


'''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()
UpperCAmelCase_			:  Optional[Any]										=   logging.get_logger('transformers.models.encodec')

UpperCAmelCase_			:  List[str]										=   {
    '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',
}
UpperCAmelCase_			:  Tuple										=   {
    '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',
}
UpperCAmelCase_			:  int										=   {
    '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',
}
UpperCAmelCase_			:  Optional[int]										=   {
    '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',
}
UpperCAmelCase_			:  List[Any]										=   {
    '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',
}
UpperCAmelCase_			:  Any										=   {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
UpperCAmelCase_			:  Tuple										=   {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
UpperCAmelCase_			:  str										=   []
UpperCAmelCase_			:  Optional[Any]										=   []


def       _lowercase							(					UpperCamelCase__							:       Union[str, Any],					UpperCamelCase__							:       Tuple,					UpperCamelCase__							:       Any,					UpperCamelCase__							:       List[Any],					UpperCamelCase__							:       Dict      ):
 for attribute in key.split('.'      ):
  __A	:		Union[str, Any]	  =      getattr(UpperCamelCase__,					UpperCamelCase__      )

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

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


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


def       _lowercase							(					UpperCamelCase__							:       Tuple,					UpperCamelCase__							:       Optional[int],					UpperCamelCase__							:       Optional[Any]      ):
 __A	:		Optional[int]	  =      []

 if model_name == "encodec_24khz" or "encodec_32khz":
  __A	:		Dict	  =      MAPPING_24K
 elif model_name == "encodec_48khz":
  __A	:		Union[str, Any]	  =      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

  __A	:		Optional[Any]	  =      False
  for key, mapped_key in MAPPING.items():
   if "*" in key:
    __A  ,__A	:		Optional[int]	  =      key.split('.*.'      )
    if prefix in name and suffix in name:
     __A	:		Optional[int]	  =      suffix

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

    __A	:		Tuple	  =      True
    if "*" in mapped_key:
     __A	:		List[Any]	  =      name.split(UpperCamelCase__      )[0].split('.'      )[-2]
     __A	:		Union[str, Any]	  =      mapped_key.replace('*',					UpperCamelCase__      )
    if "weight_g" in name:
     __A	:		List[str]	  =      'weight_g'
    elif "weight_v" in name:
     __A	:		Optional[Any]	  =      'weight_v'
    elif "weight_ih_l0" in name:
     __A	:		List[Any]	  =      'weight_ih_l0'
    elif "weight_hh_l0" in name:
     __A	:		int	  =      'weight_hh_l0'
    elif "bias_ih_l0" in name:
     __A	:		Union[str, Any]	  =      'bias_ih_l0'
    elif "bias_hh_l0" in name:
     __A	:		int	  =      'bias_hh_l0'
    elif "weight_ih_l1" in name:
     __A	:		Optional[int]	  =      'weight_ih_l1'
    elif "weight_hh_l1" in name:
     __A	:		int	  =      'weight_hh_l1'
    elif "bias_ih_l1" in name:
     __A	:		Optional[int]	  =      'bias_ih_l1'
    elif "bias_hh_l1" in name:
     __A	:		Tuple	  =      'bias_hh_l1'
    elif "bias" in name:
     __A	:		Union[str, Any]	  =      'bias'
    elif "weight" in name:
     __A	:		List[Any]	  =      'weight'
    elif "running_mean" in name:
     __A	:		int	  =      'running_mean'
    elif "running_var" in name:
     __A	:		int	  =      'running_var'
    elif "num_batches_tracked" in name:
     __A	:		int	  =      'num_batches_tracked'
    else:
     __A	:		Tuple	  =      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__							:       List[Any],					UpperCamelCase__							:       Dict,					UpperCamelCase__							:       int,					UpperCamelCase__							:       Any=None,					UpperCamelCase__							:       Optional[Any]=None,					):
 if config_path is not None:
  __A	:		Dict	  =      EncodecConfig.from_pretrained(UpperCamelCase__      )
 else:
  __A	:		Union[str, Any]	  =      EncodecConfig()

 if model_name == "encodec_24khz":
  pass  # config is already correct
 elif model_name == "encodec_32khz":
  __A	:		Union[str, Any]	  =      [8, 5, 4, 4]
  __A	:		List[Any]	  =      [2.2]
  __A	:		Optional[int]	  =      64
  __A	:		Dict	  =      32000
  __A	:		str	  =      2048
  __A	:		List[Any]	  =      False
  __A	:		List[Any]	  =      False
  __A	:		Optional[int]	  =      False
 elif model_name == "encodec_48khz":
  __A	:		Tuple	  =      [8, 5, 4, 2]
  __A	:		int	  =      [3.0, 6.0, 12.0, 24.0]
  __A	:		Union[str, Any]	  =      48000
  __A	:		Optional[Any]	  =      2
  __A	:		Any	  =      False
  __A	:		List[Any]	  =      'time_group_norm'
  __A	:		Tuple	  =      True
  __A	:		int	  =      1.0
  __A	:		Any	  =      0.01
 else:
  raise ValueError(f"""Unknown model name: {model_name}"""      )

 __A	:		Any	  =      EncodecModel(UpperCamelCase__      )

 __A	:		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(UpperCamelCase__      )

 __A	:		Union[str, Any]	  =      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
  __A	:		Any	  =      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__":
 UpperCAmelCase_			:  Any										=   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.'
 )

 UpperCAmelCase_			:  Tuple										=   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 dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin

@dataclass
class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		torch.FloatTensor


class     _lowerCamelCase       (     snake_case_     ,		snake_case_					):




 '''simple docstring'''


 @register_to_config
 def __init__(   self   ,   __lowercase = 16   ,   __lowercase = 88   ,   __lowercase = None   ,   __lowercase = None   ,   __lowercase = 1   ,   __lowercase = 0.0   ,   __lowercase = 32   ,   __lowercase = None   ,   __lowercase = False   ,   __lowercase = None   ,   __lowercase = "geglu"   ,   __lowercase = True   ,   __lowercase = True   ,   ):

  """simple docstring"""







  super().__init__()
  __A	:		Optional[int]	  =      num_attention_heads
  __A	:		Dict	  =      attention_head_dim
  __A	:		Optional[int]	  =      num_attention_heads * attention_head_dim

  __A	:		Optional[Any]	  =      in_channels

  __A	:		Optional[int]	  =      torch.nn.GroupNorm(num_groups=__lowercase   ,   num_channels=__lowercase   ,   eps=1E-6   ,   affine=__lowercase    )
  __A	:		Optional[Any]	  =      nn.Linear(__lowercase   ,   __lowercase    )

  # 3. Define transformers blocks
  __A	:		List[Any]	  =      nn.ModuleList(
      [
          BasicTransformerBlock(
              __lowercase   ,   __lowercase   ,   __lowercase   ,   dropout=__lowercase   ,   cross_attention_dim=__lowercase   ,   activation_fn=__lowercase   ,   attention_bias=__lowercase   ,   double_self_attention=__lowercase   ,   norm_elementwise_affine=__lowercase   ,   )
          for d in range(__lowercase    )
      ]    )

  __A	:		Union[str, Any]	  =      nn.Linear(__lowercase   ,   __lowercase    )







 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase=1   ,   __lowercase=None   ,   __lowercase = True   ,   ):

  """simple docstring"""







  __A  ,__A  ,__A  ,__A	:		int	  =      hidden_states.shape
  __A	:		Union[str, Any]	  =      batch_frames // num_frames

  __A	:		Union[str, Any]	  =      hidden_states

  __A	:		List[str]	  =      hidden_states[None, :].reshape(__lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    )
  __A	:		List[Any]	  =      hidden_states.permute(0   ,   2   ,   1   ,   3   ,   4    )

  __A	:		Dict	  =      self.norm(__lowercase    )
  __A	:		List[Any]	  =      hidden_states.permute(0   ,   3   ,   4   ,   2   ,   1    ).reshape(batch_size * height * width   ,   __lowercase   ,   __lowercase    )

  __A	:		Optional[Any]	  =      self.proj_in(__lowercase    )

  # 2. Blocks
  for block in self.transformer_blocks:
   __A	:		List[Any]	  =      block(
       __lowercase   ,   encoder_hidden_states=__lowercase   ,   timestep=__lowercase   ,   cross_attention_kwargs=__lowercase   ,   class_labels=__lowercase   ,   )

  # 3. Output
  __A	:		Optional[int]	  =      self.proj_out(__lowercase    )
  __A	:		int	  =      (
      hidden_states[None, None, :]
      .reshape(__lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    )
      .permute(0   ,   3   ,   4   ,   1   ,   2    )
      .contiguous()
  )
  __A	:		List[str]	  =      hidden_states.reshape(__lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    )

  __A	:		str	  =      hidden_states + residual

  if not return_dict:
   return (output,)

  return TransformerTemporalModelOutput(sample=__lowercase    )






