


'''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()
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 					lowerCamelCase_ ( __UpperCamelCase					:       Tuple							,    __UpperCamelCase					:       Optional[Any]							,    __UpperCamelCase					:       List[Any]							,    __UpperCamelCase					:       int							,    __UpperCamelCase					:       Dict		)					->	List[Any]:







     """simple docstring"""
     for attribute in key.split('.'		):
          _A				=							getattr(__UpperCamelCase							,    __UpperCamelCase		)

     if weight_type is not None:
          _A				=							getattr(__UpperCamelCase							,    __UpperCamelCase		).shape
     else:
          _A				=							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				=							value
     elif weight_type == "weight_g":
          _A				=							value
     elif weight_type == "weight_v":
          _A				=							value
     elif weight_type == "bias":
          _A				=							value
     elif weight_type == "running_mean":
          _A				=							value
     elif weight_type == "running_var":
          _A				=							value
     elif weight_type == "num_batches_tracked":
          _A				=							value
     elif weight_type == "weight_ih_l0":
          _A				=							value
     elif weight_type == "weight_hh_l0":
          _A				=							value
     elif weight_type == "bias_ih_l0":
          _A				=							value
     elif weight_type == "bias_hh_l0":
          _A				=							value
     elif weight_type == "weight_ih_l1":
          _A				=							value
     elif weight_type == "weight_hh_l1":
          _A				=							value
     elif weight_type == "bias_ih_l1":
          _A				=							value
     elif weight_type == "bias_hh_l1":
          _A				=							value
     else:
          _A				=							value

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

def 					lowerCamelCase_ ( __UpperCamelCase					:       Optional[Any]							,    __UpperCamelCase					:       str		)					->	Dict:







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

def 					lowerCamelCase_ ( __UpperCamelCase					:       Dict							,    __UpperCamelCase					:       Optional[Any]							,    __UpperCamelCase					:       int		)					->	List[Any]:







     """simple docstring"""
     _A				=							[]

     if model_name == "encodec_24khz" or "encodec_32khz":
          _A				=							MAPPING_24K
     elif model_name == "encodec_48khz":
          _A				=							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

          _A				=							False
          for key, mapped_key in MAPPING.items():
               if "*" in key:
                    _A		,				_A				=							key.split('.*.'		)
                    if prefix in name and suffix in name:
                         _A				=							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				=							True
                    if "*" in mapped_key:
                         _A				=							name.split(__UpperCamelCase		)[0].split('.'		)[-2]
                         _A				=							mapped_key.replace('*'							,    __UpperCamelCase		)
                    if "weight_g" in name:
                         _A				=							'weight_g'
                    elif "weight_v" in name:
                         _A				=							'weight_v'
                    elif "weight_ih_l0" in name:
                         _A				=							'weight_ih_l0'
                    elif "weight_hh_l0" in name:
                         _A				=							'weight_hh_l0'
                    elif "bias_ih_l0" in name:
                         _A				=							'bias_ih_l0'
                    elif "bias_hh_l0" in name:
                         _A				=							'bias_hh_l0'
                    elif "weight_ih_l1" in name:
                         _A				=							'weight_ih_l1'
                    elif "weight_hh_l1" in name:
                         _A				=							'weight_hh_l1'
                    elif "bias_ih_l1" in name:
                         _A				=							'bias_ih_l1'
                    elif "bias_hh_l1" in name:
                         _A				=							'bias_hh_l1'
                    elif "bias" in name:
                         _A				=							'bias'
                    elif "weight" in name:
                         _A				=							'weight'
                    elif "running_mean" in name:
                         _A				=							'running_mean'
                    elif "running_var" in name:
                         _A				=							'running_var'
                    elif "num_batches_tracked" in name:
                         _A				=							'num_batches_tracked'
                    else:
                         _A				=							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 					lowerCamelCase_ ( __UpperCamelCase					:       str							,    __UpperCamelCase					:       Optional[Any]							,    __UpperCamelCase					:       Any							,    __UpperCamelCase					:       List[str]=None							,    __UpperCamelCase					:       List[str]=None							,    )					->	List[str]:







     """simple docstring"""
     if config_path is not None:
          _A				=							EncodecConfig.from_pretrained(__UpperCamelCase		)
     else:
          _A				=							EncodecConfig()

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

     _A				=							EncodecModel(__UpperCamelCase		)

     _A				=							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		)

     _A				=							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
          _A				=							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__":
    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,
    )









'''simple docstring'''
import colorsys

from PIL import Image  # type: ignore

def 					lowerCamelCase_ ( __UpperCamelCase					:       float							,    __UpperCamelCase					:       float							,    __UpperCamelCase					:       int		)					->	float:







     """simple docstring"""
     _A				=							x
     _A				=							y
     for step in range(__UpperCamelCase		):  # noqa: B007
          _A				=							a * a - b * b + x
          _A				=							2 * a * b + y
          _A				=							a_new

          # divergence happens for all complex number with an absolute value
          # greater than 4
          if a * a + b * b > 4:
               break
     return step / (max_step - 1)

def 					lowerCamelCase_ ( __UpperCamelCase					:       float		)					->	tuple:







     """simple docstring"""
     if distance == 1:
          return (0, 0, 0)
     else:
          return (2_5_5, 2_5_5, 2_5_5)

def 					lowerCamelCase_ ( __UpperCamelCase					:       float		)					->	tuple:







     """simple docstring"""
     if distance == 1:
          return (0, 0, 0)
     else:
          return tuple(round(i * 2_5_5		) for i in colorsys.hsv_to_rgb(__UpperCamelCase							,    1							,    1		)		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       int = 8_0_0							,    __UpperCamelCase					:       int = 6_0_0							,    __UpperCamelCase					:       float = -0.6							,    __UpperCamelCase					:       float = 0							,    __UpperCamelCase					:       float = 3.2							,    __UpperCamelCase					:       int = 5_0							,    __UpperCamelCase					:       bool = True							,    )					->	Image.Image:







     """simple docstring"""
     _A				=							Image.new('RGB'							,    (image_width, image_height)		)
     _A				=							img.load()

     # loop through the image-coordinates
     for image_x in range(__UpperCamelCase		):
          for image_y in range(__UpperCamelCase		):
               # determine the figure-coordinates based on the image-coordinates
               _A				=							figure_width / image_width * image_height
               _A				=							figure_center_x + (image_x / image_width - 0.5) * figure_width
               _A				=							figure_center_y + (image_y / image_height - 0.5) * figure_height

               _A				=							get_distance(__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase		)

               # color the corresponding pixel based on the selected coloring-function
               if use_distance_color_coding:
                    _A				=							get_color_coded_rgb(__UpperCamelCase		)
               else:
                    _A				=							get_black_and_white_rgb(__UpperCamelCase		)

     return img


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # colored version, full figure
    lowerCAmelCase						=					get_image()

    # uncomment for colored version, different section, zoomed in
    # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
    # figure_width = 0.8)

    # uncomment for black and white version, full figure
    # img = get_image(use_distance_color_coding = False)

    # uncomment to save the image
    # img.save("mandelbrot.png")

    img.show()





