


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

UpperCAmelCase__   	=							{
    '''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__   	=							{
    '''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__   	=							{
    '''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__   	=							{
    '''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__   	=							{
    '''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__   	=							{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
UpperCAmelCase__   	=							{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
UpperCAmelCase__   	=							[]
UpperCAmelCase__   	=							[]
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case						)     ->			Optional[int]:







 """simple docstring"""


 for attribute in key.split('''.'''						):
  _lowercase							      =getattr(__snake_case     ,						__snake_case						)

 if weight_type is not None:
  _lowercase							      =getattr(__snake_case     ,						__snake_case						).shape
 else:
  _lowercase							      =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							      =value
 elif weight_type == "weight_g":
  _lowercase							      =value
 elif weight_type == "weight_v":
  _lowercase							      =value
 elif weight_type == "bias":
  _lowercase							      =value
 elif weight_type == "running_mean":
  _lowercase							      =value
 elif weight_type == "running_var":
  _lowercase							      =value
 elif weight_type == "num_batches_tracked":
  _lowercase							      =value
 elif weight_type == "weight_ih_l0":
  _lowercase							      =value
 elif weight_type == "weight_hh_l0":
  _lowercase							      =value
 elif weight_type == "bias_ih_l0":
  _lowercase							      =value
 elif weight_type == "bias_hh_l0":
  _lowercase							      =value
 elif weight_type == "weight_ih_l1":
  _lowercase							      =value
 elif weight_type == "weight_hh_l1":
  _lowercase							      =value
 elif weight_type == "bias_ih_l1":
  _lowercase							      =value
 elif weight_type == "bias_hh_l1":
  _lowercase							      =value
 else:
  _lowercase							      =value

 logger.info(F"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}."						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			int:







 """simple docstring"""


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







 """simple docstring"""


 _lowercase							      =[]

 if model_name == "encodec_24khz" or "encodec_32khz":
  _lowercase							      =MAPPING_24K
 elif model_name == "encodec_48khz":
  _lowercase							      =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

  _lowercase							      =False
  for key, mapped_key in MAPPING.items():
   if "*" in key:
    _lowercase      ,    _lowercase							      =key.split('''.*.'''						)
    if prefix in name and suffix in name:
     _lowercase							      =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							      =True
    if "*" in mapped_key:
     _lowercase							      =name.split(__snake_case						)[0].split('''.'''						)[-2]
     _lowercase							      =mapped_key.replace('''*'''     ,						__snake_case						)
    if "weight_g" in name:
     _lowercase							      ='''weight_g'''
    elif "weight_v" in name:
     _lowercase							      ='''weight_v'''
    elif "weight_ih_l0" in name:
     _lowercase							      ='''weight_ih_l0'''
    elif "weight_hh_l0" in name:
     _lowercase							      ='''weight_hh_l0'''
    elif "bias_ih_l0" in name:
     _lowercase							      ='''bias_ih_l0'''
    elif "bias_hh_l0" in name:
     _lowercase							      ='''bias_hh_l0'''
    elif "weight_ih_l1" in name:
     _lowercase							      ='''weight_ih_l1'''
    elif "weight_hh_l1" in name:
     _lowercase							      ='''weight_hh_l1'''
    elif "bias_ih_l1" in name:
     _lowercase							      ='''bias_ih_l1'''
    elif "bias_hh_l1" in name:
     _lowercase							      ='''bias_hh_l1'''
    elif "bias" in name:
     _lowercase							      ='''bias'''
    elif "weight" in name:
     _lowercase							      ='''weight'''
    elif "running_mean" in name:
     _lowercase							      ='''running_mean'''
    elif "running_var" in name:
     _lowercase							      ='''running_var'''
    elif "num_batches_tracked" in name:
     _lowercase							      ='''num_batches_tracked'''
    else:
     _lowercase							      =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 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case=None     ,						__snake_case=None     ,						)     ->			Union[str, Any]:







 """simple docstring"""


 if config_path is not None:
  _lowercase							      =EncodecConfig.from_pretrained(__snake_case						)
 else:
  _lowercase							      =EncodecConfig()

 if model_name == "encodec_24khz":
  pass  # config is already correct
 elif model_name == "encodec_32khz":
  _lowercase							      =[8, 5, 4, 4]
  _lowercase							      =[2.2]
  _lowercase							      =64
  _lowercase							      =32000
  _lowercase							      =2048
  _lowercase							      =False
  _lowercase							      =False
  _lowercase							      =False
 elif model_name == "encodec_48khz":
  _lowercase							      =[8, 5, 4, 2]
  _lowercase							      =[3.0, 6.0, 12.0, 24.0]
  _lowercase							      =48000
  _lowercase							      =2
  _lowercase							      =False
  _lowercase							      ='''time_group_norm'''
  _lowercase							      =True
  _lowercase							      =1.0
  _lowercase							      =0.01
 else:
  raise ValueError(F"Unknown model name: {model_name}"						)

 _lowercase							      =EncodecModel(__snake_case						)

 _lowercase							      =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						)

 _lowercase							      =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
  _lowercase							      =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__":
  UpperCAmelCase__   	=							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__   	=							parser.parse_args()
  convert_checkpoint(
      args.model,
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.push_to_hub,
  )





from math import isqrt
def 				UpperCAmelCase_					(							__snake_case						)     ->			list[int]:







 """simple docstring"""


 _lowercase							      =[True] * max_number
 for i in range(2     ,						isqrt(max_number - 1						) + 1						):
  if is_prime[i]:
   for j in range(i**2     ,						__snake_case     ,						__snake_case						):
    _lowercase							      =False

 return [i for i in range(2     ,						__snake_case						) if is_prime[i]]
def 				UpperCAmelCase_					(							__snake_case = 10**8						)     ->			int:







 """simple docstring"""


 _lowercase							      =calculate_prime_numbers(max_number // 2						)

 _lowercase							      =0
 _lowercase							      =0
 _lowercase							      =len(__snake_case						) - 1
 while left <= right:
  while prime_numbers[left] * prime_numbers[right] >= max_number:
   right -= 1
  semiprimes_count += right - left + 1
  left += 1

 return semiprimes_count


if __name__ == "__main__":
  print(f'''{solution() = }''')

