
'''simple docstring'''




# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    'stable diffusion controlnet',
    '0.22.0',
    'Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.',
    standard_warn=False,
    stacklevel=3,
)






'''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()
lowercase     :							List[str]       						=     logging.get_logger('transformers.models.encodec')

lowercase     :							str       						=     {
    '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',
}
lowercase     :							Optional[int]       						=     {
    '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',
}
lowercase     :							int       						=     {
    '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',
}
lowercase     :							int       						=     {
    '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',
}
lowercase     :							List[Any]       						=     {
    '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',
}
lowercase     :							Dict       						=     {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
lowercase     :							List[str]       						=     {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
lowercase     :							Union[str, Any]       						=     []
lowercase     :							int       						=     []







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    for attribute in key.split('''.'''		):
        A			:  int   =		getattr(snake_case__						,							snake_case__		)

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

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







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



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







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  List[Any]   =		[]

    if model_name == "encodec_24khz" or "encodec_32khz":
        A			:  str   =		MAPPING_24K
    elif model_name == "encodec_48khz":
        A			:  List[Any]   =		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

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

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

                A			:  Union[str, Any]   =		True
                if "*" in mapped_key:
                    A			:  List[str]   =		name.split(snake_case__		)[0].split('''.'''		)[-2]
                    A			:  Optional[Any]   =		mapped_key.replace('''*'''						,							snake_case__		)
                if "weight_g" in name:
                    A			:  Dict   =		'''weight_g'''
                elif "weight_v" in name:
                    A			:  List[str]   =		'''weight_v'''
                elif "weight_ih_l0" in name:
                    A			:  int   =		'''weight_ih_l0'''
                elif "weight_hh_l0" in name:
                    A			:  Any   =		'''weight_hh_l0'''
                elif "bias_ih_l0" in name:
                    A			:  Dict   =		'''bias_ih_l0'''
                elif "bias_hh_l0" in name:
                    A			:  List[Any]   =		'''bias_hh_l0'''
                elif "weight_ih_l1" in name:
                    A			:  Any   =		'''weight_ih_l1'''
                elif "weight_hh_l1" in name:
                    A			:  Tuple   =		'''weight_hh_l1'''
                elif "bias_ih_l1" in name:
                    A			:  Dict   =		'''bias_ih_l1'''
                elif "bias_hh_l1" in name:
                    A			:  List[str]   =		'''bias_hh_l1'''
                elif "bias" in name:
                    A			:  int   =		'''bias'''
                elif "weight" in name:
                    A			:  Optional[Any]   =		'''weight'''
                elif "running_mean" in name:
                    A			:  Optional[Any]   =		'''running_mean'''
                elif "running_var" in name:
                    A			:  Dict   =		'''running_var'''
                elif "num_batches_tracked" in name:
                    A			:  List[str]   =		'''num_batches_tracked'''
                else:
                    A			:  int   =		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 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__						,							snake_case__=None						,							snake_case__=None						,							):




    '''simple docstring'''



    if config_path is not None:
        A			:  Dict   =		EncodecConfig.from_pretrained(snake_case__		)
    else:
        A			:  Dict   =		EncodecConfig()

    if model_name == "encodec_24khz":
        pass  # config is already correct
    elif model_name == "encodec_32khz":
        A			:  Optional[Any]   =		[8, 5, 4, 4]
        A			:  List[Any]   =		[2.2]
        A			:  Tuple   =		64
        A			:  str   =		3_2000
        A			:  Optional[Any]   =		2048
        A			:  Optional[int]   =		False
        A			:  Optional[int]   =		False
        A			:  List[Any]   =		False
    elif model_name == "encodec_48khz":
        A			:  List[str]   =		[8, 5, 4, 2]
        A			:  Any   =		[3.0, 6.0, 12.0, 24.0]
        A			:  Tuple   =		4_8000
        A			:  Optional[int]   =		2
        A			:  Dict   =		False
        A			:  Union[str, Any]   =		'''time_group_norm'''
        A			:  List[Any]   =		True
        A			:  Union[str, Any]   =		1.0
        A			:  Union[str, Any]   =		0.01
    else:
        raise ValueError(F'Unknown model name: {model_name}'		)

    A			:  Optional[Any]   =		EncodecModel(snake_case__		)

    A			:  Optional[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__		)

    A			:  Optional[int]   =		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
        A			:  Union[str, 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__":
      lowercase     :							str       						=     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.'
      )

      lowercase     :							Union[str, Any]       						=     parser.parse_args()
      convert_checkpoint(
          args.model,
          args.checkpoint_path,
          args.pytorch_dump_folder_path,
          args.config_path,
          args.push_to_hub,
      )




