



import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
a_						:     List[Any]	     =		logging.get_logger('transformers.models.encodec')

a_						:     Optional[Any]	     =		{
    'quantizer.vq.layers.*._codebook.inited': 'quantizer.layers.*.codebook.inited',
    'quantizer.vq.layers.*._codebook.cluster_size': 'quantizer.layers.*.codebook.cluster_size',
    'quantizer.vq.layers.*._codebook.embed': 'quantizer.layers.*.codebook.embed',
    'quantizer.vq.layers.*._codebook.embed_avg': 'quantizer.layers.*.codebook.embed_avg',
}
a_						:     Any	     =		{
    'encoder.model.0.conv.conv': 'encoder.layers.0.conv',
    'encoder.model.1.block.1.conv.conv': 'encoder.layers.1.block.1.conv',
    'encoder.model.1.block.3.conv.conv': 'encoder.layers.1.block.3.conv',
    'encoder.model.1.shortcut.conv.conv': 'encoder.layers.1.shortcut.conv',
    'encoder.model.3.conv.conv': 'encoder.layers.3.conv',
    'encoder.model.4.block.1.conv.conv': 'encoder.layers.4.block.1.conv',
    'encoder.model.4.block.3.conv.conv': 'encoder.layers.4.block.3.conv',
    'encoder.model.4.shortcut.conv.conv': 'encoder.layers.4.shortcut.conv',
    'encoder.model.6.conv.conv': 'encoder.layers.6.conv',
    'encoder.model.7.block.1.conv.conv': 'encoder.layers.7.block.1.conv',
    'encoder.model.7.block.3.conv.conv': 'encoder.layers.7.block.3.conv',
    'encoder.model.7.shortcut.conv.conv': 'encoder.layers.7.shortcut.conv',
    'encoder.model.9.conv.conv': 'encoder.layers.9.conv',
    'encoder.model.10.block.1.conv.conv': 'encoder.layers.10.block.1.conv',
    'encoder.model.10.block.3.conv.conv': 'encoder.layers.10.block.3.conv',
    'encoder.model.10.shortcut.conv.conv': 'encoder.layers.10.shortcut.conv',
    'encoder.model.12.conv.conv': 'encoder.layers.12.conv',
    'encoder.model.13.lstm': 'encoder.layers.13.lstm',
    'encoder.model.15.conv.conv': 'encoder.layers.15.conv',
}
a_						:     List[Any]	     =		{
    'encoder.model.0.conv.norm': 'encoder.layers.0.norm',
    'encoder.model.1.block.1.conv.norm': 'encoder.layers.1.block.1.norm',
    'encoder.model.1.block.3.conv.norm': 'encoder.layers.1.block.3.norm',
    'encoder.model.1.shortcut.conv.norm': 'encoder.layers.1.shortcut.norm',
    'encoder.model.3.conv.norm': 'encoder.layers.3.norm',
    'encoder.model.4.block.1.conv.norm': 'encoder.layers.4.block.1.norm',
    'encoder.model.4.block.3.conv.norm': 'encoder.layers.4.block.3.norm',
    'encoder.model.4.shortcut.conv.norm': 'encoder.layers.4.shortcut.norm',
    'encoder.model.6.conv.norm': 'encoder.layers.6.norm',
    'encoder.model.7.block.1.conv.norm': 'encoder.layers.7.block.1.norm',
    'encoder.model.7.block.3.conv.norm': 'encoder.layers.7.block.3.norm',
    'encoder.model.7.shortcut.conv.norm': 'encoder.layers.7.shortcut.norm',
    'encoder.model.9.conv.norm': 'encoder.layers.9.norm',
    'encoder.model.10.block.1.conv.norm': 'encoder.layers.10.block.1.norm',
    'encoder.model.10.block.3.conv.norm': 'encoder.layers.10.block.3.norm',
    'encoder.model.10.shortcut.conv.norm': 'encoder.layers.10.shortcut.norm',
    'encoder.model.12.conv.norm': 'encoder.layers.12.norm',
    'encoder.model.15.conv.norm': 'encoder.layers.15.norm',
}
a_						:     Dict	     =		{
    'decoder.model.0.conv.conv': 'decoder.layers.0.conv',
    'decoder.model.1.lstm': 'decoder.layers.1.lstm',
    'decoder.model.3.convtr.convtr': 'decoder.layers.3.conv',
    'decoder.model.4.block.1.conv.conv': 'decoder.layers.4.block.1.conv',
    'decoder.model.4.block.3.conv.conv': 'decoder.layers.4.block.3.conv',
    'decoder.model.4.shortcut.conv.conv': 'decoder.layers.4.shortcut.conv',
    'decoder.model.6.convtr.convtr': 'decoder.layers.6.conv',
    'decoder.model.7.block.1.conv.conv': 'decoder.layers.7.block.1.conv',
    'decoder.model.7.block.3.conv.conv': 'decoder.layers.7.block.3.conv',
    'decoder.model.7.shortcut.conv.conv': 'decoder.layers.7.shortcut.conv',
    'decoder.model.9.convtr.convtr': 'decoder.layers.9.conv',
    'decoder.model.10.block.1.conv.conv': 'decoder.layers.10.block.1.conv',
    'decoder.model.10.block.3.conv.conv': 'decoder.layers.10.block.3.conv',
    'decoder.model.10.shortcut.conv.conv': 'decoder.layers.10.shortcut.conv',
    'decoder.model.12.convtr.convtr': 'decoder.layers.12.conv',
    'decoder.model.13.block.1.conv.conv': 'decoder.layers.13.block.1.conv',
    'decoder.model.13.block.3.conv.conv': 'decoder.layers.13.block.3.conv',
    'decoder.model.13.shortcut.conv.conv': 'decoder.layers.13.shortcut.conv',
    'decoder.model.15.conv.conv': 'decoder.layers.15.conv',
}
a_						:     int	     =		{
    'decoder.model.0.conv.norm': 'decoder.layers.0.norm',
    'decoder.model.3.convtr.norm': 'decoder.layers.3.norm',
    'decoder.model.4.block.1.conv.norm': 'decoder.layers.4.block.1.norm',
    'decoder.model.4.block.3.conv.norm': 'decoder.layers.4.block.3.norm',
    'decoder.model.4.shortcut.conv.norm': 'decoder.layers.4.shortcut.norm',
    'decoder.model.6.convtr.norm': 'decoder.layers.6.norm',
    'decoder.model.7.block.1.conv.norm': 'decoder.layers.7.block.1.norm',
    'decoder.model.7.block.3.conv.norm': 'decoder.layers.7.block.3.norm',
    'decoder.model.7.shortcut.conv.norm': 'decoder.layers.7.shortcut.norm',
    'decoder.model.9.convtr.norm': 'decoder.layers.9.norm',
    'decoder.model.10.block.1.conv.norm': 'decoder.layers.10.block.1.norm',
    'decoder.model.10.block.3.conv.norm': 'decoder.layers.10.block.3.norm',
    'decoder.model.10.shortcut.conv.norm': 'decoder.layers.10.shortcut.norm',
    'decoder.model.12.convtr.norm': 'decoder.layers.12.norm',
    'decoder.model.13.block.1.conv.norm': 'decoder.layers.13.block.1.norm',
    'decoder.model.13.block.3.conv.norm': 'decoder.layers.13.block.3.norm',
    'decoder.model.13.shortcut.conv.norm': 'decoder.layers.13.shortcut.norm',
    'decoder.model.15.conv.norm': 'decoder.layers.15.norm',
}
a_						:     Optional[Any]	     =		{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
a_						:     List[Any]	     =		{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
a_						:     int	     =		[]
a_						:     Tuple	     =		[]

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      for attribute in key.split("." ):
            lowerCamelCase								=		getattr(UpperCAmelCase__   ,   UpperCAmelCase__ )

      if weight_type is not None:
            lowerCamelCase								=		getattr(UpperCAmelCase__   ,   UpperCAmelCase__ ).shape
      else:
            lowerCamelCase								=		hf_pointer.shape

      if hf_shape != value.shape:
            raise ValueError(
                F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
                F""" {value.shape} for {full_name}""" )

      if weight_type == "weight":
            lowerCamelCase								=		value
      elif weight_type == "weight_g":
            lowerCamelCase								=		value
      elif weight_type == "weight_v":
            lowerCamelCase								=		value
      elif weight_type == "bias":
            lowerCamelCase								=		value
      elif weight_type == "running_mean":
            lowerCamelCase								=		value
      elif weight_type == "running_var":
            lowerCamelCase								=		value
      elif weight_type == "num_batches_tracked":
            lowerCamelCase								=		value
      elif weight_type == "weight_ih_l0":
            lowerCamelCase								=		value
      elif weight_type == "weight_hh_l0":
            lowerCamelCase								=		value
      elif weight_type == "bias_ih_l0":
            lowerCamelCase								=		value
      elif weight_type == "bias_hh_l0":
            lowerCamelCase								=		value
      elif weight_type == "weight_ih_l1":
            lowerCamelCase								=		value
      elif weight_type == "weight_hh_l1":
            lowerCamelCase								=		value
      elif weight_type == "bias_ih_l1":
            lowerCamelCase								=		value
      elif weight_type == "bias_hh_l1":
            lowerCamelCase								=		value
      else:
            lowerCamelCase								=		value

      logger.info(F"""{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.""" )

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      for key in ignore_keys:
            if key.endswith(".*" ):
                  if name.startswith(key[:-1] ):
                        return True
            elif ".*." in key:
                  lowerCamelCase			,							lowerCamelCase								=		key.split(".*." )
                  if prefix in name and suffix in name:
                        return True
            elif key in name:
                  return True
      return False

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		[]

      if model_name == "encodec_24khz" or "encodec_32khz":
            lowerCamelCase								=		MAPPING_24K
      elif model_name == "encodec_48khz":
            lowerCamelCase								=		MAPPING_48K
      else:
            raise ValueError(F"""Unsupported model: {model_name}""" )

      for name, value in orig_dict.items():
            if should_ignore(UpperCAmelCase__   ,   UpperCAmelCase__ ):
                  logger.info(F"""{name} was ignored""" )
                  continue

            lowerCamelCase								=		False
            for key, mapped_key in MAPPING.items():
                  if "*" in key:
                        lowerCamelCase			,							lowerCamelCase								=		key.split(".*." )
                        if prefix in name and suffix in name:
                              lowerCamelCase								=		suffix

                  if key in name:
                        # HACK otherwise .embed gets initialized with .embed_avg too
                        if key.endswith("embed" ) and name.endswith("embed_avg" ):
                              continue

                        lowerCamelCase								=		True
                        if "*" in mapped_key:
                              lowerCamelCase								=		name.split(UpperCAmelCase__ )[0].split("." )[-2]
                              lowerCamelCase								=		mapped_key.replace("*"   ,   UpperCAmelCase__ )
                        if "weight_g" in name:
                              lowerCamelCase								=		"weight_g"
                        elif "weight_v" in name:
                              lowerCamelCase								=		"weight_v"
                        elif "weight_ih_l0" in name:
                              lowerCamelCase								=		"weight_ih_l0"
                        elif "weight_hh_l0" in name:
                              lowerCamelCase								=		"weight_hh_l0"
                        elif "bias_ih_l0" in name:
                              lowerCamelCase								=		"bias_ih_l0"
                        elif "bias_hh_l0" in name:
                              lowerCamelCase								=		"bias_hh_l0"
                        elif "weight_ih_l1" in name:
                              lowerCamelCase								=		"weight_ih_l1"
                        elif "weight_hh_l1" in name:
                              lowerCamelCase								=		"weight_hh_l1"
                        elif "bias_ih_l1" in name:
                              lowerCamelCase								=		"bias_ih_l1"
                        elif "bias_hh_l1" in name:
                              lowerCamelCase								=		"bias_hh_l1"
                        elif "bias" in name:
                              lowerCamelCase								=		"bias"
                        elif "weight" in name:
                              lowerCamelCase								=		"weight"
                        elif "running_mean" in name:
                              lowerCamelCase								=		"running_mean"
                        elif "running_var" in name:
                              lowerCamelCase								=		"running_var"
                        elif "num_batches_tracked" in name:
                              lowerCamelCase								=		"num_batches_tracked"
                        else:
                              lowerCamelCase								=		None
                        set_recursively(UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ )
                  continue
            if not is_used:
                  unused_weights.append(UpperCAmelCase__ )

      logger.warning(F"""Unused weights: {unused_weights}""" )

@torch.no_grad()
def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__=None   ,   UpperCAmelCase__=None   ,   ):


      """simple docstring"""



      if config_path is not None:
            lowerCamelCase								=		EncodecConfig.from_pretrained(UpperCAmelCase__ )
      else:
            lowerCamelCase								=		EncodecConfig()

      if model_name == "encodec_24khz":
            pass  # config is already correct
      elif model_name == "encodec_32khz":
            lowerCamelCase								=		[8, 5, 4, 4]
            lowerCamelCase								=		[2.2]
            lowerCamelCase								=		64
            lowerCamelCase								=		32000
            lowerCamelCase								=		2048
            lowerCamelCase								=		False
            lowerCamelCase								=		False
            lowerCamelCase								=		False
      elif model_name == "encodec_48khz":
            lowerCamelCase								=		[8, 5, 4, 2]
            lowerCamelCase								=		[3.0, 6.0, 1_2.0, 2_4.0]
            lowerCamelCase								=		48000
            lowerCamelCase								=		2
            lowerCamelCase								=		False
            lowerCamelCase								=		"time_group_norm"
            lowerCamelCase								=		True
            lowerCamelCase								=		1.0
            lowerCamelCase								=		0.0_1
      else:
            raise ValueError(F"""Unknown model name: {model_name}""" )

      lowerCamelCase								=		EncodecModel(UpperCAmelCase__ )

      lowerCamelCase								=		EncodecFeatureExtractor(
          feature_size=config.audio_channels   ,   sampling_rate=config.sampling_rate   ,   chunk_length_s=config.chunk_length_s   ,   overlap=config.overlap   ,   )
      feature_extractor.save_pretrained(UpperCAmelCase__ )

      lowerCamelCase								=		torch.load(UpperCAmelCase__ )
      if "best_state" in original_checkpoint:
            # we might have a training state saved, in which case discard the yaml results and just retain the weights
            lowerCamelCase								=		original_checkpoint["best_state"]
      recursively_load_weights(UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ )
      model.save_pretrained(UpperCAmelCase__ )

      if repo_id:
            print("Pushing to the hub..." )
            feature_extractor.push_to_hub(UpperCAmelCase__ )
            model.push_to_hub(UpperCAmelCase__ )


if __name__ == "__main__":
     a_						:     int	     =		argparse.ArgumentParser()
     parser.add_argument(
         '--model',
         default='encodec_24khz',
         type=str,
         help='The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.',
     )
     parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
     parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
     parser.add_argument(
         '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
     )
     parser.add_argument(
         '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
     )

     a_						:     Union[str, Any]	     =		parser.parse_args()
     convert_checkpoint(
         args.model,
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.push_to_hub,
     )




from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch





@require_torch
@require_tf
class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		SMALL_MODEL_IDENTIFIER
           lowerCamelCase								=		"pt"
           lowerCamelCase								=		"tf"







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		AutoModel.from_pretrained(self.test_model    )
           model_pt.save_pretrained(__a    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		TFAutoModel.from_pretrained(self.test_model				,			from_pt=__a    )
           model_tf.save_pretrained(__a    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		"mock_framework"

           # Framework provided - return whatever the user provides
           lowerCamelCase								=		FeaturesManager.determine_framework(self.test_model				,			__a    )
           self.assertEqual(__a				,			__a    )

           # Local checkpoint and framework provided - return provided framework
           # PyTorch checkpoint
           with TemporaryDirectory() as local_pt_ckpt:
                 self._setup_pt_ckpt(__a    )
                 lowerCamelCase								=		FeaturesManager.determine_framework(__a				,			__a    )
                 self.assertEqual(__a				,			__a    )

           # TensorFlow checkpoint
           with TemporaryDirectory() as local_tf_ckpt:
                 self._setup_tf_ckpt(__a    )
                 lowerCamelCase								=		FeaturesManager.determine_framework(__a				,			__a    )
                 self.assertEqual(__a				,			__a    )







     def 						_a      (self    ):






           '''simple docstring'''



           with TemporaryDirectory() as local_pt_ckpt:
                 self._setup_pt_ckpt(__a    )
                 lowerCamelCase								=		FeaturesManager.determine_framework(__a    )
                 self.assertEqual(__a				,			self.framework_pt    )

           # TensorFlow checkpoint
           with TemporaryDirectory() as local_tf_ckpt:
                 self._setup_tf_ckpt(__a    )
                 lowerCamelCase								=		FeaturesManager.determine_framework(__a    )
                 self.assertEqual(__a				,			self.framework_tf    )

           # Invalid local checkpoint
           with TemporaryDirectory() as local_invalid_ckpt:
                 with self.assertRaises(__a    ):
                       lowerCamelCase								=		FeaturesManager.determine_framework(__a    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		MagicMock(return_value=__a    )
           with patch("transformers.onnx.features.is_tf_available"				,			__a    ):
                 lowerCamelCase								=		FeaturesManager.determine_framework(self.test_model    )
                 self.assertEqual(__a				,			self.framework_pt    )

           # PyTorch not in environment -> use TensorFlow
           lowerCamelCase								=		MagicMock(return_value=__a    )
           with patch("transformers.onnx.features.is_torch_available"				,			__a    ):
                 lowerCamelCase								=		FeaturesManager.determine_framework(self.test_model    )
                 self.assertEqual(__a				,			self.framework_tf    )

           # Both in environment -> use PyTorch
           lowerCamelCase								=		MagicMock(return_value=__a    )
           lowerCamelCase								=		MagicMock(return_value=__a    )
           with patch("transformers.onnx.features.is_tf_available"				,			__a    ), patch(
               "transformers.onnx.features.is_torch_available"				,			__a    ):
                 lowerCamelCase								=		FeaturesManager.determine_framework(self.test_model    )
                 self.assertEqual(__a				,			self.framework_pt    )

           # Both not in environment -> raise error
           lowerCamelCase								=		MagicMock(return_value=__a    )
           lowerCamelCase								=		MagicMock(return_value=__a    )
           with patch("transformers.onnx.features.is_tf_available"				,			__a    ), patch(
               "transformers.onnx.features.is_torch_available"				,			__a    ):
                 with self.assertRaises(__a    ):
                       lowerCamelCase								=		FeaturesManager.determine_framework(self.test_model    )