


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,
)
SCREAMING_SNAKE_CASE							    =							logging.getLogger(__name__)




def  a   (lowerCAmelCase__   ):
   __a					=  git.Repo(search_parent_directories=lowerCAmelCase__   )
   __a					=  {
       """repo_id""": str(lowerCAmelCase__   ),
       """repo_sha""": str(repo.head.object.hexsha   ),
       """repo_branch""": str(repo.active_branch   ),
   }

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




def  a   (lowerCAmelCase__   ):
   if params.n_gpu <= 0:
      __a					=  0
      __a					=  -1
      __a					=  True
      __a					=  False
      return

   assert torch.cuda.is_available()

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

      __a					=  int(os.environ["""WORLD_SIZE"""]   )
      __a					=  int(os.environ["""N_GPU_NODE"""]   )
      __a					=  int(os.environ["""RANK"""]   )

      # number of nodes / node ID
      __a					=  params.world_size // params.n_gpu_per_node
      __a					=  params.global_rank // params.n_gpu_per_node
      __a					=  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

      __a					=  1
      __a					=  0
      __a					=  0
      __a					=  0
      __a					=  1
      __a					=  1
      __a					=  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
   __a					=  params.node_id == 0 and params.local_rank == 0
   __a					=  params.n_nodes > 1

   # summary
   __a					=  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  a   (lowerCAmelCase__   ):
   np.random.seed(args.seed   )
   torch.manual_seed(args.seed   )
   if args.n_gpu > 0:
      torch.cuda.manual_seed_all(args.seed   )







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE							    =							{
    'vocab_file': {'mobilebert-uncased': 'https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt'},
    'tokenizer_file': {
        'mobilebert-uncased': 'https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json'
    },
}

SCREAMING_SNAKE_CASE							    =							{'mobilebert-uncased': 5_1_2}


SCREAMING_SNAKE_CASE							    =							{}


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      VOCAB_FILES_NAMES
     _lowerCamelCase   =      PRETRAINED_VOCAB_FILES_MAP
     _lowerCamelCase   =      PRETRAINED_INIT_CONFIGURATION
     _lowerCamelCase   =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _lowerCamelCase   =      MobileBertTokenizer


     def __init__(	self  ,       __A=None  ,       __A=None  ,       __A=True  ,       __A="[UNK]"  ,       __A="[SEP]"  ,       __A="[PAD]"  ,       __A="[CLS]"  ,       __A="[MASK]"  ,       __A=True  ,       __A=None  ,       **__A  ,       ):
        super().__init__(
            __A  ,       tokenizer_file=__A  ,       do_lower_case=__A  ,       unk_token=__A  ,       sep_token=__A  ,       pad_token=__A  ,       cls_token=__A  ,       mask_token=__A  ,       tokenize_chinese_chars=__A  ,       strip_accents=__A  ,       **__A  ,       )

        __a					=  json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
        if (
            normalizer_state.get("""lowercase"""  ,       __A    ) != do_lower_case
            or normalizer_state.get("""strip_accents"""  ,       __A    ) != strip_accents
            or normalizer_state.get("""handle_chinese_chars"""  ,       __A    ) != tokenize_chinese_chars
        ):
           __a					=  getattr(__A  ,       normalizer_state.pop("""type"""    )    )
           __a					=  do_lower_case
           __a					=  strip_accents
           __a					=  tokenize_chinese_chars
           __a					=  normalizer_class(**__A    )

        __a					=  do_lower_case


     def 	snake_case_  (	self  ,       __A  ,       __A=None    ):
        __a					=  [self.cls_token_id] + token_ids_a + [self.sep_token_id]

        if token_ids_a:
           output += token_ids_a + [self.sep_token_id]

        return output


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        __a					=  [self.sep_token_id]
        __a					=  [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    ) * [0] + len(token_ids_a + sep    ) * [1]


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        __a					=  self._tokenizer.model.save(__A  ,       name=__A    )
        return tuple(__A    )



