'''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				:					Any    						=			logging.get_logger('''transformers.models.encodec''')

A				:					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''',
}
A				:					Tuple    						=			{
    '''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				:					List[str]    						=			{
    '''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				:					Union[str, Any]    						=			{
    '''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				:					int    						=			{
    '''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				:					Tuple    						=			{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
A				:					Union[str, Any]    						=			{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
A				:					List[Any]    						=			[]
A				:					List[str]    						=			[]






def 				lowerCAmelCase__	(    lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      int  ,lowerCamelCase					:      int  ,lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      List[Any]       ):
       for attribute in key.split('.'       ):
              _A					:     Tuple     = getattr(lowerCamelCase__  ,lowerCamelCase__       )

       if weight_type is not None:
              _A					:     Tuple     = getattr(lowerCamelCase__  ,lowerCamelCase__       ).shape
       else:
              _A					:     Union[str, Any]     = 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					:     Union[str, Any]     = value
       elif weight_type == "weight_g":
              _A					:     str     = value
       elif weight_type == "weight_v":
              _A					:     Tuple     = value
       elif weight_type == "bias":
              _A					:     Any     = value
       elif weight_type == "running_mean":
              _A					:     str     = value
       elif weight_type == "running_var":
              _A					:     Union[str, Any]     = value
       elif weight_type == "num_batches_tracked":
              _A					:     Tuple     = value
       elif weight_type == "weight_ih_l0":
              _A					:     Any     = value
       elif weight_type == "weight_hh_l0":
              _A					:     Any     = value
       elif weight_type == "bias_ih_l0":
              _A					:     Any     = value
       elif weight_type == "bias_hh_l0":
              _A					:     Any     = value
       elif weight_type == "weight_ih_l1":
              _A					:     Any     = 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					:     str     = value

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






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      Tuple       ):
       for key in ignore_keys:
              if key.endswith('.*'       ):
                     if name.startswith(key[:-1]       ):
                            return True
              elif ".*." in key:
                     _A					:     Optional[Any]     = key.split('.*.'       )
                     if prefix in name and suffix in name:
                            return True
              elif key in name:
                     return True
       return False






def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      List[str]       ):
       _A					:     Optional[Any]     = []

       if model_name == "encodec_24khz" or "encodec_32khz":
              _A					:     str     = MAPPING_24K
       elif model_name == "encodec_48khz":
              _A					:     List[Any]     = MAPPING_48K
       else:
              raise ValueError(F'Unsupported model: {model_name}'       )

       for name, value in orig_dict.items():
              if should_ignore(lowerCamelCase__  ,lowerCamelCase__       ):
                     logger.info(F'{name} was ignored'       )
                     continue

              _A					:     Any     = False
              for key, mapped_key in MAPPING.items():
                     if "*" in key:
                            _A					:     int     = key.split('.*.'       )
                            if prefix in name and suffix in name:
                                   _A					:     Tuple     = 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					:     List[str]     = True
                            if "*" in mapped_key:
                                   _A					:     Union[str, Any]     = name.split(lowerCamelCase__       )[0].split('.'       )[-2]
                                   _A					:     List[str]     = mapped_key.replace('*'  ,lowerCamelCase__       )
                            if "weight_g" in name:
                                   _A					:     int     = '''weight_g'''
                            elif "weight_v" in name:
                                   _A					:     List[Any]     = '''weight_v'''
                            elif "weight_ih_l0" in name:
                                   _A					:     Dict     = '''weight_ih_l0'''
                            elif "weight_hh_l0" in name:
                                   _A					:     Union[str, Any]     = '''weight_hh_l0'''
                            elif "bias_ih_l0" in name:
                                   _A					:     List[str]     = '''bias_ih_l0'''
                            elif "bias_hh_l0" in name:
                                   _A					:     Any     = '''bias_hh_l0'''
                            elif "weight_ih_l1" in name:
                                   _A					:     Any     = '''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					:     Any     = '''bias_hh_l1'''
                            elif "bias" in name:
                                   _A					:     Optional[int]     = '''bias'''
                            elif "weight" in name:
                                   _A					:     List[str]     = '''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					:     Any     = '''num_batches_tracked'''
                            else:
                                   _A					:     str     = None
                            set_recursively(lowerCamelCase__  ,lowerCamelCase__  ,lowerCamelCase__  ,lowerCamelCase__  ,lowerCamelCase__       )
                     continue
              if not is_used:
                     unused_weights.append(lowerCamelCase__       )

       logger.warning(F'Unused weights: {unused_weights}'       )






@torch.no_grad()
def 				lowerCAmelCase__	(    lowerCamelCase					:      Any  ,lowerCamelCase					:      str  ,lowerCamelCase					:      Dict  ,lowerCamelCase					:      Dict=None  ,lowerCamelCase					:      Optional[int]=None  ,):
       if config_path is not None:
              _A					:     List[str]     = EncodecConfig.from_pretrained(lowerCamelCase__       )
       else:
              _A					:     Dict     = EncodecConfig()

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

       _A					:     Optional[Any]     = EncodecModel(lowerCamelCase__       )

       _A					:     Union[str, 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(lowerCamelCase__       )

       _A					:     Optional[Any]     = torch.load(lowerCamelCase__       )
       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					:     Tuple     = original_checkpoint['''best_state''']
       recursively_load_weights(lowerCamelCase__  ,lowerCamelCase__  ,lowerCamelCase__       )
       model.save_pretrained(lowerCamelCase__       )

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


if __name__ == "__main__":
   A				:					Union[str, 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.'''
   )

   A				:					str    						=			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 string

import numpy






def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      int       ):
       return b if a == 0 else greatest_common_divisor(b % a  ,lowerCamelCase       )


class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   string.ascii_uppercase + string.digits
    # This cipher takes alphanumerics into account
    # i.e. a total of 36 characters

    # take x and return x % len(key_string)
    a													=   numpy.vectorize(lambda a_	: x % 36	)

    a													=   numpy.vectorize(a_	)


    def __init__(  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							numpy.ndarray):
           _A					:     Union[str, Any]     = self.modulus(SCREAMING_SNAKE_CASE)  # mod36 calc's on the encrypt key
           self.check_determinant()  # validate the determinant of the encryption key
           _A					:     Optional[int]     = encrypt_key.shape[0]


    def        A   (  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							str):
           return self.key_string.index(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							int):
           return self.key_string[round(SCREAMING_SNAKE_CASE)]


    def        A   (  self      :							List[Any]):
           _A					:     Optional[Any]     = round(numpy.linalg.det(self.encrypt_key))

           if det < 0:
                  _A					:     int     = det % len(self.key_string)

           _A					:     str     = len(self.key_string)
           if greatest_common_divisor(SCREAMING_SNAKE_CASE				,							len(self.key_string)) != 1:
                  _A					:     Optional[int]     = (
                      F'determinant modular {req_l} of encryption key({det}) '
                      F'is not co prime w.r.t {req_l}.\nTry another key.'
                  )
                  raise ValueError(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int				,							SCREAMING_SNAKE_CASE      :							str):
           _A					:     List[Any]     = [char for char in text.upper() if char in self.key_string]

           _A					:     List[str]     = chars[-1]
           while len(SCREAMING_SNAKE_CASE) % self.break_key != 0:
                  chars.append(SCREAMING_SNAKE_CASE)

           return "".join(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							str):
           _A					:     Optional[int]     = self.process_text(text.upper())
           _A					:     List[str]     = ''

           for i in range(0				,							len(SCREAMING_SNAKE_CASE) - self.break_key + 1				,							self.break_key):
                  _A					:     Optional[Any]     = text[i : i + self.break_key]
                  _A					:     Optional[int]     = [self.replace_letters(SCREAMING_SNAKE_CASE) for char in batch]
                  _A					:     Tuple     = numpy.array([vec]).T
                  _A					:     List[str]     = self.modulus(self.encrypt_key.dot(SCREAMING_SNAKE_CASE)).T.tolist()[
                      0
                  ]
                  _A					:     str     = ''.join(
                      self.replace_digits(SCREAMING_SNAKE_CASE) for num in batch_encrypted)
                  encrypted += encrypted_batch

           return encrypted


    def        A   (  self      :							Union[str, Any]):
           _A					:     List[Any]     = round(numpy.linalg.det(self.encrypt_key))

           if det < 0:
                  _A					:     Optional[int]     = det % len(self.key_string)
           _A					:     List[str]     = None
           for i in range(len(self.key_string)):
                  if (det * i) % len(self.key_string) == 1:
                         _A					:     Union[str, Any]     = i
                         break

           _A					:     Dict     = (
               det_inv
               * numpy.linalg.det(self.encrypt_key)
               * numpy.linalg.inv(self.encrypt_key)
           )

           return self.to_int(self.modulus(SCREAMING_SNAKE_CASE))


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							str):
           _A					:     List[str]     = self.make_decrypt_key()
           _A					:     Dict     = self.process_text(text.upper())
           _A					:     str     = ''

           for i in range(0				,							len(SCREAMING_SNAKE_CASE) - self.break_key + 1				,							self.break_key):
                  _A					:     Optional[int]     = text[i : i + self.break_key]
                  _A					:     Union[str, Any]     = [self.replace_letters(SCREAMING_SNAKE_CASE) for char in batch]
                  _A					:     Tuple     = numpy.array([vec]).T
                  _A					:     Optional[int]     = self.modulus(decrypt_key.dot(SCREAMING_SNAKE_CASE)).T.tolist()[0]
                  _A					:     Tuple     = ''.join(
                      self.replace_digits(SCREAMING_SNAKE_CASE) for num in batch_decrypted)
                  decrypted += decrypted_batch

           return decrypted






def 				lowerCAmelCase__	(    ):
       _A					:     List[Any]     = int(input('Enter the order of the encryption key: '       )       )
       _A					:     List[str]     = []

       print('Enter each row of the encryption key with space separated integers'       )
       for _ in range(lowerCamelCase       ):
              _A					:     str     = [int(lowerCamelCase       ) for x in input().split()]
              hill_matrix.append(lowerCamelCase       )

       _A					:     Dict     = HillCipher(numpy.array(lowerCamelCase       )       )

       print('Would you like to encrypt or decrypt some text? (1 or 2)'       )
       _A					:     List[str]     = input('\n1. Encrypt\n2. Decrypt\n'       )
       if option == "1":
              _A					:     int     = input('What text would you like to encrypt?: '       )
              print('Your encrypted text is:'       )
              print(hc.encrypt(lowerCamelCase       )       )
       elif option == "2":
              _A					:     int     = input('What text would you like to decrypt?: '       )
              print('Your decrypted text is:'       )
              print(hc.decrypt(lowerCamelCase       )       )


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   main()






