



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

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

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[Any]	,   _snake_case	:Union[str, Any]	,   _snake_case	:List[str]	,   _snake_case	:Optional[int]	,   _snake_case	:Optional[Any]				)  ->     int:



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

   if weight_type is not None:
      _A      =       getattr(snake_case__	,   snake_case__				).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 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[int]	,   _snake_case	:Any				)  ->     List[str]:



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

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Dict	,   _snake_case	:List[Any]	,   _snake_case	:Union[str, Any]				)  ->     str:
   _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(snake_case__	,   snake_case__				):
         logger.info(F'''{name} was ignored'''				)
         continue

      _A      =       False
      for key, mapped_key in MAPPING.items():
         if "*" in key:
            _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(snake_case__				)[0].split('''.'''				)[-2]
               _A      =       mapped_key.replace('''*'''	,   snake_case__				)
            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(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 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[int]	,   _snake_case	:Optional[Any]	,   _snake_case	:str	,   _snake_case	:str=None	,   _snake_case	:Optional[Any]=None	,   )  ->     int:



   if config_path is not None:
      _A      =       EncodecConfig.from_pretrained(snake_case__				)
   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      =       64
      _A      =       32_000
      _A      =       2_048
      _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      =       48_000
      _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(snake_case__				)

   _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(snake_case__				)

   _A      =       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      =       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__":
   UpperCAmelCase_		 =					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."""
   )

   UpperCAmelCase_		 =					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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase     :							Any       						=     logging.get_logger(__name__)

lowercase     :							int       						=     {
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}





class 					A							(				__snake_case		):
     __magic_name__          =						'''timesformer'''





     def __init__(				self		,     SCREAMING_SNAKE_CASE=224		,     SCREAMING_SNAKE_CASE=16		,     SCREAMING_SNAKE_CASE=3		,     SCREAMING_SNAKE_CASE=8		,     SCREAMING_SNAKE_CASE=768		,     SCREAMING_SNAKE_CASE=12		,     SCREAMING_SNAKE_CASE=12		,     SCREAMING_SNAKE_CASE=3072		,     SCREAMING_SNAKE_CASE="gelu"		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.02		,     SCREAMING_SNAKE_CASE=1e-6		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE="divided_space_time"		,     SCREAMING_SNAKE_CASE=0		,     **SCREAMING_SNAKE_CASE		,     )					->							Tuple:






         """simple docstring"""






         super().__init__(**SCREAMING_SNAKE_CASE      )

         A			:  List[Any]   =		image_size
         A			:  List[str]   =		patch_size
         A			:  List[str]   =		num_channels
         A			:  str   =		num_frames

         A			:  Any   =		hidden_size
         A			:  Dict   =		num_hidden_layers
         A			:  Any   =		num_attention_heads
         A			:  Optional[Any]   =		intermediate_size
         A			:  Union[str, Any]   =		hidden_act
         A			:  Tuple   =		hidden_dropout_prob
         A			:  Optional[int]   =		attention_probs_dropout_prob
         A			:  Tuple   =		initializer_range
         A			:  List[Any]   =		layer_norm_eps
         A			:  Union[str, Any]   =		qkv_bias

         A			:  List[str]   =		attention_type
         A			:  Union[str, Any]   =		drop_path_rate




