






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

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


def 							lowercase_				(    __A :       int					,      __A :       Union[str, Any]					,      __A :       Optional[int]					,      __A :       List[str]					,      __A :       Any			)       ->      str:



 """simple docstring"""




 for attribute in key.split('''.'''			):
  lowercase					:   List[Any]				  =getattr(__A					,      __A			)

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

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


def 							lowercase_				(    __A :       Optional[int]					,      __A :       int			)       ->      Any:



 """simple docstring"""




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


def 							lowercase_				(    __A :       Optional[int]					,      __A :       Union[str, Any]					,      __A :       Any			)       ->      List[Any]:



 """simple docstring"""




 lowercase					:   Tuple				  =[]

 if model_name == "encodec_24khz" or "encodec_32khz":
  lowercase					:   Optional[Any]				  =MAPPING_24K
 elif model_name == "encodec_48khz":
  lowercase					:   List[str]				  =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

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

    lowercase					:   int				  =True
    if "*" in mapped_key:
     lowercase					:   Optional[Any]				  =name.split(__A			)[0].split('''.'''			)[-2]
     lowercase					:   Optional[Any]				  =mapped_key.replace('''*'''					,      __A			)
    if "weight_g" in name:
     lowercase					:   List[Any]				  ='''weight_g'''
    elif "weight_v" in name:
     lowercase					:   Any				  ='''weight_v'''
    elif "weight_ih_l0" in name:
     lowercase					:   List[Any]				  ='''weight_ih_l0'''
    elif "weight_hh_l0" in name:
     lowercase					:   List[Any]				  ='''weight_hh_l0'''
    elif "bias_ih_l0" in name:
     lowercase					:   Any				  ='''bias_ih_l0'''
    elif "bias_hh_l0" in name:
     lowercase					:   Optional[int]				  ='''bias_hh_l0'''
    elif "weight_ih_l1" in name:
     lowercase					:   Optional[Any]				  ='''weight_ih_l1'''
    elif "weight_hh_l1" in name:
     lowercase					:   List[str]				  ='''weight_hh_l1'''
    elif "bias_ih_l1" in name:
     lowercase					:   Optional[Any]				  ='''bias_ih_l1'''
    elif "bias_hh_l1" in name:
     lowercase					:   Optional[Any]				  ='''bias_hh_l1'''
    elif "bias" in name:
     lowercase					:   Dict				  ='''bias'''
    elif "weight" in name:
     lowercase					:   Optional[Any]				  ='''weight'''
    elif "running_mean" in name:
     lowercase					:   List[str]				  ='''running_mean'''
    elif "running_var" in name:
     lowercase					:   Dict				  ='''running_var'''
    elif "num_batches_tracked" in name:
     lowercase					:   List[Any]				  ='''num_batches_tracked'''
    else:
     lowercase					:   Tuple				  =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 							lowercase_				(    __A :       int					,      __A :       Any					,      __A :       Optional[Any]					,      __A :       Optional[int]=None					,      __A :       List[Any]=None					,      )       ->      Dict:



 """simple docstring"""




 if config_path is not None:
  lowercase					:   Dict				  =EncodecConfig.from_pretrained(__A			)
 else:
  lowercase					:   Dict				  =EncodecConfig()

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

 lowercase					:   str				  =EncodecModel(__A			)

 lowercase					:   List[str]				  =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			)

 lowercase					:   List[Any]				  =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
  lowercase					:   int				  =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__":
      SCREAMING_SNAKE_CASE						=	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.'
      )

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











'''simple docstring'''



import re


def 							lowercase_				(    __A :       str			)       ->      bool:



 """simple docstring"""




 lowercase					:   Any				  =re.compile(R'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$'''			)
 if match := re.search(__A					,      __A			):
  return match.string == phone
 return False


if __name__ == "__main__":
      print(indian_phone_validator('+918827897895'))



