

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()
__UpperCAmelCase    				=     logging.get_logger(__name__)


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







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      List[Any]:

     """simple docstring"""







     if "emb" in name:
          _lowerCAmelCase   :					Tuple        =    name.replace("emb"		,							"model.decoder.embed_tokens"				)
     if "transformer" in name:
          _lowerCAmelCase   :					Optional[int]        =    name.replace("transformer"		,							"model.decoder"				)
     if "cross_attention" in name:
          _lowerCAmelCase   :					Optional[int]        =    name.replace("cross_attention"		,							"encoder_attn"				)
     if "linear1" in name:
          _lowerCAmelCase   :					Union[str, Any]        =    name.replace("linear1"		,							"fc1"				)
     if "linear2" in name:
          _lowerCAmelCase   :					List[Any]        =    name.replace("linear2"		,							"fc2"				)
     if "norm1" in name:
          _lowerCAmelCase   :					List[str]        =    name.replace("norm1"		,							"self_attn_layer_norm"				)
     if "norm_cross" in name:
          _lowerCAmelCase   :					int        =    name.replace("norm_cross"		,							"encoder_attn_layer_norm"				)
     if "norm2" in name:
          _lowerCAmelCase   :					int        =    name.replace("norm2"		,							"final_layer_norm"				)
     if "out_norm" in name:
          _lowerCAmelCase   :					Optional[Any]        =    name.replace("out_norm"		,							"model.decoder.layer_norm"				)
     if "linears" in name:
          _lowerCAmelCase   :					Any        =    name.replace("linears"		,							"lm_heads"				)
     if "condition_provider.conditioners.description.output_proj" in name:
          _lowerCAmelCase   :					Optional[int]        =    name.replace("condition_provider.conditioners.description.output_proj"		,							"enc_to_dec_proj"				)
     return name







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

     """simple docstring"""







     _lowerCAmelCase   :					List[str]        =    list(state_dict.keys()				)
     _lowerCAmelCase   :					str        =    {}
     for key in keys:
          _lowerCAmelCase   :					Union[str, Any]        =    state_dict.pop(SCREAMING_SNAKE_CASE				)
          _lowerCAmelCase   :					int        =    rename_keys(SCREAMING_SNAKE_CASE				)
          if "in_proj_weight" in key:
               # split fused qkv proj
               _lowerCAmelCase   :					Dict        =    val[:hidden_size, :]
               _lowerCAmelCase   :					List[str]        =    val[hidden_size : 2 * hidden_size, :]
               _lowerCAmelCase   :					Tuple        =    val[-hidden_size:, :]
          elif "enc_to_dec_proj" in key:
               _lowerCAmelCase   :					str        =    val
          else:
               _lowerCAmelCase   :					Any        =    val
     return state_dict, enc_dec_proj_state_dict







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      MusicgenDecoderConfig:

     """simple docstring"""







     if checkpoint == "small":
          # default config values
          _lowerCAmelCase   :					str        =    1024
          _lowerCAmelCase   :					List[str]        =    24
          _lowerCAmelCase   :					Optional[int]        =    16
     elif checkpoint == "medium":
          _lowerCAmelCase   :					str        =    1536
          _lowerCAmelCase   :					List[Any]        =    48
          _lowerCAmelCase   :					Any        =    24
     elif checkpoint == "large":
          _lowerCAmelCase   :					int        =    2048
          _lowerCAmelCase   :					Tuple        =    48
          _lowerCAmelCase   :					List[Any]        =    32
     else:
          raise ValueError(f'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'''				)
     _lowerCAmelCase   :					List[str]        =    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 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=None		,							SCREAMING_SNAKE_CASE=None		,							SCREAMING_SNAKE_CASE="cpu"				)       ->      Tuple:

     """simple docstring"""







     _lowerCAmelCase   :					Union[str, Any]        =    MusicGen.get_pretrained(SCREAMING_SNAKE_CASE		,							device=SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Optional[Any]        =    decoder_config_from_checkpoint(SCREAMING_SNAKE_CASE				)

     _lowerCAmelCase   :					Optional[Any]        =    fairseq_model.lm.state_dict()
     _lowerCAmelCase      ,			_lowerCAmelCase   :					List[str]        =    rename_state_dict(
         SCREAMING_SNAKE_CASE		,							hidden_size=decoder_config.hidden_size				)

     _lowerCAmelCase   :					Dict        =    TaEncoderModel.from_pretrained("t5-base"				)
     _lowerCAmelCase   :					Optional[int]        =    EncodecModel.from_pretrained("facebook/encodec_32khz"				)
     _lowerCAmelCase   :					str        =    MusicgenForCausalLM(SCREAMING_SNAKE_CASE				).eval()

     # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
     _lowerCAmelCase      ,			_lowerCAmelCase   :					int        =    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
     _lowerCAmelCase   :					Tuple        =    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
     _lowerCAmelCase   :					Union[str, Any]        =    torch.arange(0		,							8		,							dtype=torch.long				).reshape(2		,							-1				)
     _lowerCAmelCase   :					List[str]        =    input_ids.reshape(2 * 4		,							-1				)

     with torch.no_grad():
          _lowerCAmelCase   :					List[str]        =    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
     _lowerCAmelCase   :					int        =    AutoTokenizer.from_pretrained("t5-base"				)
     _lowerCAmelCase   :					List[str]        =    AutoFeatureExtractor.from_pretrained("facebook/encodec_32khz"		,							padding_side="left"				)

     _lowerCAmelCase   :					int        =    MusicgenProcessor(feature_extractor=SCREAMING_SNAKE_CASE		,							tokenizer=SCREAMING_SNAKE_CASE				)

     # set the appropriate bos/pad token ids
     _lowerCAmelCase   :					Dict        =    2048
     _lowerCAmelCase   :					Union[str, Any]        =    2048

     # set other default generation config params
     _lowerCAmelCase   :					Dict        =    int(30 * audio_encoder.config.frame_rate				)
     _lowerCAmelCase   :					Dict        =    True
     _lowerCAmelCase   :					Optional[Any]        =    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__":
  __UpperCAmelCase    				=     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.'
  )

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









from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
  import tensorflow as tf


__UpperCAmelCase    				=     logging.get_logger(__name__)





@dataclass
class A__    ( A ):



 """simple docstring"""







 _lowercase     :					List[Any]      =						[
     '''no_inference''',
     '''no_cuda''',
     '''no_tpu''',
     '''no_speed''',
     '''no_memory''',
     '''no_env_print''',
     '''no_multi_process''',
 ]

 def __init__(		self :	List[Any]     ,						**A_ :	str       ):






      '''simple docstring'''




      for deprecated_arg in self.deprecated_args:
           if deprecated_arg in kwargs:
                _lowerCAmelCase   :					str        =    deprecated_arg[3:]
                _lowerCAmelCase   :					List[Any]        =    not kwargs.pop(A_       )
                logger.warning(
                    F'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
                    F''' {positive_arg}={kwargs[positive_arg]}'''       )
      _lowerCAmelCase   :					Any        =    kwargs.pop("tpu_name"     ,						self.tpu_name       )
      _lowerCAmelCase   :					str        =    kwargs.pop("device_idx"     ,						self.device_idx       )
      _lowerCAmelCase   :					str        =    kwargs.pop("eager_mode"     ,						self.eager_mode       )
      _lowerCAmelCase   :					Tuple        =    kwargs.pop("use_xla"     ,						self.use_xla       )
      super().__init__(**A_       )

 _lowercase     :					str      =						field(
     default=A  ,		metadata={'''help''': '''Name of TPU'''}  ,		)
 _lowercase     :					int      =						field(
     default=0  ,		metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''}  ,		)
 _lowercase     :					bool      =						field(default=A  ,		metadata={'''help''': '''Benchmark models in eager model.'''} )
 _lowercase     :					bool      =						field(
     default=A  ,		metadata={
         '''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
     }  ,		)

 @cached_property
 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      _lowerCAmelCase   :					Any        =    None
      if self.tpu:
           try:
                if self.tpu_name:
                     _lowerCAmelCase   :					Union[str, Any]        =    tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name       )
                else:
                     _lowerCAmelCase   :					List[str]        =    tf.distribute.cluster_resolver.TPUClusterResolver()
           except ValueError:
                _lowerCAmelCase   :					Optional[int]        =    None
      return tpu

 @cached_property
 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      if self.is_tpu:
           tf.config.experimental_connect_to_cluster(self._setup_tpu       )
           tf.tpu.experimental.initialize_tpu_system(self._setup_tpu       )

           _lowerCAmelCase   :					List[Any]        =    tf.distribute.TPUStrategy(self._setup_tpu       )
      else:
           # currently no multi gpu is allowed
           if self.is_gpu:
                # TODO: Currently only single GPU is supported
                tf.config.set_visible_devices(self.gpu_list[self.device_idx]     ,						"GPU"       )
                _lowerCAmelCase   :					Dict        =    tf.distribute.OneDeviceStrategy(device=F'''/gpu:{self.device_idx}'''       )
           else:
                tf.config.set_visible_devices([]     ,						"GPU"       )  # disable GPU
                _lowerCAmelCase   :					Optional[Any]        =    tf.distribute.OneDeviceStrategy(device=F'''/cpu:{self.device_idx}'''       )

      return strategy

 @property
 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      return self._setup_tpu is not None

 @property
 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      return self._setup_strategy

 @property
 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      return tf.config.list_physical_devices("GPU"       )

 @property
 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      if self.cuda:
           return len(self.gpu_list       )
      return 0


 @property
 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      return self.n_gpu > 0






