
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()
__lowerCamelCase         =			logging.get_logger('''transformers.models.encodec''')

__lowerCamelCase         =			{
    '''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''',
}
__lowerCamelCase         =			{
    '''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''',
}
__lowerCamelCase         =			{
    '''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''',
}
__lowerCamelCase         =			{
    '''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''',
}
__lowerCamelCase         =			{
    '''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''',
}
__lowerCamelCase         =			{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
__lowerCamelCase         =			{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
__lowerCamelCase         =			[]
__lowerCamelCase         =			[]







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case    )     ->     Optional[Any]:






      '''simple docstring'''







      for attribute in key.split("."    ):
            UpperCAmelCase_  :	Optional[int]				      =		getattr(__snake_case					,  __snake_case    )

      if weight_type is not None:
            UpperCAmelCase_  :	Any				      =		getattr(__snake_case					,  __snake_case    ).shape
      else:
            UpperCAmelCase_  :	Union[str, Any]				      =		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":
            UpperCAmelCase_  :	List[str]				      =		value
      elif weight_type == "weight_g":
            UpperCAmelCase_  :	Tuple				      =		value
      elif weight_type == "weight_v":
            UpperCAmelCase_  :	Tuple				      =		value
      elif weight_type == "bias":
            UpperCAmelCase_  :	str				      =		value
      elif weight_type == "running_mean":
            UpperCAmelCase_  :	Any				      =		value
      elif weight_type == "running_var":
            UpperCAmelCase_  :	Union[str, Any]				      =		value
      elif weight_type == "num_batches_tracked":
            UpperCAmelCase_  :	Optional[int]				      =		value
      elif weight_type == "weight_ih_l0":
            UpperCAmelCase_  :	Tuple				      =		value
      elif weight_type == "weight_hh_l0":
            UpperCAmelCase_  :	int				      =		value
      elif weight_type == "bias_ih_l0":
            UpperCAmelCase_  :	int				      =		value
      elif weight_type == "bias_hh_l0":
            UpperCAmelCase_  :	Optional[Any]				      =		value
      elif weight_type == "weight_ih_l1":
            UpperCAmelCase_  :	Tuple				      =		value
      elif weight_type == "weight_hh_l1":
            UpperCAmelCase_  :	str				      =		value
      elif weight_type == "bias_ih_l1":
            UpperCAmelCase_  :	List[Any]				      =		value
      elif weight_type == "bias_hh_l1":
            UpperCAmelCase_  :	Tuple				      =		value
      else:
            UpperCAmelCase_  :	Optional[Any]				      =		value

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







def    _snake_case (       __snake_case					,  __snake_case    )     ->     Union[str, Any]:






      '''simple docstring'''







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







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case    )     ->     Union[str, Any]:






      '''simple docstring'''







      UpperCAmelCase_  :	List[str]				      =		[]

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

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

            UpperCAmelCase_  :	List[Any]				      =		False
            for key, mapped_key in MAPPING.items():
                  if "*" in key:
                        UpperCAmelCase_  :	Union[str, Any]				      =		key.split(".*."    )
                        if prefix in name and suffix in name:
                              UpperCAmelCase_  :	Tuple				      =		suffix

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

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

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







@torch.no_grad()
def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case=None					,  __snake_case=None					,  )     ->     str:






      '''simple docstring'''







      if config_path is not None:
            UpperCAmelCase_  :	int				      =		EncodecConfig.from_pretrained(__snake_case    )
      else:
            UpperCAmelCase_  :	Optional[int]				      =		EncodecConfig()

      if model_name == "encodec_24khz":
            pass  # config is already correct
      elif model_name == "encodec_32khz":
            UpperCAmelCase_  :	Dict				      =		[8, 5, 4, 4]
            UpperCAmelCase_  :	Any				      =		[2.2]
            UpperCAmelCase_  :	Optional[int]				      =		6_4
            UpperCAmelCase_  :	int				      =		3_2_0_0_0
            UpperCAmelCase_  :	Union[str, Any]				      =		2_0_4_8
            UpperCAmelCase_  :	List[Any]				      =		False
            UpperCAmelCase_  :	Optional[Any]				      =		False
            UpperCAmelCase_  :	Dict				      =		False
      elif model_name == "encodec_48khz":
            UpperCAmelCase_  :	Tuple				      =		[8, 5, 4, 2]
            UpperCAmelCase_  :	Union[str, Any]				      =		[3.0, 6.0, 12.0, 24.0]
            UpperCAmelCase_  :	List[Any]				      =		4_8_0_0_0
            UpperCAmelCase_  :	Optional[Any]				      =		2
            UpperCAmelCase_  :	Optional[int]				      =		False
            UpperCAmelCase_  :	List[str]				      =		"time_group_norm"
            UpperCAmelCase_  :	int				      =		True
            UpperCAmelCase_  :	Dict				      =		1.0
            UpperCAmelCase_  :	int				      =		0.01
      else:
            raise ValueError(F"""Unknown model name: {model_name}"""    )

      UpperCAmelCase_  :	Tuple				      =		EncodecModel(__snake_case    )

      UpperCAmelCase_  :	int				      =		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(__snake_case    )

      UpperCAmelCase_  :	Optional[int]				      =		torch.load(__snake_case    )
      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
            UpperCAmelCase_  :	Tuple				      =		original_checkpoint["best_state"]
      recursively_load_weights(__snake_case					,  __snake_case					,  __snake_case    )
      model.save_pretrained(__snake_case    )

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


if __name__ == "__main__":
   __lowerCamelCase         =			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.'''
   )

   __lowerCamelCase         =			parser.parse_args()
   convert_checkpoint(
       args.model,
       args.checkpoint_path,
       args.pytorch_dump_folder_path,
       args.config_path,
       args.push_to_hub,
   )





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase         =			{
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase         =			['''RoFormerTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase         =			[
       '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RoFormerForCausalLM''',
       '''RoFormerForMaskedLM''',
       '''RoFormerForMultipleChoice''',
       '''RoFormerForQuestionAnswering''',
       '''RoFormerForSequenceClassification''',
       '''RoFormerForTokenClassification''',
       '''RoFormerLayer''',
       '''RoFormerModel''',
       '''RoFormerPreTrainedModel''',
       '''load_tf_weights_in_roformer''',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase         =			[
       '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFRoFormerForCausalLM''',
       '''TFRoFormerForMaskedLM''',
       '''TFRoFormerForMultipleChoice''',
       '''TFRoFormerForQuestionAnswering''',
       '''TFRoFormerForSequenceClassification''',
       '''TFRoFormerForTokenClassification''',
       '''TFRoFormerLayer''',
       '''TFRoFormerModel''',
       '''TFRoFormerPreTrainedModel''',
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase         =			[
       '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''FlaxRoFormerForMaskedLM''',
       '''FlaxRoFormerForMultipleChoice''',
       '''FlaxRoFormerForQuestionAnswering''',
       '''FlaxRoFormerForSequenceClassification''',
       '''FlaxRoFormerForTokenClassification''',
       '''FlaxRoFormerModel''',
       '''FlaxRoFormerPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
   from .tokenization_roformer import RoFormerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_roformer_fast import RoFormerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roformer import (
          ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          RoFormerForCausalLM,
          RoFormerForMaskedLM,
          RoFormerForMultipleChoice,
          RoFormerForQuestionAnswering,
          RoFormerForSequenceClassification,
          RoFormerForTokenClassification,
          RoFormerLayer,
          RoFormerModel,
          RoFormerPreTrainedModel,
          load_tf_weights_in_roformer,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_roformer import (
          TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRoFormerForCausalLM,
          TFRoFormerForMaskedLM,
          TFRoFormerForMultipleChoice,
          TFRoFormerForQuestionAnswering,
          TFRoFormerForSequenceClassification,
          TFRoFormerForTokenClassification,
          TFRoFormerLayer,
          TFRoFormerModel,
          TFRoFormerPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_roformer import (
          FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          FlaxRoFormerForMaskedLM,
          FlaxRoFormerForMultipleChoice,
          FlaxRoFormerForQuestionAnswering,
          FlaxRoFormerForSequenceClassification,
          FlaxRoFormerForTokenClassification,
          FlaxRoFormerModel,
          FlaxRoFormerPreTrainedModel,
      )


else:
   import sys

   __lowerCamelCase         =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
