






'''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				:      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				:      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'''






import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
      from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
      from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
      from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


__A    	=   DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

__A    	=   '''main'''
# Default branch name
__A    	=   '''f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'''
# One particular commit (not the top of `main`)
__A    	=   '''aaaaaaa'''
# This commit does not exist, so we should 404.

__A    	=   '''d9e9f15bc825e4b2c9249e9578f884bbcb5e3684'''
# Sha-1 of config.json on the top of `main`, for checking purposes
__A    	=   '''4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'''




@contextlib.contextmanager
def        _SCREAMING_SNAKE_CASE  (				)    -> List[Any]:







   """simple docstring"""
   print('Welcome!'  )
   yield
   print('Bye!'  )




@contextlib.contextmanager
def        _SCREAMING_SNAKE_CASE  (				)    -> Optional[Any]:







   """simple docstring"""
   print('Bonjour!'  )
   yield
   print('Au revoir!'  )




class a_    (    unittest.TestCase							):







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      assert transformers.__spec__ is not None
      assert importlib.util.find_spec('transformers') is not None




class a_    (    unittest.TestCase							):







   @unittest.mock.patch('sys.stdout'					,   new_callable=io.StringIO)
   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      int:
      """simple docstring"""






      with ContextManagers([]):
         print('Transformers are awesome!')
      # The print statement adds a new line at the end of the output
      self.assertEqual(mock_stdout.getvalue()					,   'Transformers are awesome!\n')







   @unittest.mock.patch('sys.stdout'					,   new_callable=io.StringIO)
   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      List[str]:
      """simple docstring"""






      with ContextManagers([context_en()]):
         print('Transformers are awesome!')
      # The output should be wrapped with an English welcome and goodbye
      self.assertEqual(mock_stdout.getvalue()					,   'Welcome!\nTransformers are awesome!\nBye!\n')







   @unittest.mock.patch('sys.stdout'					,   new_callable=io.StringIO)
   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      Tuple:
      """simple docstring"""






      with ContextManagers([context_fr(), context_en()]):
         print('Transformers are awesome!')
      # The output should be wrapped with an English and French welcome and goodbye
      self.assertEqual(mock_stdout.getvalue()					,   'Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n')







   @require_torch
   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      self.assertEqual(find_labels(__a)					,   ['labels'])
      self.assertEqual(find_labels(__a)					,   ['labels', 'next_sentence_label'])
      self.assertEqual(find_labels(__a)					,   ['start_positions', 'end_positions'])




      class a_    (    UpperCamelCase_							):
         pass


      self.assertEqual(find_labels(__a)					,   ['labels'])







   @require_tf
   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      self.assertEqual(find_labels(__a)					,   ['labels'])
      self.assertEqual(find_labels(__a)					,   ['labels', 'next_sentence_label'])
      self.assertEqual(find_labels(__a)					,   ['start_positions', 'end_positions'])




      class a_    (    UpperCamelCase_							):
         pass


      self.assertEqual(find_labels(__a)					,   ['labels'])







   @require_flax
   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      self.assertEqual(find_labels(__a)					,   [])
      self.assertEqual(find_labels(__a)					,   [])
      self.assertEqual(find_labels(__a)					,   [])




      class a_    (    UpperCamelCase_							):
         pass


      self.assertEqual(find_labels(__a)					,   [])