

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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					str:
 for attribute in key.split('.'      ):
  __lowerCamelCase    :    Any 	=			getattr(lowerCamelCase__     ,			lowerCamelCase__      )

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

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






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






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					Any:
 __lowerCamelCase    :    Any 	=			[]

 if model_name == "encodec_24khz" or "encodec_32khz":
  __lowerCamelCase    :    str 	=			MAPPING_24K
 elif model_name == "encodec_48khz":
  __lowerCamelCase    :    Union[str, Any] 	=			MAPPING_48K
 else:
  raise ValueError(F"Unsupported model: {model_name}"      )

 for name, value in orig_dict.items():
  if should_ignore(lowerCamelCase__     ,			lowerCamelCase__      ):
   logger.info(F"{name} was ignored"      )
   continue

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

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

 logger.warning(F"Unused weights: {unused_weights}"      )






@torch.no_grad()
def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__=None     ,			lowerCamelCase__=None     ,			)  ->					Optional[int]:
 if config_path is not None:
  __lowerCamelCase    :    int 	=			EncodecConfig.from_pretrained(lowerCamelCase__      )
 else:
  __lowerCamelCase    :    Union[str, Any] 	=			EncodecConfig()

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

 __lowerCamelCase    :    Dict 	=			EncodecModel(lowerCamelCase__      )

 __lowerCamelCase    :    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(lowerCamelCase__      )

 __lowerCamelCase    :    str 	=			torch.load(lowerCamelCase__      )
 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
  __lowerCamelCase    :    str 	=			original_checkpoint['best_state']
 recursively_load_weights(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )
 model.save_pretrained(lowerCamelCase__      )

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


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,
     )







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a		={
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=["""RoFormerTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """RoFormerForCausalLM""",
					    """RoFormerForMaskedLM""",
					    """RoFormerForMultipleChoice""",
					    """RoFormerForQuestionAnswering""",
					    """RoFormerForSequenceClassification""",
					    """RoFormerForTokenClassification""",
					    """RoFormerLayer""",
					    """RoFormerModel""",
					    """RoFormerPreTrainedModel""",
					    """load_tf_weights_in_roformer""",
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFRoFormerForCausalLM""",
					    """TFRoFormerForMaskedLM""",
					    """TFRoFormerForMultipleChoice""",
					    """TFRoFormerForQuestionAnswering""",
					    """TFRoFormerForSequenceClassification""",
					    """TFRoFormerForTokenClassification""",
					    """TFRoFormerLayer""",
					    """TFRoFormerModel""",
					    """TFRoFormerPreTrainedModel""",
					]


try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """FlaxRoFormerForMaskedLM""",
					    """FlaxRoFormerForMultipleChoice""",
					    """FlaxRoFormerForQuestionAnswering""",
					    """FlaxRoFormerForSequenceClassification""",
					    """FlaxRoFormerForTokenClassification""",
					    """FlaxRoFormerModel""",
					    """FlaxRoFormerPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
					from .tokenization_roformer import RoFormerTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_roformer_fast import RoFormerTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roformer import (
										    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RoFormerForCausalLM,
										    RoFormerForMaskedLM,
										    RoFormerForMultipleChoice,
										    RoFormerForQuestionAnswering,
										    RoFormerForSequenceClassification,
										    RoFormerForTokenClassification,
										    RoFormerLayer,
										    RoFormerModel,
										    RoFormerPreTrainedModel,
										    load_tf_weights_in_roformer,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roformer import (
										    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRoFormerForCausalLM,
										    TFRoFormerForMaskedLM,
										    TFRoFormerForMultipleChoice,
										    TFRoFormerForQuestionAnswering,
										    TFRoFormerForSequenceClassification,
										    TFRoFormerForTokenClassification,
										    TFRoFormerLayer,
										    TFRoFormerModel,
										    TFRoFormerPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roformer import (
										    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaxRoFormerForMaskedLM,
										    FlaxRoFormerForMultipleChoice,
										    FlaxRoFormerForQuestionAnswering,
										    FlaxRoFormerForSequenceClassification,
										    FlaxRoFormerForTokenClassification,
										    FlaxRoFormerModel,
										    FlaxRoFormerPreTrainedModel,
										)


else:
					import sys

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




