




"""simple docstring"""

import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer






def 						_snake_case					( _snake_case							: Dict							)   -> Any:






      '''simple docstring'''


      if (
          (cp >= 0X4e00 and cp <= 0X9fff)
          or (cp >= 0X3400 and cp <= 0X4dbf)  #
          or (cp >= 0X2_0000 and cp <= 0X2_a6df)  #
          or (cp >= 0X2_a700 and cp <= 0X2_b73f)  #
          or (cp >= 0X2_b740 and cp <= 0X2_b81f)  #
          or (cp >= 0X2_b820 and cp <= 0X2_ceaf)  #
          or (cp >= 0Xf900 and cp <= 0Xfaff)
          or (cp >= 0X2_f800 and cp <= 0X2_fa1f)  #
      ):  #
            return True

      return False






def 						_snake_case					( _snake_case							: str							)   -> Tuple:






      '''simple docstring'''


      for char in word:
            _A        =       ord(_snake_case							)
            if not _is_chinese_char(_snake_case							):
                  return 0
      return 1






def 						_snake_case					( _snake_case							: List[str]							)   -> Optional[Any]:






      '''simple docstring'''


      _A        =       set()

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






def 						_snake_case					( _snake_case							: List[str]    ,  _snake_case							: set()							)   -> Optional[Any]:






      '''simple docstring'''


      if not chinese_word_set:
            return bert_tokens
      _A        =       max([len(_snake_case							) for w in chinese_word_set]							)

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






def 						_snake_case					( _snake_case							: List[str]    ,  _snake_case							: LTP    ,  _snake_case							: BertTokenizer							)   -> str:






      '''simple docstring'''


      _A        =       []

      for i in range(0    ,  len(_snake_case							)    ,  1_00							):
            _A        =       ltp_tokenizer.seg(lines[i : i + 1_00]							)[0]
            _A        =       [get_chinese_word(_snake_case							) for r in res]
            ltp_res.extend(_snake_case							)
      assert len(_snake_case							) == len(_snake_case							)

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

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

      assert len(_snake_case							) == len(_snake_case							)

      return ref_ids






def 						_snake_case					( _snake_case							: List[str]							)   -> Dict:






      '''simple docstring'''


      with open(args.file_name    ,  'r'    ,  encoding='utf-8'							) as f:
            _A        =       f.readlines()
      _A        =       [line.strip() for line in data if len(_snake_case							) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
      _A        =       LTP(args.ltp							)  # faster in GPU device
      _A        =       BertTokenizer.from_pretrained(args.bert							)

      _A        =       prepare_ref(_snake_case    ,  _snake_case    ,  _snake_case							)

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


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

       a         =      parser.parse_args()
       main(args)







"""simple docstring"""

import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
       import tensorflow as tf
       from tensorflow.python.framework.errors_impl import ResourceExhaustedError

       from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
       import pyanvml.pyanvml as nvml

a         =      logging.get_logger(__name__)






def 						_snake_case					( _snake_case							: bool    ,  _snake_case							: bool							)   -> Tuple:






      '''simple docstring'''


      def run_func(_snake_case							: Any							):
            @wraps(_snake_case							)
            def run_in_eager_mode(*_snake_case							: List[str]    ,  **_snake_case							: Tuple							):
                  return func(*_snake_case    ,  **_snake_case							)

            @wraps(_snake_case							)
            @tf.function(experimental_compile=_snake_case							)
            def run_in_graph_mode(*_snake_case							: Dict    ,  **_snake_case							: Tuple							):
                  return func(*_snake_case    ,  **_snake_case							)

            if do_eager_mode is True:
                  if use_xla is not False:
                        raise ValueError(
                            'Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.'							)
                  return run_in_eager_mode
            else:
                  return run_in_graph_mode

      return run_func






def 						_snake_case					( _snake_case							: int    ,  _snake_case							: int    ,  _snake_case							: int							)   -> ["tf.Tensor"]:






      '''simple docstring'''


      _A        =       random.Random()
      _A        =       [rng.randint(0    ,  vocab_size - 1							) for i in range(batch_size * sequence_length							)]
      return tf.constant(_snake_case    ,  shape=(batch_size, sequence_length)    ,  dtype=tf.intaa							)







class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					TensorFlowBenchmarkArguments
 UpperCAmelCase   :					PretrainedConfig
 UpperCAmelCase   :					str				  =     "TensorFlow"

 @property
 def       lowerCAmelCase_							(			self	:	str		):
       return tf.__version__

 def       lowerCAmelCase_							(			self	:	List[str]   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	int		):
       # initialize GPU on separate process
       _A        =       self.args.strategy
       if strategy is None:
             raise ValueError('A device strategy has to be initialized before using TensorFlow.'		)
       _A        =       self._prepare_inference_func(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
       return self._measure_speed(_inference		)

 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	int		):
       _A        =       self.args.strategy
       if strategy is None:
             raise ValueError('A device strategy has to be initialized before using TensorFlow.'		)
       _A        =       self._prepare_train_func(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
       return self._measure_speed(_train		)

 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	int		):
       # initialize GPU on separate process
       if self.args.is_gpu:
             tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]   ,							_UpperCAmelCase		)
       _A        =       self.args.strategy
       if strategy is None:
             raise ValueError('A device strategy has to be initialized before using TensorFlow.'		)
       _A        =       self._prepare_inference_func(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
       return self._measure_memory(_inference		)

 def       lowerCAmelCase_							(			self	:	int   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	int		):
       if self.args.is_gpu:
             tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]   ,							_UpperCAmelCase		)
       _A        =       self.args.strategy
       if strategy is None:
             raise ValueError('A device strategy has to be initialized before using TensorFlow.'		)

       _A        =       self._prepare_train_func(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
       return self._measure_memory(_train		)

 def       lowerCAmelCase_							(			self	:	List[Any]   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	int		):
       _A        =       self.config_dict[model_name]

       if self.args.fpaa:
             raise NotImplementedError('Mixed precision is currently not supported.'		)

       _A        =       (
           hasattr(_UpperCAmelCase   ,							'architectures'		)
           and isinstance(config.architectures   ,							_UpperCAmelCase		)
           and len(config.architectures		) > 0
       )
       if not self.args.only_pretrain_model and has_model_class_in_config:
             try:
                   _A        =       'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
                   _A        =       __import__('transformers'   ,							fromlist=[model_class]		)
                   _A        =       getattr(_UpperCAmelCase   ,							_UpperCAmelCase		)
                   _A        =       model_cls(_UpperCAmelCase		)
             except ImportError:
                   raise ImportError(
                       F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
                       ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'		)
       else:
             _A        =       TF_MODEL_MAPPING[config.__class__](_UpperCAmelCase		)

       # encoder-decoder has vocab size saved differently
       _A        =       config.vocab_size if hasattr(_UpperCAmelCase   ,							'vocab_size'		) else config.encoder.vocab_size
       _A        =       random_input_ids(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)

       @run_with_tf_optimizations(self.args.eager_mode   ,							self.args.use_xla		)
       def encoder_decoder_forward():
             return model(_UpperCAmelCase   ,							decoder_input_ids=_UpperCAmelCase   ,							training=_UpperCAmelCase		)

       @run_with_tf_optimizations(self.args.eager_mode   ,							self.args.use_xla		)
       def encoder_forward():
             return model(_UpperCAmelCase   ,							training=_UpperCAmelCase		)

       _A        =       encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

       return _inference

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	int		):
       _A        =       self.config_dict[model_name]

       if self.args.eager_mode is not False:
             raise ValueError('Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.'		)

       if self.args.fpaa:
             raise NotImplementedError('Mixed precision is currently not supported.'		)

       _A        =       (
           hasattr(_UpperCAmelCase   ,							'architectures'		)
           and isinstance(config.architectures   ,							_UpperCAmelCase		)
           and len(config.architectures		) > 0
       )
       if not self.args.only_pretrain_model and has_model_class_in_config:
             try:
                   _A        =       'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
                   _A        =       __import__('transformers'   ,							fromlist=[model_class]		)
                   _A        =       getattr(_UpperCAmelCase   ,							_UpperCAmelCase		)
                   _A        =       model_cls(_UpperCAmelCase		)
             except ImportError:
                   raise ImportError(
                       F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
                       ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'		)
       else:
             _A        =       TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](_UpperCAmelCase		)

       # encoder-decoder has vocab size saved differently
       _A        =       config.vocab_size if hasattr(_UpperCAmelCase   ,							'vocab_size'		) else config.encoder.vocab_size
       _A        =       random_input_ids(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)

       @run_with_tf_optimizations(self.args.eager_mode   ,							self.args.use_xla		)
       def encoder_decoder_train():
             _A        =       model(_UpperCAmelCase   ,							decoder_input_ids=_UpperCAmelCase   ,							labels=_UpperCAmelCase   ,							training=_UpperCAmelCase		)[0]
             _A        =       tf.gradients(_UpperCAmelCase   ,							model.trainable_variables		)
             return gradients

       @run_with_tf_optimizations(self.args.eager_mode   ,							self.args.use_xla		)
       def encoder_train():
             _A        =       model(_UpperCAmelCase   ,							labels=_UpperCAmelCase   ,							training=_UpperCAmelCase		)[0]
             _A        =       tf.gradients(_UpperCAmelCase   ,							model.trainable_variables		)
             return gradients

       _A        =       encoder_decoder_train if config.is_encoder_decoder else encoder_train

       return _train

 def       lowerCAmelCase_							(			self	:	Tuple   ,							_UpperCAmelCase	:	int		):
       with self.args.strategy.scope():
             try:
                   if self.args.is_tpu or self.args.use_xla:
                         # run additional 10 times to stabilize compilation for tpu
                         logger.info('Do inference on TPU. Running model 5 times to stabilize compilation'		)
                         timeit.repeat(_UpperCAmelCase   ,							repeat=1   ,							number=5		)

                   # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
                   _A        =       timeit.repeat(
                       _UpperCAmelCase   ,							repeat=self.args.repeat   ,							number=10   ,							)

                   return min(_UpperCAmelCase		) / 10.0
             except ResourceExhaustedError as e:
                   self.print_fn(F'''Doesn\'t fit on GPU. {e}'''		)



 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							_UpperCAmelCase	:	Callable[[], None]		):
       logger.info(
           'Note that TensorFlow allocates more memory than '
           'it might need to speed up computation. '
           'The memory reported here corresponds to the memory '
           'reported by `nvidia-smi`, which can vary depending '
           'on total available memory on the GPU that is used.'		)
       with self.args.strategy.scope():
             try:
                   if self.args.trace_memory_line_by_line:
                         if not self.args.eager_mode:
                               raise ValueError(
                                   '`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory'
                                   ' consumption line by line.'		)
                         _A        =       start_memory_tracing('transformers'		)

                   if self.args.is_tpu:
                         # tpu
                         raise NotImplementedError(
                             'Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking'
                             ' with `args.memory=False`'		)
                   elif self.args.is_gpu:
                         # gpu
                         if not is_pyanvml_available():
                               logger.warning(
                                   'py3nvml not installed, we won\'t log GPU memory usage. '
                                   'Install py3nvml (pip install py3nvml) to log information about GPU.'		)
                               _A        =       'N/A'
                         else:
                               logger.info(
                                   'Measuring total GPU usage on GPU device. Make sure to not have additional processes'
                                   ' running on the same GPU.'		)
                               # init nvml
                               nvml.nvmlInit()
                               func()
                               _A        =       nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx		)
                               _A        =       nvml.nvmlDeviceGetMemoryInfo(_UpperCAmelCase		)
                               _A        =       meminfo.used
                               _A        =       Memory(_UpperCAmelCase		)
                               # shutdown nvml
                               nvml.nvmlShutdown()
                   else:
                         # cpu
                         if self.args.trace_memory_line_by_line:
                               logger.info(
                                   'When enabling line by line tracing, the max peak memory for CPU is inaccurate in'
                                   ' TensorFlow.'		)
                               _A        =       None
                         else:
                               _A        =       measure_peak_memory_cpu(_UpperCAmelCase		)
                               _A        =       Memory(_UpperCAmelCase		) if isinstance(_UpperCAmelCase   ,							_UpperCAmelCase		) else memory_bytes
                   if self.args.trace_memory_line_by_line:
                         _A        =       stop_memory_tracing(_UpperCAmelCase		)
                         if memory is None:
                               _A        =       summary.total
                   else:
                         _A        =       None

                   return memory, summary
             except ResourceExhaustedError as e:
                   self.print_fn(F'''Doesn\'t fit on GPU. {e}'''		)
                   return "N/A", None

