






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()
lowerCamelCase__       		=  logging.get_logger("""transformers.models.encodec""")

lowerCamelCase__       		=  {
    """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""",
}
lowerCamelCase__       		=  {
    """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""",
}
lowerCamelCase__       		=  {
    """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""",
}
lowerCamelCase__       		=  {
    """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""",
}
lowerCamelCase__       		=  {
    """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""",
}
lowerCamelCase__       		=  {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
lowerCamelCase__       		=  {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
lowerCamelCase__       		=  []
lowerCamelCase__       		=  []






def 	UpperCamelCase	(							snake_case__  : Union[str, Any]						,snake_case__  : Tuple						,snake_case__  : Optional[int]						,snake_case__  : str						,snake_case__  : Tuple	):




     '''simple docstring'''






     for attribute in key.split("""."""	):
          __snake_case      :Optional[int] 							=    getattr(snake_case__						,snake_case__	)

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

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






def 	UpperCamelCase	(							snake_case__  : Tuple						,snake_case__  : Dict	):




     '''simple docstring'''






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






def 	UpperCamelCase	(							snake_case__  : Dict						,snake_case__  : Dict						,snake_case__  : Optional[int]	):




     '''simple docstring'''






     __snake_case      :Any 							=    []

     if model_name == "encodec_24khz" or "encodec_32khz":
          __snake_case      :Tuple 							=    MAPPING_24K
     elif model_name == "encodec_48khz":
          __snake_case      :Tuple 							=    MAPPING_48K
     else:
          raise ValueError(f'''Unsupported model: {model_name}'''	)

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

          __snake_case      :Union[str, Any] 							=    False
          for key, mapped_key in MAPPING.items():
               if "*" in key:
                    __snake_case							,			__snake_case      :List[str] 							=    key.split(""".*."""	)
                    if prefix in name and suffix in name:
                         __snake_case      :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

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

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






@torch.no_grad()
def 	UpperCamelCase	(							snake_case__  : Any						,snake_case__  : List[str]						,snake_case__  : Union[str, Any]						,snake_case__  : Optional[Any]=None						,snake_case__  : Tuple=None						,):




     '''simple docstring'''






     if config_path is not None:
          __snake_case      :Any 							=    EncodecConfig.from_pretrained(snake_case__	)
     else:
          __snake_case      :List[Any] 							=    EncodecConfig()

     if model_name == "encodec_24khz":
          pass  # config is already correct
     elif model_name == "encodec_32khz":
          __snake_case      :Optional[int] 							=    [8, 5, 4, 4]
          __snake_case      :Optional[Any] 							=    [2.2]
          __snake_case      :int 							=    64
          __snake_case      :List[Any] 							=    3_2000
          __snake_case      :str 							=    2048
          __snake_case      :Union[str, Any] 							=    False
          __snake_case      :Any 							=    False
          __snake_case      :Tuple 							=    False
     elif model_name == "encodec_48khz":
          __snake_case      :Tuple 							=    [8, 5, 4, 2]
          __snake_case      :Any 							=    [3.0, 6.0, 1_2.0, 2_4.0]
          __snake_case      :Union[str, Any] 							=    4_8000
          __snake_case      :Any 							=    2
          __snake_case      :Any 							=    False
          __snake_case      :Dict 							=    """time_group_norm"""
          __snake_case      :Optional[Any] 							=    True
          __snake_case      :Optional[int] 							=    1.0
          __snake_case      :Union[str, Any] 							=    0.0_1
     else:
          raise ValueError(f'''Unknown model name: {model_name}'''	)

     __snake_case      :str 							=    EncodecModel(snake_case__	)

     __snake_case      :List[Any] 							=    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(snake_case__	)

     __snake_case      :Dict 							=    torch.load(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
          __snake_case      :Optional[Any] 							=    original_checkpoint["""best_state"""]
     recursively_load_weights(snake_case__						,snake_case__						,snake_case__	)
     model.save_pretrained(snake_case__	)

     if repo_id:
          print("""Pushing to the hub..."""	)
          feature_extractor.push_to_hub(snake_case__	)
          model.push_to_hub(snake_case__	)


if __name__ == "__main__":
     lowerCamelCase__       		=  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."""
     )

     lowerCamelCase__       		=  parser.parse_args()
     convert_checkpoint(
         args.model,
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.push_to_hub,
     )










from ...configuration_utils import PretrainedConfig


lowerCamelCase__       		=  {
    """google/tapas-base-finetuned-sqa""": (
        """https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-wtq""": (
        """https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-wikisql-supervised""": (
        """https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-tabfact""": (
        """https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json"""
    ),
}




class       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					Tuple								=			"tapas"



       def __init__(				self      ,    a__=3_05_22      ,    a__=7_68      ,    a__=12      ,    a__=12      ,    a__=30_72      ,    a__="gelu"      ,    a__=0.1      ,    a__=0.1      ,    a__=10_24      ,    a__=[3, 2_56, 2_56, 2, 2_56, 2_56, 10]      ,    a__=0.02      ,    a__=1e-12      ,    a__=0      ,    a__=10.0      ,    a__=0      ,    a__=1.0      ,    a__=None      ,    a__=1.0      ,    a__=False      ,    a__=None      ,    a__=1.0      ,    a__=1.0      ,    a__=False      ,    a__=False      ,    a__="ratio"      ,    a__=None      ,    a__=None      ,    a__=64      ,    a__=32      ,    a__=False      ,    a__=True      ,    a__=False      ,    a__=False      ,    a__=True      ,    a__=False      ,    a__=None      ,    a__=None      ,    **a__      ,    )			->   Any:






            '''simple docstring'''





            super().__init__(pad_token_id=a__      ,    **a__     )

            # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
            __snake_case      :Optional[Any] 							=    vocab_size
            __snake_case      :Tuple 							=    hidden_size
            __snake_case      :Tuple 							=    num_hidden_layers
            __snake_case      :Any 							=    num_attention_heads
            __snake_case      :List[str] 							=    hidden_act
            __snake_case      :List[str] 							=    intermediate_size
            __snake_case      :int 							=    hidden_dropout_prob
            __snake_case      :List[Any] 							=    attention_probs_dropout_prob
            __snake_case      :Any 							=    max_position_embeddings
            __snake_case      :Optional[int] 							=    type_vocab_sizes
            __snake_case      :Tuple 							=    initializer_range
            __snake_case      :Any 							=    layer_norm_eps

            # Fine-tuning task hyperparameters
            __snake_case      :Optional[int] 							=    positive_label_weight
            __snake_case      :Optional[Any] 							=    num_aggregation_labels
            __snake_case      :Dict 							=    aggregation_loss_weight
            __snake_case      :str 							=    use_answer_as_supervision
            __snake_case      :int 							=    answer_loss_importance
            __snake_case      :List[Any] 							=    use_normalized_answer_loss
            __snake_case      :List[str] 							=    huber_loss_delta
            __snake_case      :Any 							=    temperature
            __snake_case      :Optional[int] 							=    aggregation_temperature
            __snake_case      :List[str] 							=    use_gumbel_for_cells
            __snake_case      :Optional[int] 							=    use_gumbel_for_aggregation
            __snake_case      :Optional[Any] 							=    average_approximation_function
            __snake_case      :Union[str, Any] 							=    cell_selection_preference
            __snake_case      :Tuple 							=    answer_loss_cutoff
            __snake_case      :Union[str, Any] 							=    max_num_rows
            __snake_case      :Optional[int] 							=    max_num_columns
            __snake_case      :str 							=    average_logits_per_cell
            __snake_case      :Union[str, Any] 							=    select_one_column
            __snake_case      :Optional[int] 							=    allow_empty_column_selection
            __snake_case      :Optional[Any] 							=    init_cell_selection_weights_to_zero
            __snake_case      :Tuple 							=    reset_position_index_per_cell
            __snake_case      :Any 							=    disable_per_token_loss

            # Aggregation hyperparameters
            __snake_case      :List[str] 							=    aggregation_labels
            __snake_case      :int 							=    no_aggregation_label_index

            if isinstance(self.aggregation_labels      ,    a__     ):
                 __snake_case      :Any 							=    {int(a__     ): v for k, v in aggregation_labels.items()}


