


'''simple docstring'''






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

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





def lowerCamelCase__      (						__lowerCamelCase	:							Dict	,   __lowerCamelCase	:							str	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							List[Any]	,   __lowerCamelCase	:							Union[str, Any]							):
   for attribute in key.split('.'							):
      _UpperCAmelCase       :       Dict							   =getattr(a__	,   a__							)

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

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





def lowerCamelCase__      (						__lowerCamelCase	:							List[str]	,   __lowerCamelCase	:							List[Any]							):
   for key in ignore_keys:
      if key.endswith('.*'							):
         if name.startswith(key[:-1]							):
            return True
      elif ".*." in key:
         _UpperCAmelCase   ,	_UpperCAmelCase       :       Optional[int]							   =key.split('.*.'							)
         if prefix in name and suffix in name:
            return True
      elif key in name:
         return True
   return False





def lowerCamelCase__      (						__lowerCamelCase	:							List[Any]	,   __lowerCamelCase	:							Optional[Any]	,   __lowerCamelCase	:							Dict							):
   _UpperCAmelCase       :       int							   =[]

   if model_name == "encodec_24khz" or "encodec_32khz":
      _UpperCAmelCase       :       int							   =MAPPING_24K
   elif model_name == "encodec_48khz":
      _UpperCAmelCase       :       List[Any]							   =MAPPING_48K
   else:
      raise ValueError(f"Unsupported model: {model_name}"							)

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

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

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





@torch.no_grad()
def lowerCamelCase__      (						__lowerCamelCase	:							Union[str, Any]	,   __lowerCamelCase	:							Dict	,   __lowerCamelCase	:							Tuple	,   __lowerCamelCase	:							Union[str, Any]=None	,   __lowerCamelCase	:							Any=None	,   ):
   if config_path is not None:
      _UpperCAmelCase       :       int							   =EncodecConfig.from_pretrained(a__							)
   else:
      _UpperCAmelCase       :       Optional[Any]							   =EncodecConfig()

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

   _UpperCAmelCase       :       List[Any]							   =EncodecModel(a__							)

   _UpperCAmelCase       :       List[Any]							   =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(a__							)

   _UpperCAmelCase       :       str							   =torch.load(a__							)
   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       :       Dict							   =original_checkpoint['best_state']
   recursively_load_weights(a__	,   a__	,   a__							)
   model.save_pretrained(a__							)

   if repo_id:
      print('Pushing to the hub...'							)
      feature_extractor.push_to_hub(a__							)
      model.push_to_hub(a__							)


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

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





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase							={
    'configuration_groupvit': [
        'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'GroupViTConfig',
        'GroupViTOnnxConfig',
        'GroupViTTextConfig',
        'GroupViTVisionConfig',
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase							=[
     'GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'GroupViTModel',
     'GroupViTPreTrainedModel',
     'GroupViTTextModel',
     'GroupViTVisionModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase							=[
     'TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFGroupViTModel',
     'TFGroupViTPreTrainedModel',
     'TFGroupViTTextModel',
     'TFGroupViTVisionModel',
 ]

if TYPE_CHECKING:
 from .configuration_groupvit import (
     GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     GroupViTConfig,
     GroupViTOnnxConfig,
     GroupViTTextConfig,
     GroupViTVisionConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_groupvit import (
      GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      GroupViTModel,
      GroupViTPreTrainedModel,
      GroupViTTextModel,
      GroupViTVisionModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_groupvit import (
      TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFGroupViTModel,
      TFGroupViTPreTrainedModel,
      TFGroupViTTextModel,
      TFGroupViTVisionModel,
  )

else:
 import sys

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

