"""simple docstring"""


import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  # This defines a "chinese character" as anything in the CJK Unicode block:
  #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
  #
  # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
  # despite its name. The modern Korean Hangul alphabet is a different block,
  # as is Japanese Hiragana and Katakana. Those alphabets are used to write
  # space-separated words, so they are not treated specially and handled
  # like the all of the other languages.
  if (
      (cp >= 0x4_e_0_0 and cp <= 0x9_f_f_f)
      or (cp >= 0x3_4_0_0 and cp <= 0x4_d_b_f)  #
      or (cp >= 0x2_0_0_0_0 and cp <= 0x2_a_6_d_f)  #
      or (cp >= 0x2_a_7_0_0 and cp <= 0x2_b_7_3_f)  #
      or (cp >= 0x2_b_7_4_0 and cp <= 0x2_b_8_1_f)  #
      or (cp >= 0x2_b_8_2_0 and cp <= 0x2_c_e_a_f)  #
      or (cp >= 0xf_9_0_0 and cp <= 0xf_a_f_f)
      or (cp >= 0x2_f_8_0_0 and cp <= 0x2_f_a_1_f)  #
  ):  #
    return True

  return False






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  # word like '180' or '身高' or '神'
  for char in word:
    _UpperCAmelCase         =					ord(lowercase						)
    if not _is_chinese_char(lowercase						):
      return 0
  return 1






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					set()

  for token in tokens:
    _UpperCAmelCase         =					len(lowercase						) > 1 and is_chinese(lowercase						)
    if chinese_word:
      word_set.add(lowercase						)
  _UpperCAmelCase         =					list(lowercase						)
  return word_list






def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """simple docstring"""
  if not chinese_word_set:
    return bert_tokens
  _UpperCAmelCase         =					max([len(lowercase						) for w in chinese_word_set]						)

  _UpperCAmelCase         =					bert_tokens
  _UpperCAmelCase       , _UpperCAmelCase         =					0, len(lowercase						)
  while start < end:
    _UpperCAmelCase         =					True
    if is_chinese(bert_word[start]						):
      _UpperCAmelCase         =					min(end - start      ,lowercase						)
      for i in range(lowercase      ,1      ,-1						):
        _UpperCAmelCase         =					"""""".join(bert_word[start : start + i]						)
        if whole_word in chinese_word_set:
          for j in range(start + 1      ,start + i						):
            _UpperCAmelCase         =					"""##""" + bert_word[j]
          _UpperCAmelCase         =					start + i
          _UpperCAmelCase         =					False
          break
    if single_word:
      start += 1
  return bert_word






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					[]

  for i in range(0      ,len(lowercase						)      ,1_00						):
    _UpperCAmelCase         =					ltp_tokenizer.pipeline(lines[i : i + 1_00]      ,tasks=["""cws"""]						).cws
    _UpperCAmelCase         =					[get_chinese_word(lowercase						) for r in res]
    ltp_res.extend(lowercase						)
  assert len(lowercase						) == len(lowercase						)

  _UpperCAmelCase         =					[]
  for i in range(0      ,len(lowercase						)      ,1_00						):
    _UpperCAmelCase         =					bert_tokenizer(lines[i : i + 1_00]      ,add_special_tokens=lowercase      ,truncation=lowercase      ,max_length=5_12						)
    bert_res.extend(res["""input_ids"""]						)
  assert len(lowercase						) == len(lowercase						)

  _UpperCAmelCase         =					[]
  for input_ids, chinese_word in zip(lowercase      ,lowercase						):
    _UpperCAmelCase         =					[]
    for id in input_ids:
      _UpperCAmelCase         =					bert_tokenizer._convert_id_to_token(lowercase						)
      input_tokens.append(lowercase						)
    _UpperCAmelCase         =					add_sub_symbol(lowercase      ,lowercase						)
    _UpperCAmelCase         =					[]
    # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
    for i, token in enumerate(lowercase						):
      if token[:2] == "##":
        _UpperCAmelCase         =					token[2:]
        # save chinese tokens' pos
        if len(lowercase						) == 1 and _is_chinese_char(ord(lowercase						)						):
          ref_id.append(lowercase						)
    ref_ids.append(lowercase						)

  assert len(lowercase						) == len(lowercase						)

  return ref_ids






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
  # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
  with open(args.file_name      ,"""r"""      ,encoding="""utf-8"""						) as f:
    _UpperCAmelCase         =					f.readlines()
  _UpperCAmelCase         =					[line.strip() for line in data if len(lowercase						) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
  _UpperCAmelCase         =					LTP(args.ltp						)  # faster in GPU device
  _UpperCAmelCase         =					BertTokenizer.from_pretrained(args.bert						)

  _UpperCAmelCase         =					prepare_ref(lowercase      ,lowercase      ,lowercase						)

  with open(args.save_path      ,"""w"""      ,encoding="""utf-8"""						) as f:
    _UpperCAmelCase         =					[json.dumps(lowercase						) + """\n""" for ref in ref_ids]
    f.writelines(lowercase						)


if __name__ == "__main__":
    UpperCAmelCase__     						=  argparse.ArgumentParser(description="""prepare_chinese_ref""")
    parser.add_argument(
        """--file_name""",
        required=False,
        type=str,
        default="""./resources/chinese-demo.txt""",
        help="""file need process, same as training data in lm""",
    )
    parser.add_argument(
        """--ltp""",
        required=False,
        type=str,
        default="""./resources/ltp""",
        help="""resources for LTP tokenizer, usually a path""",
    )
    parser.add_argument(
        """--bert""",
        required=False,
        type=str,
        default="""./resources/robert""",
        help="""resources for Bert tokenizer""",
    )
    parser.add_argument(
        """--save_path""",
        required=False,
        type=str,
        default="""./resources/ref.txt""",
        help="""path to save res""",
    )

    UpperCAmelCase__     						=  parser.parse_args()
    main(args)

"""simple docstring"""


from .constants import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    TORCH_LAUNCH_PARAMS,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
)
from .dataclasses import (
    BnbQuantizationConfig,
    ComputeEnvironment,
    CustomDtype,
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    DynamoBackend,
    FPaRecipeKwargs,
    FullyShardedDataParallelPlugin,
    GradientAccumulationPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    KwargsHandler,
    LoggerType,
    MegatronLMPlugin,
    PrecisionType,
    ProjectConfiguration,
    RNGType,
    SageMakerDistributedType,
    TensorInformation,
    TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
    get_ccl_version,
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_aim_available,
    is_bfaa_available,
    is_bnb_available,
    is_botoa_available,
    is_ccl_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_fpa_available,
    is_ipex_available,
    is_megatron_lm_available,
    is_mlflow_available,
    is_mps_available,
    is_npu_available,
    is_rich_available,
    is_safetensors_available,
    is_sagemaker_available,
    is_tensorboard_available,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
from .modeling import (
    check_device_map,
    check_tied_parameters_in_config,
    check_tied_parameters_on_same_device,
    compute_module_sizes,
    convert_file_size_to_int,
    dtype_byte_size,
    find_tied_parameters,
    get_balanced_memory,
    get_max_layer_size,
    get_max_memory,
    get_mixed_precision_context_manager,
    id_tensor_storage,
    infer_auto_device_map,
    load_checkpoint_in_model,
    load_offloaded_weights,
    load_state_dict,
    named_module_tensors,
    retie_parameters,
    set_module_tensor_to_device,
    shard_checkpoint,
)
from .offload import (
    OffloadedWeightsLoader,
    PrefixedDataset,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
    save_offload_index,
)
from .operations import (
    broadcast,
    broadcast_object_list,
    concatenate,
    convert_outputs_to_fpaa,
    convert_to_fpaa,
    find_batch_size,
    find_device,
    gather,
    gather_object,
    get_data_structure,
    honor_type,
    initialize_tensors,
    is_namedtuple,
    is_tensor_information,
    is_torch_tensor,
    listify,
    pad_across_processes,
    recursively_apply,
    reduce,
    send_to_device,
    slice_tensors,
)
from .versions import compare_versions, is_torch_version


if is_deepspeed_available():
    from .deepspeed import (
        DeepSpeedEngineWrapper,
        DeepSpeedOptimizerWrapper,
        DeepSpeedSchedulerWrapper,
        DummyOptim,
        DummyScheduler,
        HfDeepSpeedConfig,
    )

from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
    PrepareForLaunch,
    _filter_args,
    prepare_deepspeed_cmd_env,
    prepare_multi_gpu_env,
    prepare_sagemager_args_inputs,
    prepare_simple_launcher_cmd_env,
    prepare_tpu,
)
from .megatron_lm import (
    AbstractTrainStep,
    BertTrainStep,
    GPTTrainStep,
    MegatronEngine,
    MegatronLMDummyDataLoader,
    MegatronLMDummyScheduler,
    MegatronLMOptimizerWrapper,
    MegatronLMSchedulerWrapper,
    TaTrainStep,
    avg_losses_across_data_parallel_group,
    gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
    extract_model_from_parallel,
    get_pretty_name,
    is_port_in_use,
    merge_dicts,
    patch_environment,
    save,
    wait_for_everyone,
    write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers
