






"""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()
_SCREAMING_SNAKE_CASE					:				int					    =						logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE					:				Any					    =						['''model.decoder.embed_positions.weights''']







def      lowerCamelCase__       (							_lowerCamelCase       :		Dict )      ->				Optional[Any]:
  if "emb" in name:
    lowerCamelCase_         =						name.replace('emb'		,				'model.decoder.embed_tokens' )
  if "transformer" in name:
    lowerCamelCase_         =						name.replace('transformer'		,				'model.decoder' )
  if "cross_attention" in name:
    lowerCamelCase_         =						name.replace('cross_attention'		,				'encoder_attn' )
  if "linear1" in name:
    lowerCamelCase_         =						name.replace('linear1'		,				'fc1' )
  if "linear2" in name:
    lowerCamelCase_         =						name.replace('linear2'		,				'fc2' )
  if "norm1" in name:
    lowerCamelCase_         =						name.replace('norm1'		,				'self_attn_layer_norm' )
  if "norm_cross" in name:
    lowerCamelCase_         =						name.replace('norm_cross'		,				'encoder_attn_layer_norm' )
  if "norm2" in name:
    lowerCamelCase_         =						name.replace('norm2'		,				'final_layer_norm' )
  if "out_norm" in name:
    lowerCamelCase_         =						name.replace('out_norm'		,				'model.decoder.layer_norm' )
  if "linears" in name:
    lowerCamelCase_         =						name.replace('linears'		,				'lm_heads' )
  if "condition_provider.conditioners.description.output_proj" in name:
    lowerCamelCase_         =						name.replace('condition_provider.conditioners.description.output_proj'		,				'enc_to_dec_proj' )
  return name







def      lowerCamelCase__       (							_lowerCamelCase       :		OrderedDict		,				_lowerCamelCase       :		int )      ->				Tuple[Dict, Dict]:
  lowerCamelCase_         =						list(state_dict.keys() )
  lowerCamelCase_         =						{}
  for key in keys:
    lowerCamelCase_         =						state_dict.pop(_lowerCamelCase )
    lowerCamelCase_         =						rename_keys(_lowerCamelCase )
    if "in_proj_weight" in key:
      # split fused qkv proj
      lowerCamelCase_         =						val[:hidden_size, :]
      lowerCamelCase_         =						val[hidden_size : 2 * hidden_size, :]
      lowerCamelCase_         =						val[-hidden_size:, :]
    elif "enc_to_dec_proj" in key:
      lowerCamelCase_         =						val
    else:
      lowerCamelCase_         =						val
  return state_dict, enc_dec_proj_state_dict







def      lowerCamelCase__       (							_lowerCamelCase       :		str )      ->				MusicgenDecoderConfig:
  if checkpoint == "small":
    # default config values
    lowerCamelCase_         =						1024
    lowerCamelCase_         =						24
    lowerCamelCase_         =						16
  elif checkpoint == "medium":
    lowerCamelCase_         =						1536
    lowerCamelCase_         =						48
    lowerCamelCase_         =						24
  elif checkpoint == "large":
    lowerCamelCase_         =						2048
    lowerCamelCase_         =						48
    lowerCamelCase_         =						32
  else:
    raise ValueError(F'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.''' )
  lowerCamelCase_         =						MusicgenDecoderConfig(
      hidden_size=_lowerCamelCase		,				ffn_dim=hidden_size * 4		,				num_hidden_layers=_lowerCamelCase		,				num_attention_heads=_lowerCamelCase		,				)
  return config







@torch.no_grad()
def      lowerCamelCase__       (							_lowerCamelCase       :		Optional[Any]		,				_lowerCamelCase       :		Any=None		,				_lowerCamelCase       :		int=None		,				_lowerCamelCase       :		Optional[Any]="cpu" )      ->				Dict:
  lowerCamelCase_         =						MusicGen.get_pretrained(_lowerCamelCase		,				device=_lowerCamelCase )
  lowerCamelCase_         =						decoder_config_from_checkpoint(_lowerCamelCase )

  lowerCamelCase_         =						fairseq_model.lm.state_dict()
  lowerCamelCase_     ,	lowerCamelCase_         =						rename_state_dict(
      _lowerCamelCase		,				hidden_size=decoder_config.hidden_size )

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

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

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

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

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

  # init the composite model
  lowerCamelCase_         =						MusicgenForConditionalGeneration(text_encoder=_lowerCamelCase		,				audio_encoder=_lowerCamelCase		,				decoder=_lowerCamelCase )

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

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

  with torch.no_grad():
    lowerCamelCase_         =						model(input_ids=_lowerCamelCase		,				decoder_input_ids=_lowerCamelCase ).logits

  if logits.shape != (8, 1, 2048):
    raise ValueError('Incorrect shape for logits' )

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

  lowerCamelCase_         =						MusicgenProcessor(feature_extractor=_lowerCamelCase		,				tokenizer=_lowerCamelCase )

  # set the appropriate bos/pad token ids
  lowerCamelCase_         =						2048
  lowerCamelCase_         =						2048

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

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

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


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE					:				Tuple					    =						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.'''
  )

  _SCREAMING_SNAKE_CASE					:				Optional[Any]					    =						parser.parse_args()
  convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)












"""simple docstring"""





import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing






class      a						(					__snake_case		):






    def __init__(       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							str = "▁"       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						__SCREAMING_SNAKE_CASE     :							Union[str, AddedToken] = "<unk>"       ,						__SCREAMING_SNAKE_CASE     :							Union[str, AddedToken] = "</s>"       ,						__SCREAMING_SNAKE_CASE     :							Union[str, AddedToken] = "<pad>"       ,						)     ->		Optional[int]:
      lowerCamelCase_         =						{
          'pad': {'id': 0, 'token': pad_token},
          'eos': {'id': 1, 'token': eos_token},
          'unk': {'id': 2, 'token': unk_token},
      }

      lowerCamelCase_         =						[None] * len(self.special_tokens					)
      for token_dict in self.special_tokens.values():
        lowerCamelCase_         =						token_dict['token']

      lowerCamelCase_         =						Tokenizer(Unigram()					)

      lowerCamelCase_         =						normalizers.Sequence(
          [
              normalizers.Nmt(),
              normalizers.NFKC(),
              normalizers.Replace(Regex(' {2,}'					)       ,						' '					),
              normalizers.Lowercase(),
          ]					)
      lowerCamelCase_         =						pre_tokenizers.Sequence(
          [
              pre_tokenizers.Metaspace(replacement=__SCREAMING_SNAKE_CASE       ,						add_prefix_space=__SCREAMING_SNAKE_CASE					),
              pre_tokenizers.Digits(individual_digits=__SCREAMING_SNAKE_CASE					),
              pre_tokenizers.Punctuation(),
          ]					)
      lowerCamelCase_         =						decoders.Metaspace(replacement=__SCREAMING_SNAKE_CASE       ,						add_prefix_space=__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						TemplateProcessing(
          single=F'''$A {self.special_tokens["eos"]["token"]}'''       ,						special_tokens=[(self.special_tokens['eos']['token'], self.special_tokens['eos']['id'])]       ,						)

      lowerCamelCase_         =						{
          'model': 'SentencePieceUnigram',
          'replacement': replacement,
          'add_prefix_space': add_prefix_space,
      }

      super().__init__(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Union[str, List[str]]       ,						__SCREAMING_SNAKE_CASE     :							int = 8000       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						)     ->		Tuple:
      lowerCamelCase_         =						trainers.UnigramTrainer(
          vocab_size=__SCREAMING_SNAKE_CASE       ,						special_tokens=self.special_tokens_list       ,						show_progress=__SCREAMING_SNAKE_CASE       ,						)

      if isinstance(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					):
        lowerCamelCase_         =						[files]
      self._tokenizer.train(__SCREAMING_SNAKE_CASE       ,						trainer=__SCREAMING_SNAKE_CASE					)

      self.add_unk_id()






    def    UpperCamelCase       (       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Union[Iterator[str], Iterator[Iterator[str]]]       ,						__SCREAMING_SNAKE_CASE     :							int = 8000       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						)     ->		str:
      lowerCamelCase_         =						trainers.UnigramTrainer(
          vocab_size=__SCREAMING_SNAKE_CASE       ,						special_tokens=self.special_tokens_list       ,						show_progress=__SCREAMING_SNAKE_CASE       ,						)

      self._tokenizer.train_from_iterator(__SCREAMING_SNAKE_CASE       ,						trainer=__SCREAMING_SNAKE_CASE					)

      self.add_unk_id()






    def    UpperCamelCase       (       self     :							List[Any]					)     ->		Dict:
      lowerCamelCase_         =						json.loads(self._tokenizer.to_str()					)

      lowerCamelCase_         =						self.special_tokens['unk']['id']

      lowerCamelCase_         =						Tokenizer.from_str(json.dumps(__SCREAMING_SNAKE_CASE					)					)




