


"""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()
snake_case__		:     str    				=				logging.get_logger('''transformers.models.encodec''')

snake_case__		:     Dict    				=				{
    '''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''',
}
snake_case__		:     List[str]    				=				{
    '''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''',
}
snake_case__		:     Optional[int]    				=				{
    '''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''',
}
snake_case__		:     Optional[int]    				=				{
    '''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''',
}
snake_case__		:     List[str]    				=				{
    '''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''',
}
snake_case__		:     str    				=				{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
snake_case__		:     str    				=				{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
snake_case__		:     Any    				=				[]
snake_case__		:     int    				=				[]


def      _snake_case						(						_snake_case				:			str ,   _snake_case				:			str ,   _snake_case				:			List[Any] ,   _snake_case				:			str ,   _snake_case				:			Optional[Any]			):
      for attribute in key.split('''.'''			):
            lowerCAmelCase					:	List[Any]       =   getattr(_snake_case ,   _snake_case			)

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

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


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


def      _snake_case						(						_snake_case				:			int ,   _snake_case				:			Any ,   _snake_case				:			Tuple			):
      lowerCAmelCase					:	Any       =   []

      if model_name == "encodec_24khz" or "encodec_32khz":
            lowerCAmelCase					:	Optional[int]       =   MAPPING_24K
      elif model_name == "encodec_48khz":
            lowerCAmelCase					:	Optional[Any]       =   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

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

                  if key in name:
                        # HACK otherwise .embed gets initialized with .embed_avg too
                        if key.endswith('''embed'''			) and name.endswith('''embed_avg'''			):
                              continue

                        lowerCAmelCase					:	List[Any]       =   True
                        if "*" in mapped_key:
                              lowerCAmelCase					:	Union[str, Any]       =   name.split(_snake_case			)[0].split('''.'''			)[-2]
                              lowerCAmelCase					:	Optional[int]       =   mapped_key.replace('''*''' ,   _snake_case			)
                        if "weight_g" in name:
                              lowerCAmelCase					:	List[str]       =   '''weight_g'''
                        elif "weight_v" in name:
                              lowerCAmelCase					:	Optional[int]       =   '''weight_v'''
                        elif "weight_ih_l0" in name:
                              lowerCAmelCase					:	List[Any]       =   '''weight_ih_l0'''
                        elif "weight_hh_l0" in name:
                              lowerCAmelCase					:	Union[str, Any]       =   '''weight_hh_l0'''
                        elif "bias_ih_l0" in name:
                              lowerCAmelCase					:	Optional[Any]       =   '''bias_ih_l0'''
                        elif "bias_hh_l0" in name:
                              lowerCAmelCase					:	int       =   '''bias_hh_l0'''
                        elif "weight_ih_l1" in name:
                              lowerCAmelCase					:	Tuple       =   '''weight_ih_l1'''
                        elif "weight_hh_l1" in name:
                              lowerCAmelCase					:	Any       =   '''weight_hh_l1'''
                        elif "bias_ih_l1" in name:
                              lowerCAmelCase					:	List[str]       =   '''bias_ih_l1'''
                        elif "bias_hh_l1" in name:
                              lowerCAmelCase					:	Union[str, Any]       =   '''bias_hh_l1'''
                        elif "bias" in name:
                              lowerCAmelCase					:	Optional[Any]       =   '''bias'''
                        elif "weight" in name:
                              lowerCAmelCase					:	List[str]       =   '''weight'''
                        elif "running_mean" in name:
                              lowerCAmelCase					:	str       =   '''running_mean'''
                        elif "running_var" in name:
                              lowerCAmelCase					:	Union[str, Any]       =   '''running_var'''
                        elif "num_batches_tracked" in name:
                              lowerCAmelCase					:	Optional[Any]       =   '''num_batches_tracked'''
                        else:
                              lowerCAmelCase					:	Tuple       =   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      _snake_case						(						_snake_case				:			Any ,   _snake_case				:			int ,   _snake_case				:			List[Any] ,   _snake_case				:			Optional[Any]=None ,   _snake_case				:			Optional[Any]=None ,   ):
      if config_path is not None:
            lowerCAmelCase					:	Optional[int]       =   EncodecConfig.from_pretrained(_snake_case			)
      else:
            lowerCAmelCase					:	int       =   EncodecConfig()

      if model_name == "encodec_24khz":
            pass  # config is already correct
      elif model_name == "encodec_32khz":
            lowerCAmelCase					:	str       =   [8, 5, 4, 4]
            lowerCAmelCase					:	Optional[int]       =   [2.2]
            lowerCAmelCase					:	int       =   64
            lowerCAmelCase					:	List[str]       =   32000
            lowerCAmelCase					:	str       =   2048
            lowerCAmelCase					:	str       =   False
            lowerCAmelCase					:	List[str]       =   False
            lowerCAmelCase					:	str       =   False
      elif model_name == "encodec_48khz":
            lowerCAmelCase					:	Dict       =   [8, 5, 4, 2]
            lowerCAmelCase					:	Any       =   [3.0, 6.0, 12.0, 24.0]
            lowerCAmelCase					:	Tuple       =   48000
            lowerCAmelCase					:	Optional[Any]       =   2
            lowerCAmelCase					:	int       =   False
            lowerCAmelCase					:	Union[str, Any]       =   '''time_group_norm'''
            lowerCAmelCase					:	List[Any]       =   True
            lowerCAmelCase					:	str       =   1.0
            lowerCAmelCase					:	Optional[int]       =   0.01
      else:
            raise ValueError(f'''Unknown model name: {model_name}'''			)

      lowerCAmelCase					:	Dict       =   EncodecModel(_snake_case			)

      lowerCAmelCase					:	Any       =   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			)

      lowerCAmelCase					:	List[Any]       =   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
            lowerCAmelCase					:	Dict       =   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__":
 snake_case__		:     Optional[Any]    				=				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.'''
 )

 snake_case__		:     Union[str, Any]    				=				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 __future__ import annotations



class 		snake_case_:






     def __init__(	self  :					int					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					str				):
           lowerCAmelCase,      lowerCAmelCase					:	List[str]       =   text, pattern
           lowerCAmelCase,      lowerCAmelCase					:	Union[str, Any]       =   len(UpperCamelCase_				), len(UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					List[Any]					,				UpperCamelCase_  :					str				):

           for i in range(self.patLen - 1					,				-1					,				-1				):
                 if char == self.pattern[i]:
                       return i
           return -1






     def      lowerCamelCase__     (	self  :					Optional[Any]					,				UpperCamelCase_  :					int				):

           for i in range(self.patLen - 1					,				-1					,				-1				):
                 if self.pattern[i] != self.text[current_pos + i]:
                       return current_pos + i
           return -1






     def      lowerCamelCase__     (	self  :					Dict				):
           # searches pattern in text and returns index positions
           lowerCAmelCase					:	Union[str, Any]       =   []
           for i in range(self.textLen - self.patLen + 1				):
                 lowerCAmelCase					:	str       =   self.mismatch_in_text(UpperCamelCase_				)
                 if mismatch_index == -1:
                       positions.append(UpperCamelCase_				)
                 else:
                       lowerCAmelCase					:	Optional[Any]       =   self.match_in_pattern(self.text[mismatch_index]				)
                       lowerCAmelCase					:	int       =   (
                           mismatch_index - match_index
                       )  # shifting index lgtm [py/multiple-definition]
           return positions



snake_case__		:     str    				=				'''ABAABA'''
snake_case__		:     List[str]    				=				'''AB'''
snake_case__		:     Union[str, Any]    				=				BoyerMooreSearch(text, pattern)
snake_case__		:     Optional[Any]    				=				bms.bad_character_heuristic()

if len(positions) == 0:
 print('''No match found''')
else:
 print('''Pattern found in following positions: ''')
 print(positions)


