



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





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





def 						__snake_case(				_lowerCAmelCase		)		-> MusicgenDecoderConfig:
      if checkpoint == "small":
            # default config values
            snake_case__  :    Dict    						=      1_024
            snake_case__  :    Tuple    						=      24
            snake_case__  :    int    						=      16
      elif checkpoint == "medium":
            snake_case__  :    List[str]    						=      1_536
            snake_case__  :    List[Any]    						=      48
            snake_case__  :    int    						=      24
      elif checkpoint == "large":
            snake_case__  :    Optional[Any]    						=      2_048
            snake_case__  :    Optional[int]    						=      48
            snake_case__  :    List[Any]    						=      32
      else:
            raise ValueError(f"Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."		)
      snake_case__  :    List[Any]    						=      MusicgenDecoderConfig(
          hidden_size=_lowerCAmelCase     ,   ffn_dim=hidden_size * 4     ,   num_hidden_layers=_lowerCAmelCase     ,   num_attention_heads=_lowerCAmelCase     ,   )
      return config





@torch.no_grad()
def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase=None     ,   _lowerCAmelCase=None     ,   _lowerCAmelCase="cpu"		)		-> Any:
      snake_case__  :    List[str]    						=      MusicGen.get_pretrained(_lowerCAmelCase     ,   device=_lowerCAmelCase		)
      snake_case__  :    Any    						=      decoder_config_from_checkpoint(_lowerCAmelCase		)

      snake_case__  :    int    						=      fairseq_model.lm.state_dict()
      snake_case__						,				snake_case__  :    List[Any]    						=      rename_state_dict(
          _lowerCAmelCase     ,   hidden_size=decoder_config.hidden_size		)

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

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

      with torch.no_grad():
            snake_case__  :    Optional[int]    						=      model(input_ids=_lowerCAmelCase     ,   decoder_input_ids=_lowerCAmelCase		).logits

      if logits.shape != (8, 1, 2_048):
            raise ValueError("""Incorrect shape for logits"""		)

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

      snake_case__  :    Tuple    						=      MusicgenProcessor(feature_extractor=_lowerCAmelCase     ,   tokenizer=_lowerCAmelCase		)

      # set the appropriate bos/pad token ids
      snake_case__  :    Dict    						=      2_048
      snake_case__  :    Optional[int]    						=      2_048

      # set other default generation config params
      snake_case__  :    Tuple    						=      int(30 * audio_encoder.config.frame_rate		)
      snake_case__  :    Tuple    						=      True
      snake_case__  :    Tuple    						=      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__":
       __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 unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
 import torch

 from transformers import XLMRobertaModel






@require_sentencepiece
@require_tokenizers
@require_torch
class        lowerCAmelCase_(				unittest.TestCase     ):
  '''simple docstring'''




  @slow
  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      lowerCAmelCase__ :  Tuple							      =					XLMRobertaModel.from_pretrained("""xlm-roberta-base"""      )
      lowerCAmelCase__ :  Optional[int]							      =					torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]      )
      # The dog is cute and lives in the garden house

      lowerCAmelCase__ :  str							      =					torch.Size((1, 12, 768)      )  # batch_size, sequence_length, embedding_vector_dim
      lowerCAmelCase__ :  Dict							      =					torch.tensor(
          [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]]      )
      #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
      #  xlmr.eval()
      #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
      with torch.no_grad():
          lowerCAmelCase__ :  Optional[int]							      =					model(__UpperCAmelCase      )["""last_hidden_state"""].detach()
      self.assertEqual(output.shape  ,__UpperCAmelCase      )
      # compare the actual values for a slice of last dim
      self.assertTrue(torch.allclose(output[:, :, -1]  ,__UpperCAmelCase  ,atol=1E-3      )      )






  @slow
  def 							UpperCAmelCase_	(    self      )    ->		int:
      lowerCAmelCase__ :  Union[str, Any]							      =					XLMRobertaModel.from_pretrained("""xlm-roberta-large"""      )
      lowerCAmelCase__ :  Optional[Any]							      =					torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]      )
      # The dog is cute and lives in the garden house

      lowerCAmelCase__ :  Dict							      =					torch.Size((1, 12, 1024)      )  # batch_size, sequence_length, embedding_vector_dim
      lowerCAmelCase__ :  Union[str, Any]							      =					torch.tensor(
          [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]]      )
      #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
      #  xlmr.eval()
      #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
      with torch.no_grad():
          lowerCAmelCase__ :  Union[str, Any]							      =					model(__UpperCAmelCase      )["""last_hidden_state"""].detach()
      self.assertEqual(output.shape  ,__UpperCAmelCase      )
      # compare the actual values for a slice of last dim
      self.assertTrue(torch.allclose(output[:, :, -1]  ,__UpperCAmelCase  ,atol=1E-3      )      )


