


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   :					int	=      logging.get_logger(__name__)


A   :					Dict	=      ["model.decoder.embed_positions.weights"]

def    lowercase_	(	_A    :					Optional[Any]		):

      """simple docstring"""




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

def    lowercase_	(	_A    :					OrderedDict					,      _A    :					int		):

      """simple docstring"""




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

def    lowercase_	(	_A    :					str		):

      """simple docstring"""




      if checkpoint == "small":
            # default config values
            lowerCamelCase__	:       Union[str, Any]					=						1024
            lowerCamelCase__	:       List[Any]					=						24
            lowerCamelCase__	:       int					=						16
      elif checkpoint == "medium":
            lowerCamelCase__	:       Tuple					=						1536
            lowerCamelCase__	:       Union[str, Any]					=						48
            lowerCamelCase__	:       Optional[Any]					=						24
      elif checkpoint == "large":
            lowerCamelCase__	:       Any					=						2048
            lowerCamelCase__	:       Optional[Any]					=						48
            lowerCamelCase__	:       Tuple					=						32
      else:
            raise ValueError(F"Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."		)
      lowerCamelCase__	:       Tuple					=						MusicgenDecoderConfig(
          hidden_size=_A					,      ffn_dim=hidden_size * 4					,      num_hidden_layers=_A					,      num_attention_heads=_A					,      )
      return config

@torch.no_grad()
def    lowercase_	(	_A    :					List[str]					,      _A    :					Any=None					,      _A    :					Dict=None					,      _A    :					str="cpu"		):

      """simple docstring"""




      lowerCamelCase__	:       Dict					=						MusicGen.get_pretrained(_A					,      device=_A		)
      lowerCamelCase__	:       Optional[Any]					=						decoder_config_from_checkpoint(_A		)

      lowerCamelCase__	:       str					=						fairseq_model.lm.state_dict()
      lowerCamelCase__					,     lowerCamelCase__	:       Optional[Any]					=						rename_state_dict(
          _A					,      hidden_size=decoder_config.hidden_size		)

      lowerCamelCase__	:       Union[str, Any]					=						TaEncoderModel.from_pretrained("t5-base"		)
      lowerCamelCase__	:       Dict					=						EncodecModel.from_pretrained("facebook/encodec_32khz"		)
      lowerCamelCase__	:       List[Any]					=						MusicgenForCausalLM(_A		).eval()

      # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
      lowerCamelCase__					,     lowerCamelCase__	:       Optional[int]					=						decoder.load_state_dict(_A					,      strict=_A		)

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

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

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

      # init the composite model
      lowerCamelCase__	:       Optional[int]					=						MusicgenForConditionalGeneration(text_encoder=_A					,      audio_encoder=_A					,      decoder=_A		)

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

      # check we can do a forward pass
      lowerCamelCase__	:       Optional[int]					=						torch.arange(0					,      8					,      dtype=torch.long		).reshape(2					,      -1		)
      lowerCamelCase__	:       Any					=						input_ids.reshape(2 * 4					,      -1		)

      with torch.no_grad():
            lowerCamelCase__	:       List[Any]					=						model(input_ids=_A					,      decoder_input_ids=_A		).logits

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

      # now construct the processor
      lowerCamelCase__	:       Union[str, Any]					=						AutoTokenizer.from_pretrained("t5-base"		)
      lowerCamelCase__	:       Dict					=						AutoFeatureExtractor.from_pretrained("facebook/encodec_32khz"					,      padding_side="left"		)

      lowerCamelCase__	:       List[str]					=						MusicgenProcessor(feature_extractor=_A					,      tokenizer=_A		)

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

      # set other default generation config params
      lowerCamelCase__	:       Optional[Any]					=						int(30 * audio_encoder.config.frame_rate		)
      lowerCamelCase__	:       Dict					=						True
      lowerCamelCase__	:       int					=						3.0

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

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


if __name__ == "__main__":
    A   :					List[Any]	=      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   :					Optional[Any]	=      parser.parse_args()
    convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)






def    lowercase_	(	_A    :					int		):

      """simple docstring"""




      if not isinstance(_A					,      _A		):
            lowerCamelCase__	:       List[str]					=						F"Input value of [number={number}] must be an integer"
            raise TypeError(_A		)
      if number < 0:
            return False
      lowerCamelCase__	:       Dict					=						number * number
      while number > 0:
            if number % 10 != number_square % 10:
                  return False
            number //= 10
            number_square //= 10
      return True


if __name__ == "__main__":
    import doctest

    doctest.testmod()


