
'''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 					UpperCAmelCase_		(__a      :    Union[str, Any] ,	__a      :    Dict ,	__a      :    Any ,	__a      :    Dict ,	__a      :    int ):






   """simple docstring"""

   for attribute in key.split('.' ):
      _a				:	List[Any]			=   getattr(__a ,	__a )

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

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





def 					UpperCAmelCase_		(__a      :    Tuple ,	__a      :    Optional[Any] ):






   """simple docstring"""

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





def 					UpperCAmelCase_		(__a      :    List[str] ,	__a      :    str ,	__a      :    Optional[int] ):






   """simple docstring"""

   _a				:	Optional[int]			=   []

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

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

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

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

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





@torch.no_grad()
def 					UpperCAmelCase_		(__a      :    Optional[Any] ,	__a      :    Any ,	__a      :    Dict ,	__a      :    List[str]=None ,	__a      :    str=None ,	):






   """simple docstring"""

   if config_path is not None:
      _a				:	str			=   EncodecConfig.from_pretrained(__a )
   else:
      _a				:	Optional[int]			=   EncodecConfig()

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

   _a				:	Union[str, Any]			=   EncodecModel(__a )

   _a				:	Tuple			=   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(__a )

   _a				:	Any			=   torch.load(__a )
   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				:	List[Any]			=   original_checkpoint['best_state']
   recursively_load_weights(__a ,	__a ,	__a )
   model.save_pretrained(__a )

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


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 unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  @property
  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	int			=   UNetaDModel(
         block_out_channels=(32, 64)							,layers_per_block=2							,sample_size=32							,in_channels=3							,out_channels=3							,down_block_types=('DownBlock2D', 'AttnDownBlock2D')							,up_block_types=('AttnUpBlock2D', 'UpBlock2D')							,)
     return model


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a				:	str			=   self.dummy_uncond_unet
     _a				:	int			=   PNDMScheduler()

     _a				:	str			=   PNDMPipeline(unet=_a							,scheduler=_a       )
     pndm.to(_a       )
     pndm.set_progress_bar_config(disable=_a       )

     _a				:	Optional[int]			=   torch.manual_seed(0       )
     _a				:	Optional[Any]			=   pndm(generator=_a							,num_inference_steps=20							,output_type='numpy'       ).images

     _a				:	List[str]			=   torch.manual_seed(0       )
     _a				:	Any			=   pndm(generator=_a							,num_inference_steps=20							,output_type='numpy'							,return_dict=_a       )[0]

     _a				:	List[Any]			=   image[0, -3:, -3:, -1]
     _a				:	Any			=   image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     _a				:	List[Any]			=   np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

     assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1E-2







@slow
@require_torch
class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a				:	List[str]			=   'google/ddpm-cifar10-32'

     _a				:	str			=   UNetaDModel.from_pretrained(_a       )
     _a				:	Union[str, Any]			=   PNDMScheduler()

     _a				:	Tuple			=   PNDMPipeline(unet=_a							,scheduler=_a       )
     pndm.to(_a       )
     pndm.set_progress_bar_config(disable=_a       )
     _a				:	str			=   torch.manual_seed(0       )
     _a				:	Optional[Any]			=   pndm(generator=_a							,output_type='numpy'       ).images

     _a				:	Dict			=   image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     _a				:	Tuple			=   np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]       )

     assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2



