




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()
snake_case_ : Tuple    		=       logging.get_logger("transformers.models.encodec")

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


def    A						(__A   :    str   ,				__A   :    Optional[Any]   ,				__A   :    List[str]   ,				__A   :    int   ,				__A   :    List[Any]       )   ->      Tuple:







     """simple docstring"""


     for attribute in key.split('''.'''       ):
          UpperCAmelCase_          =							getattr(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_       )

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

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


def    A						(__A   :    Optional[Any]   ,				__A   :    Tuple       )   ->      Union[str, Any]:







     """simple docstring"""


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


def    A						(__A   :    str   ,				__A   :    List[str]   ,				__A   :    str       )   ->      Union[str, Any]:







     """simple docstring"""


     UpperCAmelCase_          =							[]

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

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

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

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







     """simple docstring"""


     if config_path is not None:
          UpperCAmelCase_          =							EncodecConfig.from_pretrained(SCREAMING_SNAKE_CASE_       )
     else:
          UpperCAmelCase_          =							EncodecConfig()

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

     UpperCAmelCase_          =							EncodecModel(SCREAMING_SNAKE_CASE_       )

     UpperCAmelCase_          =							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_       )

     UpperCAmelCase_          =							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
          UpperCAmelCase_          =							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__":
    snake_case_ : Tuple    		=       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."
    )

    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,
    )










"""simple docstring"""




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel


def     lowercase			(SCREAMING_SNAKE_CASE_		:				BertModel ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				str  )   ->						Dict:
						SCREAMING_SNAKE_CASE													=					('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

						SCREAMING_SNAKE_CASE													=					(
						    ('layer.', 'layer_'),
						    ('word_embeddings.weight', 'word_embeddings'),
						    ('position_embeddings.weight', 'position_embeddings'),
						    ('token_type_embeddings.weight', 'token_type_embeddings'),
						    ('.', '/'),
						    ('LayerNorm/weight', 'LayerNorm/gamma'),
						    ('LayerNorm/bias', 'LayerNorm/beta'),
						    ('weight', 'kernel'),
						)

						if not os.path.isdir(SCREAMING_SNAKE_CASE_  ):
												os.makedirs(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					model.state_dict()

						def to_tf_var_name(SCREAMING_SNAKE_CASE_		:				str  ):
												for patt, repl in iter(SCREAMING_SNAKE_CASE_  ):
																		SCREAMING_SNAKE_CASE													=					name.replace(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
												return F'bert/{name}'

						def create_tf_var(SCREAMING_SNAKE_CASE_		:				np.ndarray ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				tf.Session  ):
												SCREAMING_SNAKE_CASE													=					tf.dtypes.as_dtype(tensor.dtype  )
												SCREAMING_SNAKE_CASE													=					tf.get_variable(dtype=SCREAMING_SNAKE_CASE_ ,			shape=tensor.shape ,			name=SCREAMING_SNAKE_CASE_ ,			initializer=tf.zeros_initializer()  )
												session.run(tf.variables_initializer([tf_var]  )  )
												session.run(SCREAMING_SNAKE_CASE_  )
												return tf_var

						tf.reset_default_graph()
						with tf.Session() as session:
												for var_name in state_dict:
																		SCREAMING_SNAKE_CASE													=					to_tf_var_name(SCREAMING_SNAKE_CASE_  )
																		SCREAMING_SNAKE_CASE													=					state_dict[var_name].numpy()
																		if any(x in var_name for x in tensors_to_transpose  ):
																								SCREAMING_SNAKE_CASE													=					torch_tensor.T
																		SCREAMING_SNAKE_CASE													=					create_tf_var(tensor=SCREAMING_SNAKE_CASE_ ,			name=SCREAMING_SNAKE_CASE_ ,			session=SCREAMING_SNAKE_CASE_  )
																		tf.keras.backend.set_value(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
																		SCREAMING_SNAKE_CASE													=					session.run(SCREAMING_SNAKE_CASE_  )
																		print(F'Successfully created {tf_name}: {np.allclose(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )}'  )

												SCREAMING_SNAKE_CASE													=					tf.train.Saver(tf.trainable_variables()  )
												saver.save(SCREAMING_SNAKE_CASE_ ,			os.path.join(SCREAMING_SNAKE_CASE_ ,			model_name.replace('-' ,			'_'  ) + '.ckpt'  )  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Union[str, Any]=None  )   ->						Any:
						SCREAMING_SNAKE_CASE													=					argparse.ArgumentParser()
						parser.add_argument('--model_name' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='model name e.g. bert-base-uncased'  )
						parser.add_argument(
						    '--cache_dir' ,			type=SCREAMING_SNAKE_CASE_ ,			default=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='Directory containing pytorch model'  )
						parser.add_argument('--pytorch_model_path' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='/path/to/<pytorch-model-name>.bin'  )
						parser.add_argument('--tf_cache_dir' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='Directory in which to save tensorflow model'  )
						SCREAMING_SNAKE_CASE													=					parser.parse_args(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					BertModel.from_pretrained(
						    pretrained_model_name_or_path=args.model_name ,			state_dict=torch.load(args.pytorch_model_path  ) ,			cache_dir=args.cache_dir ,			)

						convert_pytorch_checkpoint_to_tf(model=SCREAMING_SNAKE_CASE_ ,			ckpt_dir=args.tf_cache_dir ,			model_name=args.model_name  )


if __name__ == "__main__":
	main()


