sep_token_id]
							lowercase_      :   Optional[Any]        =			[self.cls_token_id]
							if token_ids_a is None:
													return token_ids_a + sep + cls
							return token_ids_a + sep + token_ids_a + sep + cls
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = None       )						->  List[int]:
							lowercase_      :   int        =			[self.sep_token_id]
							lowercase_      :   str        =			[2]

							if token_ids_a is None:
													return len(token_ids_a + sep       ) * [0] + cls_segment_id
							return len(token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1] + cls_segment_id







	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = None       )						->  Tuple[str]:
							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(_lowercase       ):
													logger.error(f"Vocabulary path ({save_directory}) should be a directory"       )
													return
							lowercase_      :   Any        =			os.path.join(
							    _lowercase  ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

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

							return (out_vocab_file,)








'''simple docstring'''

import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
A:      Tuple  				=				logging.getLogger(__name__)






def 	_UpperCAmelCase    (  a		:	str							)			->			List[Any]:





						"""simple docstring"""
						lowercase_      :   List[str]        =			git.Repo(search_parent_directories=a							)
						lowercase_      :   Union[str, Any]        =			{
						    'repo_id': str(a							),
						    'repo_sha': str(repo.head.object.hexsha							),
						    'repo_branch': str(repo.active_branch							),
						}

						with open(os.path.join(a						,			'git_log.json'							)						,			'w'							) as f:
												json.dump(a						,			a						,			indent=4							)






def 	_UpperCAmelCase    (  a		:	str							)			->			Union[str, Any]:





						"""simple docstring"""
						if params.n_gpu <= 0:
												lowercase_      :   int        =			0
												lowercase_      :   Union[str, Any]        =			-1
												lowercase_      :   List[str]        =			True
												lowercase_      :   Optional[Any]        =			False
												return

						assert torch.cuda.is_available()

						logger.info('Initializing GPUs'							)
						if params.n_gpu > 1:
												assert params.local_rank != -1

												lowercase_      :   Dict        =			int(os.environ['WORLD_SIZE']							)
												lowercase_      :   Union[str, Any]        =			int(os.environ['N_GPU_NODE']							)
												lowercase_      :   Optional[int]        =			int(os.environ['RANK']							)

												# number of nodes / node ID
												lowercase_      :   int        =			params.world_size // params.n_gpu_per_node
												lowercase_      :   str        =			params.global_rank // params.n_gpu_per_node
												lowercase_      :   Dict        =			True

												assert params.n_nodes == int(os.environ['N_NODES']							)
												assert params.node_id == int(os.environ['NODE_RANK']							)

						# local job (single GPU)
						else:
												assert params.local_rank == -1

												lowercase_      :   str        =			1
												lowercase_      :   Dict        =			0
												lowercase_      :   Tuple        =			0
												lowercase_      :   List[Any]        =			0
												lowercase_      :   int        =			1
												lowercase_      :   Tuple        =			1
												lowercase_      :   str        =			False

						# sanity checks
						assert params.n_nodes >= 1
						assert 0 <= params.node_id < params.n_nodes
						assert 0 <= params.local_rank <= params.global_rank < params.world_size
						assert params.world_size == params.n_nodes * params.n_gpu_per_node

						# define whether this is the master process / if we are in multi-node distributed mode
						lowercase_      :   List[str]        =			params.node_id == 0 and params.local_rank == 0
						lowercase_      :   Optional[Any]        =			params.n_nodes > 1

						# summary
						lowercase_      :   int        =			f"--- Global rank: {params.global_rank} - "
						logger.info(PREFIX + 'Number of nodes: %i' % params.n_nodes							)
						logger.info(PREFIX + 'Node ID        : %i' % params.node_id							)
						logger.info(PREFIX + 'Local rank     : %i' % params.local_rank							)
						logger.info(PREFIX + 'World size     : %i' % params.world_size							)
						logger.info(PREFIX + 'GPUs per node  : %i' % params.n_gpu_per_node							)
						logger.info(PREFIX + 'Master         : %s' % str(params.is_master							)							)
						logger.info(PREFIX + 'Multi-node     : %s' % str(params.multi_node							)							)
						logger.info(PREFIX + 'Multi-GPU      : %s' % str(params.multi_gpu							)							)
						logger.info(PREFIX + 'Hostname       : %s' % socket.gethostname()							)

						# set GPU device
						torch.cuda.set_device(params.local_rank							)

						# initialize multi-GPU
						if params.multi_gpu:
												logger.info('Initializing PyTorch distributed'							)
												torch.distributed.init_process_group(
												    init_method='env://'						,			backend='nccl'						,			)






def 	_UpperCAmelCase    (  a		:	Dict							)			->			Optional[int]:





						"""simple docstring"""
						np.random.seed(args.seed							)
						torch.manual_seed(args.seed							)
						if args.n_gpu > 0:
												torch.cuda.manual_seed_all(args.seed							)
