

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

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







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			List[Any]:
      for attribute in key.split("""."""   ):
            UpperCamelCase      = getattr(_lowerCAmelCase		,						_lowerCAmelCase   )

      if weight_type is not None:
            UpperCamelCase      = getattr(_lowerCAmelCase		,						_lowerCAmelCase   ).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    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			Any:
      for key in ignore_keys:
            if key.endswith(""".*"""   ):
                  if name.startswith(key[:-1]   ):
                        return True
            elif ".*." in key:
                  UpperCamelCase      = key.split(""".*."""   )
                  if prefix in name and suffix in name:
                        return True
            elif key in name:
                  return True
      return False







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			Optional[int]:
      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(_lowerCAmelCase		,						_lowerCAmelCase   ):
                  logger.info(F"{name} was ignored"   )
                  continue

            UpperCamelCase      = False
            for key, mapped_key in MAPPING.items():
                  if "*" in key:
                        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(_lowerCAmelCase   )[0].split("""."""   )[-2]
                              UpperCamelCase      = mapped_key.replace("""*"""		,						_lowerCAmelCase   )
                        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(_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    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase=None		,						__UpperCamelCase=None		,						)->			Any:
      if config_path is not None:
            UpperCamelCase      = EncodecConfig.from_pretrained(_lowerCAmelCase   )
      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(_lowerCAmelCase   )

      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(_lowerCAmelCase   )

      UpperCamelCase      = 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
            UpperCamelCase      = 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__":
     SCREAMING_SNAKE_CASE__     =       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__     =       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 typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__     =       {
    'configuration_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     =       ['MobileBertTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     =       [
         'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MobileBertForMaskedLM',
         'MobileBertForMultipleChoice',
         'MobileBertForNextSentencePrediction',
         'MobileBertForPreTraining',
         'MobileBertForQuestionAnswering',
         'MobileBertForSequenceClassification',
         'MobileBertForTokenClassification',
         'MobileBertLayer',
         'MobileBertModel',
         'MobileBertPreTrainedModel',
         'load_tf_weights_in_mobilebert',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     =       [
         'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFMobileBertForMaskedLM',
         'TFMobileBertForMultipleChoice',
         'TFMobileBertForNextSentencePrediction',
         'TFMobileBertForPreTraining',
         'TFMobileBertForQuestionAnswering',
         'TFMobileBertForSequenceClassification',
         'TFMobileBertForTokenClassification',
         'TFMobileBertMainLayer',
         'TFMobileBertModel',
         'TFMobileBertPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_mobilebert import (
         MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         MobileBertConfig,
         MobileBertOnnxConfig,
     )
     from .tokenization_mobilebert import MobileBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mobilebert_fast import MobileBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mobilebert import (
              MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              MobileBertForMaskedLM,
              MobileBertForMultipleChoice,
              MobileBertForNextSentencePrediction,
              MobileBertForPreTraining,
              MobileBertForQuestionAnswering,
              MobileBertForSequenceClassification,
              MobileBertForTokenClassification,
              MobileBertLayer,
              MobileBertModel,
              MobileBertPreTrainedModel,
              load_tf_weights_in_mobilebert,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mobilebert import (
              TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFMobileBertForMaskedLM,
              TFMobileBertForMultipleChoice,
              TFMobileBertForNextSentencePrediction,
              TFMobileBertForPreTraining,
              TFMobileBertForQuestionAnswering,
              TFMobileBertForSequenceClassification,
              TFMobileBertForTokenClassification,
              TFMobileBertMainLayer,
              TFMobileBertModel,
              TFMobileBertPreTrainedModel,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE__     =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
