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__		:					Union[str, Any]						=					logging.get_logger("transformers.models.encodec")

SCREAMING_SNAKE_CASE__		:					Tuple						=					{
    "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__		:					Union[str, Any]						=					{
    "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__		:					str						=					{
    "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__		:					Any						=					{
    "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__		:					str						=					{
    "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__		:					str						=					{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
SCREAMING_SNAKE_CASE__		:					Union[str, Any]						=					{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
SCREAMING_SNAKE_CASE__		:					List[str]						=					[]
SCREAMING_SNAKE_CASE__		:					Dict						=					[]







def     _a				(      lowercase__  :			Union[str, Any]     ,  lowercase__  :			int     ,  lowercase__  :			Any     ,  lowercase__  :			Optional[Any]     ,  lowercase__  :			List[Any] ):







     '''simple docstring'''


     for attribute in key.split('.' ):
          SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		getattr(lowercase__     ,  lowercase__ )

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

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







def     _a				(      lowercase__  :			Tuple     ,  lowercase__  :			Tuple ):







     '''simple docstring'''


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







def     _a				(      lowercase__  :			Union[str, Any]     ,  lowercase__  :			Optional[Any]     ,  lowercase__  :			Optional[int] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Tuple			=		[]

     if model_name == "encodec_24khz" or "encodec_32khz":
          SCREAMING_SNAKE_CASE__				:				Tuple			=		MAPPING_24K
     elif model_name == "encodec_48khz":
          SCREAMING_SNAKE_CASE__				:				str			=		MAPPING_48K
     else:
          raise ValueError(f'''Unsupported model: {model_name}''' )

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

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

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

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

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







@torch.no_grad()
def     _a				(      lowercase__  :			Tuple     ,  lowercase__  :			Union[str, Any]     ,  lowercase__  :			List[str]     ,  lowercase__  :			Optional[Any]=None     ,  lowercase__  :			List[Any]=None     ,  ):







     '''simple docstring'''


     if config_path is not None:
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		EncodecConfig.from_pretrained(lowercase__ )
     else:
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		EncodecConfig()

     if model_name == "encodec_24khz":
          pass  # config is already correct
     elif model_name == "encodec_32khz":
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[8, 5, 4, 4]
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		[2.2]
          SCREAMING_SNAKE_CASE__				:				Dict			=		64
          SCREAMING_SNAKE_CASE__				:				Dict			=		3_20_00
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		20_48
          SCREAMING_SNAKE_CASE__				:				Tuple			=		False
          SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		False
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		False
     elif model_name == "encodec_48khz":
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		[8, 5, 4, 2]
          SCREAMING_SNAKE_CASE__				:				Any			=		[3.0, 6.0, 12.0, 24.0]
          SCREAMING_SNAKE_CASE__				:				int			=		4_80_00
          SCREAMING_SNAKE_CASE__				:				List[str]			=		2
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		False
          SCREAMING_SNAKE_CASE__				:				Tuple			=		'time_group_norm'
          SCREAMING_SNAKE_CASE__				:				int			=		True
          SCREAMING_SNAKE_CASE__				:				List[str]			=		1.0
          SCREAMING_SNAKE_CASE__				:				Tuple			=		0.01
     else:
          raise ValueError(f'''Unknown model name: {model_name}''' )

     SCREAMING_SNAKE_CASE__				:				Any			=		EncodecModel(lowercase__ )

     SCREAMING_SNAKE_CASE__				:				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(lowercase__ )

     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.load(lowercase__ )
     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
          SCREAMING_SNAKE_CASE__				:				str			=		original_checkpoint['best_state']
     recursively_load_weights(lowercase__     ,  lowercase__     ,  lowercase__ )
     model.save_pretrained(lowercase__ )

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


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE__		:					int						=					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__		:					Dict						=					parser.parse_args()
      convert_checkpoint(
          args.model,
          args.checkpoint_path,
          args.pytorch_dump_folder_path,
          args.config_path,
          args.push_to_hub,
      )




import math
import sys







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		''
     try:
          with open(lowercase__     ,  'rb' ) as binary_file:
               SCREAMING_SNAKE_CASE__				:				Tuple			=		binary_file.read()
          for dat in data:
               SCREAMING_SNAKE_CASE__				:				Tuple			=		f'''{dat:08b}'''
               result += curr_byte
          return result
     except OSError:
          print('File not accessible' )
          sys.exit()







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				List[str]			=		{'0': '0', '1': '1'}
     SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				int			=		'', ''
     SCREAMING_SNAKE_CASE__				:				Tuple			=		len(lowercase__ )

     for i in range(len(lowercase__ ) ):
          curr_string += data_bits[i]
          if curr_string not in lexicon:
               continue

          SCREAMING_SNAKE_CASE__				:				int			=		lexicon[curr_string]
          result += last_match_id
          SCREAMING_SNAKE_CASE__				:				str			=		last_match_id + '0'

          if math.loga(lowercase__ ).is_integer():
               SCREAMING_SNAKE_CASE__				:				List[str]			=		{}
               for curr_key in list(lowercase__ ):
                    SCREAMING_SNAKE_CASE__				:				Optional[int]			=		lexicon.pop(lowercase__ )
               SCREAMING_SNAKE_CASE__				:				Optional[int]			=		new_lex

          SCREAMING_SNAKE_CASE__				:				Any			=		last_match_id + '1'
          index += 1
          SCREAMING_SNAKE_CASE__				:				Tuple			=		''
     return result







def     _a				(      lowercase__  :			str     ,  lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				str			=		8
     try:
          with open(lowercase__     ,  'wb' ) as opened_file:
               SCREAMING_SNAKE_CASE__				:				Dict			=		[
                   to_write[i : i + byte_length]
                   for i in range(0     ,  len(lowercase__ )     ,  lowercase__ )
               ]

               if len(result_byte_array[-1] ) % byte_length == 0:
                    result_byte_array.append('10000000' )
               else:
                    result_byte_array[-1] += "1" + "0" * (
                        byte_length - len(result_byte_array[-1] ) - 1
                    )

               for elem in result_byte_array[:-1]:
                    opened_file.write(int(lowercase__     ,  2 ).to_bytes(1     ,  byteorder='big' ) )
     except OSError:
          print('File not accessible' )
          sys.exit()







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Dict			=		0
     for letter in data_bits:
          if letter == "1":
               break
          counter += 1

     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		data_bits[counter:]
     SCREAMING_SNAKE_CASE__				:				int			=		data_bits[counter + 1 :]
     return data_bits







def     _a				(      lowercase__  :			str     ,  lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		read_file_binary(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Dict			=		remove_prefix(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Tuple			=		decompress_data(lowercase__ )
     write_file_binary(lowercase__     ,  lowercase__ )


if __name__ == "__main__":
      compress(sys.argv[1], sys.argv[2])



