




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													=			logging.getLogger(__name__)




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				Any:
      _UpperCAmelCase      =			git.Repo(search_parent_directories=snake_case       )
      _UpperCAmelCase      =			{
          """repo_id""": str(snake_case       ),
          """repo_sha""": str(repo.head.object.hexsha       ),
          """repo_branch""": str(repo.active_branch       ),
      }

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




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				int:
      if params.n_gpu <= 0:
            _UpperCAmelCase      =			0
            _UpperCAmelCase      =			-1
            _UpperCAmelCase      =			True
            _UpperCAmelCase      =			False
            return

      assert torch.cuda.is_available()

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

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

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

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

      # summary
      _UpperCAmelCase      =			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        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				int:
      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_bert import BertTokenizer


a													=			logging.get_logger(__name__)

a													=			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

a													=			{
    "vocab_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/vocab.txt",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/vocab.txt",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt"
        ),
        "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt",
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt"
        ),
        "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt",
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json"
        ),
        "bert-base-multilingual-cased": (
            "https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-cased": (
            "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json"
        ),
    },
}

a													=			{
    "bert-base-uncased": 512,
    "bert-large-uncased": 512,
    "bert-base-cased": 512,
    "bert-large-cased": 512,
    "bert-base-multilingual-uncased": 512,
    "bert-base-multilingual-cased": 512,
    "bert-base-chinese": 512,
    "bert-base-german-cased": 512,
    "bert-large-uncased-whole-word-masking": 512,
    "bert-large-cased-whole-word-masking": 512,
    "bert-large-uncased-whole-word-masking-finetuned-squad": 512,
    "bert-large-cased-whole-word-masking-finetuned-squad": 512,
    "bert-base-cased-finetuned-mrpc": 512,
    "bert-base-german-dbmdz-cased": 512,
    "bert-base-german-dbmdz-uncased": 512,
    "TurkuNLP/bert-base-finnish-cased-v1": 512,
    "TurkuNLP/bert-base-finnish-uncased-v1": 512,
    "wietsedv/bert-base-dutch-cased": 512,
}

a													=			{
    "bert-base-uncased": {"do_lower_case": True},
    "bert-large-uncased": {"do_lower_case": True},
    "bert-base-cased": {"do_lower_case": False},
    "bert-large-cased": {"do_lower_case": False},
    "bert-base-multilingual-uncased": {"do_lower_case": True},
    "bert-base-multilingual-cased": {"do_lower_case": False},
    "bert-base-chinese": {"do_lower_case": False},
    "bert-base-german-cased": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking-finetuned-squad": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking-finetuned-squad": {"do_lower_case": False},
    "bert-base-cased-finetuned-mrpc": {"do_lower_case": False},
    "bert-base-german-dbmdz-cased": {"do_lower_case": False},
    "bert-base-german-dbmdz-uncased": {"do_lower_case": True},
    "TurkuNLP/bert-base-finnish-cased-v1": {"do_lower_case": False},
    "TurkuNLP/bert-base-finnish-uncased-v1": {"do_lower_case": True},
    "wietsedv/bert-base-dutch-cased": {"do_lower_case": False},
}




class       _A				(		__lowercase     ):
		__a					    =       VOCAB_FILES_NAMES
		__a					    =       PRETRAINED_VOCAB_FILES_MAP
		__a					    =       PRETRAINED_INIT_CONFIGURATION
		__a					    =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__a					    =       BertTokenizer






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

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

								_UpperCAmelCase      =			do_lower_case






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=None			):
								_UpperCAmelCase      =			[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 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = None			):
								_UpperCAmelCase      =			[self.sep_token_id]
								_UpperCAmelCase      =			[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 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = None			):
								_UpperCAmelCase      =			self._tokenizer.model.save(_SCREAMING_SNAKE_CASE						,		name=_SCREAMING_SNAKE_CASE			)
								return tuple(_SCREAMING_SNAKE_CASE			)