





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()
lowercase_          =		logging.get_logger(__name__)


lowercase_          =		["""model.decoder.embed_positions.weights"""]




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




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




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




@torch.no_grad()
def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE=None		,						_SCREAMING_SNAKE_CASE=None		,						_SCREAMING_SNAKE_CASE="cpu"		)    ->       Optional[int]:
  lowercase__           =  MusicGen.get_pretrained(_SCREAMING_SNAKE_CASE		,						device=_SCREAMING_SNAKE_CASE		)
  lowercase__           =  decoder_config_from_checkpoint(_SCREAMING_SNAKE_CASE		)

  lowercase__           =  fairseq_model.lm.state_dict()
  lowercase__      ,   lowercase__           =  rename_state_dict(
      _SCREAMING_SNAKE_CASE		,						hidden_size=decoder_config.hidden_size		)

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

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

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

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

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

  # init the composite model
  lowercase__           =  MusicgenForConditionalGeneration(text_encoder=_SCREAMING_SNAKE_CASE		,						audio_encoder=_SCREAMING_SNAKE_CASE		,						decoder=_SCREAMING_SNAKE_CASE		)

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

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

  with torch.no_grad():
    lowercase__           =  model(input_ids=_SCREAMING_SNAKE_CASE		,						decoder_input_ids=_SCREAMING_SNAKE_CASE		).logits

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

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

  lowercase__           =  MusicgenProcessor(feature_extractor=_SCREAMING_SNAKE_CASE		,						tokenizer=_SCREAMING_SNAKE_CASE		)

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

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

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

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


if __name__ == "__main__":
      lowercase_          =		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."""
      )

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









from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase				)
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		str			 =	field(default='language-modeling'      ,     metadata={'include_in_asdict_even_if_is_default': True}				)

   _UpperCamelCase   :		ClassVar[Features]			 =	Features({'text': Value('string'				)}				)
   _UpperCamelCase   :		ClassVar[Features]			 =	Features({}				)
   _UpperCamelCase   :		str			 =	"text"


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       Dict[str, str]:





     """simple docstring"""







     return {self.text_column: "text"}


