






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

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




def        _SCREAMING_SNAKE_CASE  (				A  :       Any  ,		A  :       Tuple  ,		A  :       Tuple  ,		A  :       Optional[Any]  ,		A  :       Union[str, Any]  )    -> Dict:







   """simple docstring"""
   for attribute in key.split('.'  ):
      __snake_case				:      str     							= getattr(A  ,		A  )

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

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




def        _SCREAMING_SNAKE_CASE  (				A  :       Optional[int]  ,		A  :       List[Any]  )    -> Optional[Any]:







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




def        _SCREAMING_SNAKE_CASE  (				A  :       Union[str, Any]  ,		A  :       List[str]  ,		A  :       Any  )    -> Tuple:







   """simple docstring"""
   __snake_case				:      List[Any]     							= []

   if model_name == "encodec_24khz" or "encodec_32khz":
      __snake_case				:      Dict     							= MAPPING_24K
   elif model_name == "encodec_48khz":
      __snake_case				:      Optional[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

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







   """simple docstring"""
   if config_path is not None:
      __snake_case				:      Any     							= EncodecConfig.from_pretrained(A  )
   else:
      __snake_case				:      Tuple     							= 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[int]     							= [2.2]
      __snake_case				:      Tuple     							= 64
      __snake_case				:      List[str]     							= 3_20_00
      __snake_case				:      Dict     							= 20_48
      __snake_case				:      Any     							= False
      __snake_case				:      List[Any]     							= False
      __snake_case				:      Any     							= False
   elif model_name == "encodec_48khz":
      __snake_case				:      Tuple     							= [8, 5, 4, 2]
      __snake_case				:      Any     							= [3.0, 6.0, 12.0, 24.0]
      __snake_case				:      int     							= 4_80_00
      __snake_case				:      Optional[Any]     							= 2
      __snake_case				:      str     							= False
      __snake_case				:      Union[str, Any]     							= 'time_group_norm'
      __snake_case				:      List[str]     							= True
      __snake_case				:      Tuple     							= 1.0
      __snake_case				:      Tuple     							= 0.01
   else:
      raise ValueError(F"""Unknown model name: {model_name}"""  )

   __snake_case				:      List[Any]     							= EncodecModel(A  )

   __snake_case				:      Optional[int]     							= 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  )

   __snake_case				:      Union[str, 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
      __snake_case				:      str     							= 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__":
      __A    	=   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.'''
      )

      __A    	=   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 Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


__A    	=   logging.get_logger(__name__)




class a_    (    UpperCamelCase_							):
   _snake_case        =							["""pixel_values"""]







   def __init__(self					,   __a = True					,   __a = None					,   __a = None					,   __a = PILImageResampling.BILINEAR					,   __a = True					,   __a = 1 / 2_5_5					,   __a = True					,   __a = None					,   __a = None					,   **__a					,   )			->      None:
      """simple docstring"""






      super().__init__(**__a)
      __snake_case				:      Tuple     							= size if size is not None else {'shortest_edge': 3_8_4}
      __snake_case				:      List[Any]     							= get_size_dict(__a					,   default_to_square=__a)

      __snake_case				:      int     							= do_resize
      __snake_case				:      List[str]     							= size
      # Default value set here for backwards compatibility where the value in config is None
      __snake_case				:      Any     							= crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
      __snake_case				:      Tuple     							= resample
      __snake_case				:      Dict     							= do_rescale
      __snake_case				:      Any     							= rescale_factor
      __snake_case				:      str     							= do_normalize
      __snake_case				:      Union[str, Any]     							= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      __snake_case				:      Dict     							= image_std if image_std is not None else IMAGENET_STANDARD_STD







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a					,   __a = PILImageResampling.BICUBIC					,   __a = None					,   **__a					,   )			->      np.ndarray:
      """simple docstring"""






      __snake_case				:      Dict     							= get_size_dict(__a					,   default_to_square=__a)
      if "shortest_edge" not in size:
         raise ValueError(F"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}""")
      __snake_case				:      List[str]     							= size['shortest_edge']

      if shortest_edge < 3_8_4:
         # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
         __snake_case				:      Any     							= int(shortest_edge / crop_pct)
         __snake_case				:      Any     							= get_resize_output_image_size(__a					,   size=__a					,   default_to_square=__a)
         __snake_case				:      int     							= resize(image=__a					,   size=__a					,   resample=__a					,   data_format=__a					,   **__a)
         # then crop to (shortest_edge, shortest_edge)
         return center_crop(image=__a					,   size=(shortest_edge, shortest_edge)					,   data_format=__a					,   **__a)
      else:
         # warping (no cropping) when evaluated at 384 or larger
         return resize(
             __a					,   size=(shortest_edge, shortest_edge)					,   resample=__a					,   data_format=__a					,   **__a)







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a = None					,   **__a					,   )			->      Any:
      """simple docstring"""






      return rescale(__a					,   scale=__a					,   data_format=__a					,   **__a)







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a					,   __a = None					,   **__a					,   )			->      np.ndarray:
      """simple docstring"""






      return normalize(__a					,   mean=__a					,   std=__a					,   data_format=__a					,   **__a)







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = ChannelDimension.FIRST					,   **__a					,   )			->      PIL.Image.Image:
      """simple docstring"""






      __snake_case				:      Optional[int]     							= do_resize if do_resize is not None else self.do_resize
      __snake_case				:      Dict     							= crop_pct if crop_pct is not None else self.crop_pct
      __snake_case				:      Tuple     							= resample if resample is not None else self.resample
      __snake_case				:      Any     							= do_rescale if do_rescale is not None else self.do_rescale
      __snake_case				:      Any     							= rescale_factor if rescale_factor is not None else self.rescale_factor
      __snake_case				:      Union[str, Any]     							= do_normalize if do_normalize is not None else self.do_normalize
      __snake_case				:      Optional[int]     							= image_mean if image_mean is not None else self.image_mean
      __snake_case				:      Optional[Any]     							= image_std if image_std is not None else self.image_std

      __snake_case				:      List[str]     							= size if size is not None else self.size
      __snake_case				:      Any     							= get_size_dict(__a					,   default_to_square=__a)

      __snake_case				:      Dict     							= make_list_of_images(__a)

      if not valid_images(__a):
         raise ValueError(
             'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
             'torch.Tensor, tf.Tensor or jax.ndarray.')

      if do_resize and size is None or resample is None:
         raise ValueError('Size and resample must be specified if do_resize is True.')

      if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
         raise ValueError('crop_pct must be specified if size < 384.')

      if do_rescale and rescale_factor is None:
         raise ValueError('Rescale factor must be specified if do_rescale is True.')

      if do_normalize and (image_mean is None or image_std is None):
         raise ValueError('Image mean and std must be specified if do_normalize is True.')

      # All transformations expect numpy arrays.
      __snake_case				:      Tuple     							= [to_numpy_array(__a) for image in images]

      if do_resize:
         __snake_case				:      Optional[int]     							= [self.resize(image=__a					,   size=__a					,   crop_pct=__a					,   resample=__a) for image in images]

      if do_rescale:
         __snake_case				:      Optional[int]     							= [self.rescale(image=__a					,   scale=__a) for image in images]

      if do_normalize:
         __snake_case				:      Any     							= [self.normalize(image=__a					,   mean=__a					,   std=__a) for image in images]

      __snake_case				:      Dict     							= [to_channel_dimension_format(__a					,   __a) for image in images]

      __snake_case				:      Union[str, Any]     							= {'pixel_values': images}
      return BatchFeature(data=__a					,   tensor_type=__a)