'''simple docstring'''







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__lowerCamelCase		       =    logging.get_logger(__name__)

__lowerCamelCase		       =    {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			List[Any]:
       for attribute in key.split("""."""				):
              A_						=      getattr(UpperCAmelCase__,       UpperCAmelCase__				)

       if weight_type is not None:
              A_						=      getattr(UpperCAmelCase__,       UpperCAmelCase__				).shape
       else:
              A_						=      hf_pointer.shape

       assert hf_shape == value.shape, (
           F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
           F''' {value.shape} for {full_name}'''
       )

       if weight_type == "weight":
              A_						=      value
       elif weight_type == "weight_g":
              A_						=      value
       elif weight_type == "weight_v":
              A_						=      value
       elif weight_type == "bias":
              A_						=      value
       else:
              A_						=      value

       logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''				)
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			Optional[int]:
       A_						=      []
       A_						=      fairseq_model.state_dict()

       A_						=      hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              A_						=      False
              if "conv_layers" in name:
                     load_conv_layer(
                         UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       hf_model.config.feat_extract_norm == """group""",       )
                     A_						=      True
              else:
                     for key, mapped_key in MAPPING.items():
                            A_						=      """hubert.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

                            if key in name or (key.split("""w2v_model."""				)[-1] == name.split("""."""				)[0] and not is_finetuned):
                                   A_						=      True
                                   if "*" in mapped_key:
                                          A_						=      name.split(UpperCAmelCase__				)[0].split("""."""				)[-2]
                                          A_						=      mapped_key.replace("""*""",       UpperCAmelCase__				)
                                   if "weight_g" in name:
                                          A_						=      """weight_g"""
                                   elif "weight_v" in name:
                                          A_						=      """weight_v"""
                                   elif "weight" in name:
                                          A_						=      """weight"""
                                   elif "bias" in name:
                                          A_						=      """bias"""
                                   else:
                                          A_						=      None
                                   set_recursively(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)
                            continue
              if not is_used:
                     unused_weights.append(UpperCAmelCase__				)

       logger.warning(F'''Unused weights: {unused_weights}'''				)
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			Any:
       A_						=      full_name.split("""conv_layers."""				)[-1]
       A_						=      name.split("""."""				)
       A_						=      int(items[0]				)
       A_						=      int(items[1]				)

       if type_id == 0:
              if "bias" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                         F'''{full_name} has size {value.shape}, but'''
                         F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
                     )
                     A_						=      value
                     logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''				)
              elif "weight" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                         F'''{full_name} has size {value.shape}, but'''
                         F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
                     )
                     A_						=      value
                     logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''				)
       elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
              if "bias" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                         F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
                         " found."
                     )
                     A_						=      value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
              elif "weight" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                         F'''{full_name} has size {value.shape}, but'''
                         F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
                     )
                     A_						=      value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
       else:
              unused_weights.append(UpperCAmelCase__				)
@torch.no_grad()
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__=None,       UpperCAmelCase__=None,       UpperCAmelCase__=True				)			->			List[Any]:
       if config_path is not None:
              A_						=      HubertConfig.from_pretrained(UpperCAmelCase__				)
       else:
              A_						=      HubertConfig()

       if is_finetuned:
              if dict_path:
                     A_						=      Dictionary.load(UpperCAmelCase__				)

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     A_						=      target_dict.pad_index
                     A_						=      target_dict.bos_index
                     A_						=      target_dict.eos_index
                     A_						=      len(target_dict.symbols				)
                     A_						=      os.path.join(UpperCAmelCase__,       """vocab.json"""				)
                     if not os.path.isdir(UpperCAmelCase__				):
                            logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(UpperCAmelCase__				)				)
                            return
                     os.makedirs(UpperCAmelCase__,       exist_ok=UpperCAmelCase__				)
                     with open(UpperCAmelCase__,       """w""",       encoding="""utf-8"""				) as vocab_handle:
                            json.dump(target_dict.indices,       UpperCAmelCase__				)
                     A_						=      WavaVecaCTCTokenizer(
                         UpperCAmelCase__,       unk_token=target_dict.unk_word,       pad_token=target_dict.pad_word,       bos_token=target_dict.bos_word,       eos_token=target_dict.eos_word,       word_delimiter_token="""|""",       do_lower_case=UpperCAmelCase__,       )
                     A_						=      True if config.feat_extract_norm == """layer""" else False
                     A_						=      WavaVecaFeatureExtractor(
                         feature_size=1,       sampling_rate=1_60_00,       padding_value=0,       do_normalize=UpperCAmelCase__,       return_attention_mask=UpperCAmelCase__,       )
                     A_						=      WavaVecaProcessor(feature_extractor=UpperCAmelCase__,       tokenizer=UpperCAmelCase__				)
                     processor.save_pretrained(UpperCAmelCase__				)

              A_						=      HubertForCTC(UpperCAmelCase__				)
       else:
              A_						=      HubertModel(UpperCAmelCase__				)

       if is_finetuned:
              A_					,			A_					,			A_						=      fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path],       arg_overrides={"""data""": """/""".join(dict_path.split("""/"""				)[:-1]				)}				)
       else:
              A_					,			A_					,			A_						=      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				)

       A_						=      model[0].eval()

       recursively_load_weights(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

       hf_wavavec.save_pretrained(UpperCAmelCase__				)


if __name__ == "__main__":
      __lowerCamelCase		       =    argparse.ArgumentParser()
      parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
      parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
      parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
      parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
      parser.add_argument(
          '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
      )
      __lowerCamelCase		       =    parser.parse_args()
      convert_hubert_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )



'''simple docstring'''







import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Optional[int]:
       # 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 >= 0X4E_00 and cp <= 0X9F_FF)
           or (cp >= 0X34_00 and cp <= 0X4D_BF)  #
           or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF)  #
           or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F)  #
           or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F)  #
           or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF)  #
           or (cp >= 0XF9_00 and cp <= 0XFA_FF)
           or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F)  #
       ):  #
              return True

       return False
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			List[Any]:
       # word like '180' or '身高' or '神'
       for char in word:
              A_						=      ord(UpperCAmelCase__				)
              if not _is_chinese_char(UpperCAmelCase__				):
                     return 0
       return 1
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			int:
       A_						=      set()

       for token in tokens:
              A_						=      len(UpperCAmelCase__				) > 1 and is_chinese(UpperCAmelCase__				)
              if chinese_word:
                     word_set.add(UpperCAmelCase__				)
       A_						=      list(UpperCAmelCase__				)
       return word_list
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			Dict:
       if not chinese_word_set:
              return bert_tokens
       A_						=      max([len(UpperCAmelCase__				) for w in chinese_word_set]				)

       A_						=      bert_tokens
       A_					,			A_						=      0, len(UpperCAmelCase__				)
       while start < end:
              A_						=      True
              if is_chinese(bert_word[start]				):
                     A_						=      min(end - start,       UpperCAmelCase__				)
                     for i in range(UpperCAmelCase__,       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       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			Dict:
       A_						=      []

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

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

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

       assert len(UpperCAmelCase__				) == len(UpperCAmelCase__				)

       return ref_ids
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Optional[Any]:
       # 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:
              A_						=      f.readlines()
       A_						=      [line.strip() for line in data if len(UpperCAmelCase__				) > 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(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

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


if __name__ == "__main__":
      __lowerCamelCase		       =    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''',
      )

      __lowerCamelCase		       =    parser.parse_args()
      main(args)
