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

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

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     for attribute in key.split('.'							):
          lowercase_					:       List[Any]     				=					getattr(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)

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

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

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     for key in ignore_keys:
          if key.endswith('.*'							):
               if name.startswith(key[:-1]							):
                    return True
          elif ".*." in key:
               lowercase_				,lowercase_					:       int     				=					key.split('.*.'							)
               if prefix in name and suffix in name:
                    return True
          elif key in name:
               return True
     return False

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       Dict     				=					[]

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

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

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

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

@torch.no_grad()
def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_=None  ,       SCREAMING_SNAKE_CASE_=None  ,       ):
     if config_path is not None:
          lowercase_					:       List[Any]     				=					EncodecConfig.from_pretrained(SCREAMING_SNAKE_CASE_							)
     else:
          lowercase_					:       Optional[int]     				=					EncodecConfig()

     if model_name == "encodec_24khz":
          pass  # config is already correct
     elif model_name == "encodec_32khz":
          lowercase_					:       Tuple     				=					[8, 5, 4, 4]
          lowercase_					:       Union[str, Any]     				=					[2.2]
          lowercase_					:       List[Any]     				=					64
          lowercase_					:       Dict     				=					32_000
          lowercase_					:       List[str]     				=					2_048
          lowercase_					:       str     				=					False
          lowercase_					:       str     				=					False
          lowercase_					:       List[Any]     				=					False
     elif model_name == "encodec_48khz":
          lowercase_					:       int     				=					[8, 5, 4, 2]
          lowercase_					:       str     				=					[3.0, 6.0, 12.0, 24.0]
          lowercase_					:       Tuple     				=					48_000
          lowercase_					:       Optional[Any]     				=					2
          lowercase_					:       Dict     				=					False
          lowercase_					:       List[Any]     				=					'time_group_norm'
          lowercase_					:       int     				=					True
          lowercase_					:       List[Any]     				=					1.0
          lowercase_					:       Tuple     				=					0.01
     else:
          raise ValueError(f'''Unknown model name: {model_name}'''							)

     lowercase_					:       Any     				=					EncodecModel(SCREAMING_SNAKE_CASE_							)

     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(SCREAMING_SNAKE_CASE_							)

     lowercase_					:       List[str]     				=					torch.load(SCREAMING_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_					:       Optional[int]     				=					original_checkpoint['best_state']
     recursively_load_weights(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
     model.save_pretrained(SCREAMING_SNAKE_CASE_							)

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


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

 _A								=   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 __future__ import annotations

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):

     # preprocessing the first row
     for i in range(1  ,       len(matrix[0]							)							):
          matrix[0][i] += matrix[0][i - 1]

     # preprocessing the first column
     for i in range(1  ,       len(SCREAMING_SNAKE_CASE_							)							):
          matrix[i][0] += matrix[i - 1][0]

     # updating the path cost for current position
     for i in range(1  ,       len(SCREAMING_SNAKE_CASE_							)							):
          for j in range(1  ,       len(matrix[0]							)							):
               matrix[i][j] += min(matrix[i - 1][j]  ,       matrix[i][j - 1]							)

     return matrix[-1][-1]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




