




"""simple docstring"""

import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple

import torch
from audiocraft.models import MusicGen

from transformers import (
    AutoFeatureExtractor,
    AutoTokenizer,
    EncodecModel,
    MusicgenDecoderConfig,
    MusicgenForConditionalGeneration,
    MusicgenProcessor,
    TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging


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


a         =      ['''model.decoder.embed_positions.weights''']






def 						_snake_case					( _snake_case							: str							)   -> Optional[int]:






      '''simple docstring'''


      if "emb" in name:
            _A        =       name.replace('emb'    ,  'model.decoder.embed_tokens'							)
      if "transformer" in name:
            _A        =       name.replace('transformer'    ,  'model.decoder'							)
      if "cross_attention" in name:
            _A        =       name.replace('cross_attention'    ,  'encoder_attn'							)
      if "linear1" in name:
            _A        =       name.replace('linear1'    ,  'fc1'							)
      if "linear2" in name:
            _A        =       name.replace('linear2'    ,  'fc2'							)
      if "norm1" in name:
            _A        =       name.replace('norm1'    ,  'self_attn_layer_norm'							)
      if "norm_cross" in name:
            _A        =       name.replace('norm_cross'    ,  'encoder_attn_layer_norm'							)
      if "norm2" in name:
            _A        =       name.replace('norm2'    ,  'final_layer_norm'							)
      if "out_norm" in name:
            _A        =       name.replace('out_norm'    ,  'model.decoder.layer_norm'							)
      if "linears" in name:
            _A        =       name.replace('linears'    ,  'lm_heads'							)
      if "condition_provider.conditioners.description.output_proj" in name:
            _A        =       name.replace('condition_provider.conditioners.description.output_proj'    ,  'enc_to_dec_proj'							)
      return name






def 						_snake_case					( _snake_case							: OrderedDict    ,  _snake_case							: int							)   -> Tuple[Dict, Dict]:






      '''simple docstring'''


      _A        =       list(state_dict.keys()							)
      _A        =       {}
      for key in keys:
            _A        =       state_dict.pop(_snake_case							)
            _A        =       rename_keys(_snake_case							)
            if "in_proj_weight" in key:
                  # split fused qkv proj
                  _A        =       val[:hidden_size, :]
                  _A        =       val[hidden_size : 2 * hidden_size, :]
                  _A        =       val[-hidden_size:, :]
            elif "enc_to_dec_proj" in key:
                  _A        =       val
            else:
                  _A        =       val
      return state_dict, enc_dec_proj_state_dict






def 						_snake_case					( _snake_case							: str							)   -> MusicgenDecoderConfig:






      '''simple docstring'''


      if checkpoint == "small":
            # default config values
            _A        =       10_24
            _A        =       24
            _A        =       16
      elif checkpoint == "medium":
            _A        =       15_36
            _A        =       48
            _A        =       24
      elif checkpoint == "large":
            _A        =       20_48
            _A        =       48
            _A        =       32
      else:
            raise ValueError(F'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'''							)
      _A        =       MusicgenDecoderConfig(
          hidden_size=_snake_case    ,  ffn_dim=hidden_size * 4    ,  num_hidden_layers=_snake_case    ,  num_attention_heads=_snake_case    ,  )
      return config






@torch.no_grad()
def 						_snake_case					( _snake_case							: Dict    ,  _snake_case							: Any=None    ,  _snake_case							: str=None    ,  _snake_case							: Union[str, Any]="cpu"							)   -> int:






      '''simple docstring'''


      _A        =       MusicGen.get_pretrained(_snake_case    ,  device=_snake_case							)
      _A        =       decoder_config_from_checkpoint(_snake_case							)

      _A        =       fairseq_model.lm.state_dict()
      _A       ,   _A        =       rename_state_dict(
          _snake_case    ,  hidden_size=decoder_config.hidden_size							)

      _A        =       TaEncoderModel.from_pretrained('t5-base'							)
      _A        =       EncodecModel.from_pretrained('facebook/encodec_32khz'							)
      _A        =       MusicgenForCausalLM(_snake_case							).eval()

      # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
      _A       ,   _A        =       decoder.load_state_dict(_snake_case    ,  strict=_snake_case							)

      for key in missing_keys.copy():
            if key.startswith(('text_encoder', 'audio_encoder')							) or key in EXPECTED_MISSING_KEYS:
                  missing_keys.remove(_snake_case							)

      if len(_snake_case							) > 0:
            raise ValueError(F'''Missing key(s) in state_dict: {missing_keys}'''							)

      if len(_snake_case							) > 0:
            raise ValueError(F'''Unexpected key(s) in state_dict: {unexpected_keys}'''							)

      # init the composite model
      _A        =       MusicgenForConditionalGeneration(text_encoder=_snake_case    ,  audio_encoder=_snake_case    ,  decoder=_snake_case							)

      # load the pre-trained enc-dec projection (from the decoder state dict)
      model.enc_to_dec_proj.load_state_dict(_snake_case							)

      # check we can do a forward pass
      _A        =       torch.arange(0    ,  8    ,  dtype=torch.long							).reshape(2    ,  -1							)
      _A        =       input_ids.reshape(2 * 4    ,  -1							)

      with torch.no_grad():
            _A        =       model(input_ids=_snake_case    ,  decoder_input_ids=_snake_case							).logits

      if logits.shape != (8, 1, 20_48):
            raise ValueError('Incorrect shape for logits'							)

      # now construct the processor
      _A        =       AutoTokenizer.from_pretrained('t5-base'							)
      _A        =       AutoFeatureExtractor.from_pretrained('facebook/encodec_32khz'    ,  padding_side='left'							)

      _A        =       MusicgenProcessor(feature_extractor=_snake_case    ,  tokenizer=_snake_case							)

      # set the appropriate bos/pad token ids
      _A        =       20_48
      _A        =       20_48

      # set other default generation config params
      _A        =       int(30 * audio_encoder.config.frame_rate							)
      _A        =       True
      _A        =       3.0

      if pytorch_dump_folder is not None:
            Path(_snake_case							).mkdir(exist_ok=_snake_case							)
            logger.info(F'''Saving model {checkpoint} to {pytorch_dump_folder}'''							)
            model.save_pretrained(_snake_case							)
            processor.save_pretrained(_snake_case							)

      if repo_id:
            logger.info(F'''Pushing model {checkpoint} to {repo_id}'''							)
            model.push_to_hub(_snake_case							)
            processor.push_to_hub(_snake_case							)


if __name__ == "__main__":
       a         =      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--checkpoint''',
           default='''small''',
           type=str,
           help='''Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.''',
       )
       parser.add_argument(
           '''--pytorch_dump_folder''',
           required=True,
           default=None,
           type=str,
           help='''Path to the output PyTorch model directory.''',
       )
       parser.add_argument(
           '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
       )
       parser.add_argument(
           '''--device''', default='''cpu''', type=str, help='''Torch device to run the conversion, either cpu or cuda.'''
       )

       a         =      parser.parse_args()
       convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)







"""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)

