

"""simple docstring"""






import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


_lowerCAmelCase						=		1E-4

if is_torch_available():
 import torch

 from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
 from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder







@require_torch
class 	__UpperCamelCase    :

  def __init__(		self    ,_A    ,_A=16    ,_A=13    ,_A=7    ,_A=14    ,_A=10    ,_A=19    ,_A=5    ,_A=4    ,_A=True    ,_A=16    ,_A=2    ,_A=4    ,_A=4    ,_A="gelu"    ,_A=0.1    ,_A=0.1    ,_A=[1, 2, 3, 4, 5]    ,_A=25    ,_A=5    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    d_model
      _lowerCAmelCase       :   int  =    parent
      _lowerCAmelCase       :   str  =    batch_size
      _lowerCAmelCase       :   Union[str, Any]  =    prediction_length
      _lowerCAmelCase       :   Union[str, Any]  =    context_length
      _lowerCAmelCase       :   List[Any]  =    cardinality
      _lowerCAmelCase       :   int  =    num_time_features
      _lowerCAmelCase       :   Tuple  =    lags_sequence
      _lowerCAmelCase       :   Union[str, Any]  =    embedding_dimension
      _lowerCAmelCase       :   Optional[Any]  =    is_training
      _lowerCAmelCase       :   Optional[Any]  =    hidden_size
      _lowerCAmelCase       :   Tuple  =    num_hidden_layers
      _lowerCAmelCase       :   Any  =    num_attention_heads
      _lowerCAmelCase       :   Union[str, Any]  =    intermediate_size
      _lowerCAmelCase       :   int  =    hidden_act
      _lowerCAmelCase       :   int  =    hidden_dropout_prob
      _lowerCAmelCase       :   Dict  =    attention_probs_dropout_prob

      _lowerCAmelCase       :   str  =    context_length
      _lowerCAmelCase       :   Any  =    prediction_length + label_length
      _lowerCAmelCase       :   Any  =    label_length

      _lowerCAmelCase       :   Tuple  =    moving_average
      _lowerCAmelCase       :   Tuple  =    autocorrelation_factor

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return AutoformerConfig(
          d_model=self.d_model    ,encoder_layers=self.num_hidden_layers    ,decoder_layers=self.num_hidden_layers    ,encoder_attention_heads=self.num_attention_heads    ,decoder_attention_heads=self.num_attention_heads    ,encoder_ffn_dim=self.intermediate_size    ,decoder_ffn_dim=self.intermediate_size    ,dropout=self.hidden_dropout_prob    ,attention_dropout=self.attention_probs_dropout_prob    ,prediction_length=self.prediction_length    ,context_length=self.context_length    ,label_length=self.label_length    ,lags_sequence=self.lags_sequence    ,num_time_features=self.num_time_features    ,num_static_categorical_features=1    ,cardinality=[self.cardinality]    ,embedding_dimension=[self.embedding_dimension]    ,moving_average=self.moving_average    ,)

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    config.context_length + max(config.lags_sequence      )

      _lowerCAmelCase       :   Optional[int]  =    ids_tensor([self.batch_size, 1]    ,config.cardinality[0]      )
      _lowerCAmelCase       :   int  =    floats_tensor([self.batch_size, _past_length, config.num_time_features]      )
      _lowerCAmelCase       :   Tuple  =    floats_tensor([self.batch_size, _past_length]      )
      _lowerCAmelCase       :   str  =    floats_tensor([self.batch_size, _past_length]      ) > 0.5

      # decoder inputs
      _lowerCAmelCase       :   int  =    floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]      )
      _lowerCAmelCase       :   List[Any]  =    floats_tensor([self.batch_size, config.prediction_length]      )

      _lowerCAmelCase       :   Optional[Any]  =    {
          'past_values': past_values,
          'static_categorical_features': static_categorical_features,
          'past_time_features': past_time_features,
          'past_observed_mask': past_observed_mask,
          'future_time_features': future_time_features,
          'future_values': future_values,
      }
      return inputs_dict

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    self.get_config()
      _lowerCAmelCase       :   Dict  =    self.prepare_autoformer_inputs_dict(_A      )
      return config, inputs_dict

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase,					_lowerCAmelCase       :   Optional[int]  =    self.prepare_config_and_inputs()
      return config, inputs_dict







  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    AutoformerModel(config=_A      ).to(_A      ).eval()
      _lowerCAmelCase       :   Tuple  =    model(**_A      )

      _lowerCAmelCase       :   Any  =    outputs.encoder_last_hidden_state
      _lowerCAmelCase       :   List[Any]  =    outputs.last_hidden_state

      with tempfile.TemporaryDirectory() as tmpdirname:
          _lowerCAmelCase       :   Union[str, Any]  =    model.get_encoder()
          encoder.save_pretrained(_A      )
          _lowerCAmelCase       :   List[Any]  =    AutoformerEncoder.from_pretrained(_A      ).to(_A      )

      _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   Dict  =    model.create_network_inputs(**_A      )
      _lowerCAmelCase,					_lowerCAmelCase       :   List[Any]  =    model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]      )

      _lowerCAmelCase       :   Tuple  =    torch.cat(
          (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])    ,dim=-1    ,)
      _lowerCAmelCase       :   List[str]  =    encoder(inputs_embeds=_A      )[0]
      self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3      )

      _lowerCAmelCase       :   str  =    (
          torch.mean(transformer_inputs[:, : config.context_length, ...]    ,dim=1      )
          .unsqueeze(1      )
          .repeat(1    ,config.prediction_length    ,1      )
      )
      _lowerCAmelCase       :   str  =    torch.zeros(
          [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]]    ,device=enc_input.device    ,)

      _lowerCAmelCase       :   List[str]  =    torch.cat(
          (
              torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)    ,dim=1      ),
              feature[:, config.context_length - config.label_length :, ...],
          )    ,dim=-1    ,)
      _lowerCAmelCase       :   str  =    torch.cat(
          (
              torch.cat((trend_input[:, -config.label_length :, ...], mean)    ,dim=1      ),
              feature[:, config.context_length - config.label_length :, ...],
          )    ,dim=-1    ,)

      with tempfile.TemporaryDirectory() as tmpdirname:
          _lowerCAmelCase       :   Dict  =    model.get_decoder()
          decoder.save_pretrained(_A      )
          _lowerCAmelCase       :   Dict  =    AutoformerDecoder.from_pretrained(_A      ).to(_A      )

      _lowerCAmelCase       :   Tuple  =    decoder(
          trend=_A    ,inputs_embeds=_A    ,encoder_hidden_states=_A    ,)[0]

      self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3      )







@require_torch
class 	__UpperCamelCase    (    a__							,							a__							,							unittest.TestCase						):
  _UpperCAmelCase          =		(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
  _UpperCAmelCase          =		(AutoformerForPrediction,) if is_torch_available() else ()
  _UpperCAmelCase          =		{"feature-extraction": AutoformerModel} if is_torch_available() else {}
  _UpperCAmelCase          =		False
  _UpperCAmelCase          =		False
  _UpperCAmelCase          =		False
  _UpperCAmelCase          =		False
  _UpperCAmelCase          =		False
  _UpperCAmelCase          =		False

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    AutoformerModelTester(self      )
      _lowerCAmelCase       :   List[Any]  =    ConfigTester(self    ,config_class=_A    ,has_text_modality=_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      self.config_tester.run_common_tests()

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase,					_lowerCAmelCase       :   Tuple  =    self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
          _lowerCAmelCase       :   Dict  =    model_class(_A      )

          with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_pretrained(_A      )
              _lowerCAmelCase,					_lowerCAmelCase       :   Optional[Any]  =    model_class.from_pretrained(_A    ,output_loading_info=_A      )
          self.assertEqual(info['missing_keys']    ,[]      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_encoder_decoder_model_standalone(*_A      )

  @unittest.skip(reason='Model has no tokens embeddings'      )
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      pass

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    inspect.signature(getattr(_A    ,'forward'      )      )
      # The main input is the name of the argument after `self`
      _lowerCAmelCase       :   str  =    list(model_signature.parameters.keys()      )[1]
      self.assertEqual(AutoformerModel.main_input_name    ,_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase,					_lowerCAmelCase       :   Union[str, Any]  =    self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          _lowerCAmelCase       :   Optional[int]  =    model_class(_A      )
          _lowerCAmelCase       :   int  =    inspect.signature(model.forward      )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          _lowerCAmelCase       :   Optional[Any]  =    [*signature.parameters.keys()]

          _lowerCAmelCase       :   Dict  =    [
              'past_values',
              'past_time_features',
              'past_observed_mask',
              'static_categorical_features',
              'static_real_features',
              'future_values',
              'future_time_features',
          ]

          if model.__class__.__name__ in ["AutoformerForPrediction"]:
              expected_arg_names.append('future_observed_mask'      )

          expected_arg_names.extend(
              [
                  'decoder_attention_mask',
                  'head_mask',
                  'decoder_head_mask',
                  'cross_attn_head_mask',
                  'encoder_outputs',
                  'past_key_values',
                  'output_hidden_states',
                  'output_attentions',
                  'use_cache',
                  'return_dict',
              ]      )

          self.assertListEqual(arg_names[: len(_A      )]    ,_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase,					_lowerCAmelCase       :   Dict  =    self.model_tester.prepare_config_and_inputs_for_common()
      _lowerCAmelCase       :   Any  =    True

      _lowerCAmelCase       :   str  =    getattr(self.model_tester    ,'seq_length'    ,_A      )
      _lowerCAmelCase       :   List[str]  =    getattr(self.model_tester    ,'decoder_seq_length'    ,_A      )
      _lowerCAmelCase       :   int  =    getattr(self.model_tester    ,'encoder_seq_length'    ,_A      )
      _lowerCAmelCase       :   List[Any]  =    getattr(self.model_tester    ,'d_model'    ,_A      )
      _lowerCAmelCase       :   str  =    getattr(self.model_tester    ,'num_attention_heads'    ,_A      )
      _lowerCAmelCase       :   Optional[Any]  =    d_model // num_attention_heads

      for model_class in self.all_model_classes:
          _lowerCAmelCase       :   Any  =    True
          _lowerCAmelCase       :   Any  =    False
          _lowerCAmelCase       :   int  =    True
          _lowerCAmelCase       :   int  =    model_class(_A      )
          model.to(_A      )
          model.eval()
          with torch.no_grad():
              _lowerCAmelCase       :   Union[str, Any]  =    model(**self._prepare_for_class(_A    ,_A      )      )
          _lowerCAmelCase       :   Any  =    outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
          self.assertEqual(len(_A      )    ,self.model_tester.num_hidden_layers      )

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          _lowerCAmelCase       :   int  =    True
          _lowerCAmelCase       :   List[str]  =    model_class(_A      )
          model.to(_A      )
          model.eval()
          with torch.no_grad():
              _lowerCAmelCase       :   Optional[Any]  =    model(**self._prepare_for_class(_A    ,_A      )      )
          _lowerCAmelCase       :   List[Any]  =    outputs.encoder_attentions
          self.assertEqual(len(_A      )    ,self.model_tester.num_hidden_layers      )

          self.assertListEqual(
              list(attentions[0].shape[-3:]      )    ,[self.model_tester.num_attention_heads, encoder_seq_length, dim]    ,)
          _lowerCAmelCase       :   Union[str, Any]  =    len(_A      )

          _lowerCAmelCase       :   Union[str, Any]  =    7

          if "last_hidden_state" in outputs:
              correct_outlen += 1

          if "trend" in outputs:
              correct_outlen += 1

          if "past_key_values" in outputs:
              correct_outlen += 1  # past_key_values have been returned

          if "loss" in outputs:
              correct_outlen += 1

          if "params" in outputs:
              correct_outlen += 1

          self.assertEqual(_A    ,_A      )

          # decoder attentions
          _lowerCAmelCase       :   Dict  =    outputs.decoder_attentions
          self.assertIsInstance(_A    ,(list, tuple)      )
          self.assertEqual(len(_A      )    ,self.model_tester.num_hidden_layers      )
          self.assertListEqual(
              list(decoder_attentions[0].shape[-3:]      )    ,[self.model_tester.num_attention_heads, decoder_seq_length, dim]    ,)

          # cross attentions
          _lowerCAmelCase       :   Any  =    outputs.cross_attentions
          self.assertIsInstance(_A    ,(list, tuple)      )
          self.assertEqual(len(_A      )    ,self.model_tester.num_hidden_layers      )
          self.assertListEqual(
              list(cross_attentions[0].shape[-3:]      )    ,[self.model_tester.num_attention_heads, decoder_seq_length, dim]    ,)

      # Check attention is always last and order is fine
      _lowerCAmelCase       :   str  =    True
      _lowerCAmelCase       :   List[Any]  =    True
      _lowerCAmelCase       :   Union[str, Any]  =    model_class(_A      )
      model.to(_A      )
      model.eval()
      with torch.no_grad():
          _lowerCAmelCase       :   Dict  =    model(**self._prepare_for_class(_A    ,_A      )      )

      self.assertEqual(out_len + 2    ,len(_A      )      )

      _lowerCAmelCase       :   str  =    outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

      self.assertEqual(len(_A      )    ,self.model_tester.num_hidden_layers      )
      self.assertListEqual(
          list(self_attentions[0].shape[-3:]      )    ,[self.model_tester.num_attention_heads, encoder_seq_length, dim]    ,)







  @is_flaky()
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      super().test_retain_grad_hidden_states_attentions()





def 		lowerCamelCase__		(					_lowerCamelCase="train-batch.pt"   ):

    '''simple docstring'''



    _lowerCAmelCase       :   int  =    hf_hub_download(repo_id='hf-internal-testing/tourism-monthly-batch' ,   filename=_lowerCamelCase ,   repo_type='dataset'   )
    _lowerCAmelCase       :   Optional[Any]  =    torch.load(_lowerCamelCase ,   map_location=_lowerCamelCase   )
    return batch







@require_torch
@slow
class 	__UpperCamelCase    (    unittest.TestCase						):

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    AutoformerModel.from_pretrained('huggingface/autoformer-tourism-monthly'      ).to(_A      )
      _lowerCAmelCase       :   Dict  =    prepare_batch()

      with torch.no_grad():
          _lowerCAmelCase       :   Union[str, Any]  =    model(
              past_values=batch['past_values']    ,past_time_features=batch['past_time_features']    ,past_observed_mask=batch['past_observed_mask']    ,static_categorical_features=batch['static_categorical_features']    ,future_values=batch['future_values']    ,future_time_features=batch['future_time_features']    ,)[0]

      _lowerCAmelCase       :   Any  =    torch.Size(
          (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)      )
      self.assertEqual(output.shape    ,_A      )

      _lowerCAmelCase       :   Dict  =    torch.tensor(
          [[0.3_5_9_3, -1.3_3_9_8, 0.6_3_3_0], [0.2_2_7_9, 1.5_3_9_6, -0.1_7_9_2], [0.0_4_5_0, 1.3_2_2_5, -0.2_3_3_5]]    ,device=_A      )
      self.assertTrue(torch.allclose(output[0, :3, :3]    ,_A    ,atol=_A      )      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly'      ).to(_A      )
      _lowerCAmelCase       :   Any  =    prepare_batch('val-batch.pt'      )
      with torch.no_grad():
          _lowerCAmelCase       :   List[str]  =    model(
              past_values=batch['past_values']    ,past_time_features=batch['past_time_features']    ,past_observed_mask=batch['past_observed_mask']    ,static_categorical_features=batch['static_categorical_features']    ,).encoder_last_hidden_state
      _lowerCAmelCase       :   Dict  =    torch.Size((64, model.config.context_length, model.config.d_model)      )
      self.assertEqual(output.shape    ,_A      )

      _lowerCAmelCase       :   Tuple  =    torch.tensor(
          [[-0.0_7_3_4, -0.9_0_3_6, 0.8_3_5_8], [4.7_1_8_6, 2.4_1_1_3, 1.9_5_8_1], [1.7_9_5_3, 2.3_5_5_8, 1.2_9_7_0]]    ,device=_A      )
      self.assertTrue(torch.allclose(output[0, :3, :3]    ,_A    ,atol=_A      )      )







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly'      ).to(_A      )
      _lowerCAmelCase       :   int  =    prepare_batch('val-batch.pt'      )
      with torch.no_grad():
          _lowerCAmelCase       :   Any  =    model.generate(
              static_categorical_features=batch['static_categorical_features']    ,past_time_features=batch['past_time_features']    ,past_values=batch['past_values']    ,future_time_features=batch['future_time_features']    ,past_observed_mask=batch['past_observed_mask']    ,)
      _lowerCAmelCase       :   List[str]  =    torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)      )
      self.assertEqual(outputs.sequences.shape    ,_A      )

      _lowerCAmelCase       :   Dict  =    torch.tensor([3_1_3_0.6_7_6_3, 4_0_5_6.5_2_9_3, 7_0_5_3.0_7_8_6]    ,device=_A      )
      _lowerCAmelCase       :   Optional[int]  =    outputs.sequences.mean(dim=1      )
      self.assertTrue(torch.allclose(mean_prediction[0, -3:]    ,_A    ,rtol=1E-1      )      )








"""simple docstring"""






import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    if (
        (cp >= 0X4E00 and cp <= 0X9FFF)
        or (cp >= 0X3400 and cp <= 0X4DBF)  #
        or (cp >= 0X20000 and cp <= 0X2A6DF)  #
        or (cp >= 0X2A700 and cp <= 0X2B73F)  #
        or (cp >= 0X2B740 and cp <= 0X2B81F)  #
        or (cp >= 0X2B820 and cp <= 0X2CEAF)  #
        or (cp >= 0XF900 and cp <= 0XFAFF)
        or (cp >= 0X2F800 and cp <= 0X2FA1F)  #
    ):  #
        return True

    return False

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    for char in word:
        _lowerCAmelCase       :   Dict  =    ord(_lowerCamelCase   )
        if not _is_chinese_char(_lowerCamelCase   ):
            return 0
    return 1

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Tuple  =    set()

    for token in tokens:
        _lowerCAmelCase       :   Optional[int]  =    len(_lowerCamelCase   ) > 1 and is_chinese(_lowerCamelCase   )
        if chinese_word:
            word_set.add(_lowerCamelCase   )
    _lowerCAmelCase       :   Union[str, Any]  =    list(_lowerCamelCase   )
    return word_list

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    if not chinese_word_set:
        return bert_tokens
    _lowerCAmelCase       :   Optional[Any]  =    max([len(_lowerCamelCase   ) for w in chinese_word_set]   )

    _lowerCAmelCase       :   str  =    bert_tokens
    _lowerCAmelCase,					_lowerCAmelCase       :   Optional[Any]  =    0, len(_lowerCamelCase   )
    while start < end:
        _lowerCAmelCase       :   Dict  =    True
        if is_chinese(bert_word[start]   ):
            _lowerCAmelCase       :   str  =    min(end - start ,   _lowerCamelCase   )
            for i in range(_lowerCamelCase ,   1 ,   -1   ):
                _lowerCAmelCase       :   List[Any]  =    ''.join(bert_word[start : start + i]   )
                if whole_word in chinese_word_set:
                    for j in range(start + 1 ,   start + i   ):
                        _lowerCAmelCase       :   Tuple  =    '##' + bert_word[j]
                    _lowerCAmelCase       :   Optional[int]  =    start + i
                    _lowerCAmelCase       :   Any  =    False
                    break
        if single_word:
            start += 1
    return bert_word

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Dict  =    []

    for i in range(0 ,   len(_lowerCamelCase   ) ,   100   ):
        _lowerCAmelCase       :   Tuple  =    ltp_tokenizer.seg(lines[i : i + 100]   )[0]
        _lowerCAmelCase       :   List[Any]  =    [get_chinese_word(_lowerCamelCase   ) for r in res]
        ltp_res.extend(_lowerCamelCase   )
    assert len(_lowerCamelCase   ) == len(_lowerCamelCase   )

    _lowerCAmelCase       :   int  =    []
    for i in range(0 ,   len(_lowerCamelCase   ) ,   100   ):
        _lowerCAmelCase       :   Dict  =    bert_tokenizer(lines[i : i + 100] ,   add_special_tokens=_lowerCamelCase ,   truncation=_lowerCamelCase ,   max_length=512   )
        bert_res.extend(res['input_ids']   )
    assert len(_lowerCamelCase   ) == len(_lowerCamelCase   )

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

    assert len(_lowerCamelCase   ) == len(_lowerCamelCase   )

    return ref_ids

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



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

    _lowerCAmelCase       :   Optional[Any]  =    prepare_ref(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   )

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


if __name__ == "__main__":
 _lowerCAmelCase						=		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""")

 _lowerCAmelCase						=		parser.parse_args()
 main(args)





