




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   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	Any:

    """simple docstring"""


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

    if weight_type is not None:
        __UpperCamelCase  		=						getattr(__UpperCamelCase					,     __UpperCamelCase      ).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 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	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 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	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(__UpperCamelCase					,     __UpperCamelCase      ):
            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(__UpperCamelCase      )[0].split('''.'''      )[-2]
                    __UpperCamelCase  		=						mapped_key.replace('''*'''					,     __UpperCamelCase      )
                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(__UpperCamelCase					,     __UpperCamelCase					,     __UpperCamelCase					,     __UpperCamelCase					,     __UpperCamelCase      )
            continue
        if not is_used:
            unused_weights.append(__UpperCamelCase      )

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




@torch.no_grad()
def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_=None					,     lowercase_=None					,     )       ->	int:

    """simple docstring"""


    if config_path is not None:
        __UpperCamelCase  		=						EncodecConfig.from_pretrained(__UpperCamelCase      )
    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  		=						3_20_00
        __UpperCamelCase  		=						20_48
        __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  		=						4_80_00
        __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(__UpperCamelCase      )

    __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(__UpperCamelCase      )

    __UpperCamelCase  		=						torch.load(__UpperCamelCase      )
    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(__UpperCamelCase					,     __UpperCamelCase					,     __UpperCamelCase      )
    model.save_pretrained(__UpperCamelCase      )

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


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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_   		=     {
    "configuration_longformer": [
        "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "LongformerConfig",
        "LongformerOnnxConfig",
    ],
    "tokenization_longformer": ["LongformerTokenizer"],
}

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   		=     [
     "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LongformerForMaskedLM",
     "LongformerForMultipleChoice",
     "LongformerForQuestionAnswering",
     "LongformerForSequenceClassification",
     "LongformerForTokenClassification",
     "LongformerModel",
     "LongformerPreTrainedModel",
     "LongformerSelfAttention",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   		=     [
     "TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFLongformerForMaskedLM",
     "TFLongformerForMultipleChoice",
     "TFLongformerForQuestionAnswering",
     "TFLongformerForSequenceClassification",
     "TFLongformerForTokenClassification",
     "TFLongformerModel",
     "TFLongformerPreTrainedModel",
     "TFLongformerSelfAttention",
 ]


if TYPE_CHECKING:
 from .configuration_longformer import (
     LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     LongformerConfig,
     LongformerOnnxConfig,
 )
 from .tokenization_longformer import LongformerTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_longformer_fast import LongformerTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_longformer import (
      LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      LongformerForMaskedLM,
      LongformerForMultipleChoice,
      LongformerForQuestionAnswering,
      LongformerForSequenceClassification,
      LongformerForTokenClassification,
      LongformerModel,
      LongformerPreTrainedModel,
      LongformerSelfAttention,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_longformer import (
      TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLongformerForMaskedLM,
      TFLongformerForMultipleChoice,
      TFLongformerForQuestionAnswering,
      TFLongformerForSequenceClassification,
      TFLongformerForTokenClassification,
      TFLongformerModel,
      TFLongformerPreTrainedModel,
      TFLongformerSelfAttention,
  )

else:
 import sys

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






