

"""simple docstring"""






import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase						=		logging.get_logger(__name__)

set_seed(7_7_0)


_lowerCAmelCase						=		{
    """c_attn""": """att_proj""",
    """c_proj""": """out_proj""",
    """c_fc""": """in_proj""",
    """transformer.""": """""",
    """h.""": """layers.""",
    """ln_1""": """layernorm_1""",
    """ln_2""": """layernorm_2""",
    """ln_f""": """layernorm_final""",
    """wpe""": """position_embeds_layer""",
    """wte""": """input_embeds_layer""",
}


_lowerCAmelCase						=		{
    """text_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """text.pt""",
    },
    """coarse_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """coarse.pt""",
    },
    """fine_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """fine.pt""",
    },
    """text""": {
        """repo_id""": """suno/bark""",
        """file_name""": """text_2.pt""",
    },
    """coarse""": {
        """repo_id""": """suno/bark""",
        """file_name""": """coarse_2.pt""",
    },
    """fine""": {
        """repo_id""": """suno/bark""",
        """file_name""": """fine_2.pt""",
    },
}

_lowerCAmelCase						=		os.path.dirname(os.path.abspath(__file__))
_lowerCAmelCase						=		os.path.join(os.path.expanduser("""~"""), """.cache""")
_lowerCAmelCase						=		os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase=False   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Any  =    model_type
    if use_small:
        key += "_small"
    return os.path.join(lowercase__ ,   REMOTE_MODEL_PATHS[key]['file_name']   )

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    os.makedirs(lowercase__ ,   exist_ok=lowercase__   )
    hf_hub_download(repo_id=lowercase__ ,   filename=lowercase__ ,   local_dir=lowercase__   )

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase=False ,   _lowerCamelCase="text"   ):

    '''simple docstring'''



    if model_type == "text":
        _lowerCAmelCase       :   Dict  =    BarkSemanticModel
        _lowerCAmelCase       :   str  =    BarkSemanticConfig
        _lowerCAmelCase       :   List[str]  =    BarkSemanticGenerationConfig
    elif model_type == "coarse":
        _lowerCAmelCase       :   Union[str, Any]  =    BarkCoarseModel
        _lowerCAmelCase       :   Dict  =    BarkCoarseConfig
        _lowerCAmelCase       :   Optional[int]  =    BarkCoarseGenerationConfig
    elif model_type == "fine":
        _lowerCAmelCase       :   str  =    BarkFineModel
        _lowerCAmelCase       :   str  =    BarkFineConfig
        _lowerCAmelCase       :   Any  =    BarkFineGenerationConfig
    else:
        raise NotImplementedError()
    _lowerCAmelCase       :   int  =    f"""{model_type}_small""" if use_small else model_type
    _lowerCAmelCase       :   str  =    REMOTE_MODEL_PATHS[model_key]
    if not os.path.exists(lowercase__   ):
        logger.info(f"""{model_type} model not found, downloading into `{CACHE_DIR}`."""   )
        _download(model_info['repo_id'] ,   model_info['file_name']   )
    _lowerCAmelCase       :   List[str]  =    torch.load(lowercase__ ,   map_location=lowercase__   )
    # this is a hack
    _lowerCAmelCase       :   Optional[Any]  =    checkpoint['model_args']
    if "input_vocab_size" not in model_args:
        _lowerCAmelCase       :   List[Any]  =    model_args['vocab_size']
        _lowerCAmelCase       :   Optional[Any]  =    model_args['vocab_size']
        del model_args["vocab_size"]

    # convert Bark model arguments to HF Bark model arguments
    _lowerCAmelCase       :   Any  =    model_args.pop('n_head'   )
    _lowerCAmelCase       :   Optional[int]  =    model_args.pop('n_embd'   )
    _lowerCAmelCase       :   List[str]  =    model_args.pop('n_layer'   )

    _lowerCAmelCase       :   Tuple  =    ConfigClass(**checkpoint['model_args']   )
    _lowerCAmelCase       :   List[Any]  =    ModelClass(config=lowercase__   )
    _lowerCAmelCase       :   str  =    GenerationConfigClass()

    _lowerCAmelCase       :   Tuple  =    model_generation_config
    _lowerCAmelCase       :   Dict  =    checkpoint['model']
    # fixup checkpoint
    _lowerCAmelCase       :   str  =    '_orig_mod.'
    for k, v in list(state_dict.items()   ):
        if k.startswith(lowercase__   ):
            # replace part of the key with corresponding layer name in HF implementation
            _lowerCAmelCase       :   Any  =    k[len(lowercase__   ) :]
            for old_layer_name in new_layer_name_dict:
                _lowerCAmelCase       :   Any  =    new_k.replace(lowercase__ ,   new_layer_name_dict[old_layer_name]   )

            _lowerCAmelCase       :   Any  =    state_dict.pop(lowercase__   )

    _lowerCAmelCase       :   Dict  =    set(state_dict.keys()   ) - set(model.state_dict().keys()   )
    _lowerCAmelCase       :   Any  =    {k for k in extra_keys if not k.endswith('.attn.bias'   )}
    _lowerCAmelCase       :   List[str]  =    set(model.state_dict().keys()   ) - set(state_dict.keys()   )
    _lowerCAmelCase       :   Dict  =    {k for k in missing_keys if not k.endswith('.attn.bias'   )}
    if len(lowercase__   ) != 0:
        raise ValueError(f"""extra keys found: {extra_keys}"""   )
    if len(lowercase__   ) != 0:
        raise ValueError(f"""missing keys: {missing_keys}"""   )
    model.load_state_dict(lowercase__ ,   strict=lowercase__   )
    _lowerCAmelCase       :   str  =    model.num_parameters(exclude_embeddings=lowercase__   )
    _lowerCAmelCase       :   int  =    checkpoint['best_val_loss'].item()
    logger.info(f"""model loaded: {round(n_params/1e6 ,   1   )}M params, {round(lowercase__ ,   3   )} loss"""   )
    model.eval()
    model.to(lowercase__   )
    del checkpoint, state_dict

    return model

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase=False ,   _lowerCamelCase="text"   ):

    '''simple docstring'''



    if model_type not in ("text", "coarse", "fine"):
        raise NotImplementedError()

    _lowerCAmelCase       :   Dict  =    'cpu'  # do conversion on cpu

    _lowerCAmelCase       :   Optional[Any]  =    _get_ckpt_path(lowercase__ ,   use_small=lowercase__   )
    _lowerCAmelCase       :   List[Any]  =    _load_model(lowercase__ ,   lowercase__ ,   model_type=lowercase__ ,   use_small=lowercase__   )

    # load bark initial model
    _lowerCAmelCase       :   List[str]  =    _bark_load_model(lowercase__ ,   'cpu' ,   model_type=lowercase__ ,   use_small=lowercase__   )

    if model_type == "text":
        _lowerCAmelCase       :   Tuple  =    bark_model['model']

    if model.num_parameters(exclude_embeddings=lowercase__   ) != bark_model.get_num_params():
        raise ValueError('initial and new models don\'t have the same number of parameters'   )

    # check if same output as the bark model
    _lowerCAmelCase       :   Tuple  =    5
    _lowerCAmelCase       :   List[Any]  =    10

    if model_type in ["text", "coarse"]:
        _lowerCAmelCase       :   List[str]  =    torch.randint(256 ,   (batch_size, sequence_length) ,   dtype=torch.int   )
        _lowerCAmelCase       :   Tuple  =    bark_model(lowercase__   )[0]

        _lowerCAmelCase       :   Dict  =    model(lowercase__   )

        # take last logits
        _lowerCAmelCase       :   List[Any]  =    output_new_model_total.logits[:, [-1], :]

    else:
        _lowerCAmelCase       :   int  =    3
        _lowerCAmelCase       :   List[str]  =    8
        _lowerCAmelCase       :   Dict  =    torch.randint(256 ,   (batch_size, sequence_length, n_codes_total) ,   dtype=torch.int   )

        _lowerCAmelCase       :   List[Any]  =    model(lowercase__ ,   lowercase__   )
        _lowerCAmelCase       :   Tuple  =    bark_model(lowercase__ ,   lowercase__   )

        _lowerCAmelCase       :   Any  =    output_new_model_total.logits

    # output difference should come from the difference of self-attention implementation design
    if output_new_model.shape != output_old_model.shape:
        raise ValueError('initial and new outputs don\'t have the same shape'   )
    if (output_new_model - output_old_model).abs().max().item() > 1e-3:
        raise ValueError('initial and new outputs are not equal'   )

    Path(lowercase__   ).mkdir(exist_ok=lowercase__   )
    model.save_pretrained(lowercase__   )

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Tuple  =    os.path.join(lowercase__ ,   lowercase__   )

    _lowerCAmelCase       :   Tuple  =    BarkSemanticConfig.from_pretrained(os.path.join(lowercase__ ,   'config.json'   )   )
    _lowerCAmelCase       :   Dict  =    BarkCoarseConfig.from_pretrained(os.path.join(lowercase__ ,   'config.json'   )   )
    _lowerCAmelCase       :   Union[str, Any]  =    BarkFineConfig.from_pretrained(os.path.join(lowercase__ ,   'config.json'   )   )
    _lowerCAmelCase       :   List[str]  =    EncodecConfig.from_pretrained('facebook/encodec_24khz'   )

    _lowerCAmelCase       :   Tuple  =    BarkSemanticModel.from_pretrained(lowercase__   )
    _lowerCAmelCase       :   Union[str, Any]  =    BarkCoarseModel.from_pretrained(lowercase__   )
    _lowerCAmelCase       :   Any  =    BarkFineModel.from_pretrained(lowercase__   )
    _lowerCAmelCase       :   Dict  =    EncodecModel.from_pretrained('facebook/encodec_24khz'   )

    _lowerCAmelCase       :   Optional[Any]  =    BarkConfig.from_sub_model_configs(
        lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__   )

    _lowerCAmelCase       :   List[Any]  =    BarkGenerationConfig.from_sub_model_configs(
        semantic.generation_config ,   coarseAcoustic.generation_config ,   fineAcoustic.generation_config   )

    _lowerCAmelCase       :   Optional[Any]  =    BarkModel(lowercase__   )

    _lowerCAmelCase       :   Optional[Any]  =    semantic
    _lowerCAmelCase       :   List[Any]  =    coarseAcoustic
    _lowerCAmelCase       :   Optional[Any]  =    fineAcoustic
    _lowerCAmelCase       :   Union[str, Any]  =    codec

    _lowerCAmelCase       :   Dict  =    bark_generation_config

    Path(lowercase__   ).mkdir(exist_ok=lowercase__   )
    bark.save_pretrained(lowercase__ ,   repo_id=lowercase__ ,   push_to_hub=lowercase__   )


if __name__ == "__main__":
       _lowerCAmelCase						=		argparse.ArgumentParser()
       # Required parameters

       parser.add_argument("""model_type""", type=str, help="""text, coarse or fine.""")
       parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
       parser.add_argument("""--is_small""", action="""store_true""", help="""convert the small version instead of the large.""")

       _lowerCAmelCase						=		parser.parse_args()

       load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)








"""simple docstring"""






import argparse
import struct
import unittest







class 	__UpperCamelCase    :

  def __init__(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    data

      # Initialize hash values
      _lowerCAmelCase       :   Any  =    [
          0x6A09_E667,
          0xBB67_AE85,
          0x3C6E_F372,
          0xA54F_F53A,
          0x510E_527F,
          0x9B05_688C,
          0x1F83_D9AB,
          0x5BE0_CD19,
      ]

      # Initialize round constants
      _lowerCAmelCase       :   str  =    [
          0x428A_2F98,
          0x7137_4491,
          0xB5C0_FBCF,
          0xE9B5_DBA5,
          0x3956_C25B,
          0x59F1_11F1,
          0x923F_82A4,
          0xAB1C_5ED5,
          0xD807_AA98,
          0x1283_5B01,
          0x2431_85BE,
          0x550C_7DC3,
          0x72BE_5D74,
          0x80DE_B1FE,
          0x9BDC_06A7,
          0xC19B_F174,
          0xE49B_69C1,
          0xEFBE_4786,
          0x0FC1_9DC6,
          0x240C_A1CC,
          0x2DE9_2C6F,
          0x4A74_84AA,
          0x5CB0_A9DC,
          0x76F9_88DA,
          0x983E_5152,
          0xA831_C66D,
          0xB003_27C8,
          0xBF59_7FC7,
          0xC6E0_0BF3,
          0xD5A7_9147,
          0x06CA_6351,
          0x1429_2967,
          0x27B7_0A85,
          0x2E1B_2138,
          0x4D2C_6DFC,
          0x5338_0D13,
          0x650A_7354,
          0x766A_0ABB,
          0x81C2_C92E,
          0x9272_2C85,
          0xA2BF_E8A1,
          0xA81A_664B,
          0xC24B_8B70,
          0xC76C_51A3,
          0xD192_E819,
          0xD699_0624,
          0xF40E_3585,
          0x106A_A070,
          0x19A4_C116,
          0x1E37_6C08,
          0x2748_774C,
          0x34B0_BCB5,
          0x391C_0CB3,
          0x4ED8_AA4A,
          0x5B9C_CA4F,
          0x682E_6FF3,
          0x748F_82EE,
          0x78A5_636F,
          0x84C8_7814,
          0x8CC7_0208,
          0x90BE_FFFA,
          0xA450_6CEB,
          0xBEF9_A3F7,
          0xC671_78F2,
      ]

      _lowerCAmelCase       :   Any  =    self.preprocessing(self.data      )
      self.final_hash()

  @staticmethod
  def __lowerCamelCase		(		_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    b'\x80' + (b'\x00' * (63 - (len(_A      ) + 8) % 64))
      _lowerCAmelCase       :   Any  =    struct.pack('>Q'    ,(len(_A      ) * 8)      )
      return data + padding + big_endian_integer

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    [
          self.preprocessed_data[x : x + 64]
          for x in range(0    ,len(self.preprocessed_data      )    ,64      )
      ]

      for block in self.blocks:
          # Convert the given block into a list of 4 byte integers
          _lowerCAmelCase       :   int  =    list(struct.unpack('>16L'    ,_A      )      )
          # add 48 0-ed integers
          words += [0] * 48

          _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   Tuple  =    self.hashes

          for index in range(0    ,64      ):
              if index > 15:
                  # modify the zero-ed indexes at the end of the array
                  _lowerCAmelCase       :   List[str]  =    (
                      self.ror(words[index - 15]    ,7      )
                      ^ self.ror(words[index - 15]    ,18      )
                      ^ (words[index - 15] >> 3)
                  )
                  _lowerCAmelCase       :   Tuple  =    (
                      self.ror(words[index - 2]    ,17      )
                      ^ self.ror(words[index - 2]    ,19      )
                      ^ (words[index - 2] >> 10)
                  )

                  _lowerCAmelCase       :   str  =    (
                      words[index - 16] + sa + words[index - 7] + sa
                  ) % 0x1_0000_0000

              # Compression
              _lowerCAmelCase       :   Optional[Any]  =    self.ror(_A    ,6      ) ^ self.ror(_A    ,11      ) ^ self.ror(_A    ,25      )
              _lowerCAmelCase       :   int  =    (e & f) ^ ((~e & 0xFFFF_FFFF) & g)
              _lowerCAmelCase       :   int  =    (
                  h + sa + ch + self.round_constants[index] + words[index]
              ) % 0x1_0000_0000
              _lowerCAmelCase       :   Union[str, Any]  =    self.ror(_A    ,2      ) ^ self.ror(_A    ,13      ) ^ self.ror(_A    ,22      )
              _lowerCAmelCase       :   Any  =    (a & b) ^ (a & c) ^ (b & c)
              _lowerCAmelCase       :   Any  =    (sa + maj) % 0x1_0000_0000

              _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   Tuple  =    (
                  g,
                  f,
                  e,
                  ((d + tempa) % 0x1_0000_0000),
                  c,
                  b,
                  a,
                  ((tempa + tempa) % 0x1_0000_0000),
              )

          _lowerCAmelCase       :   Any  =    [a, b, c, d, e, f, g, h]

          # Modify final values
          _lowerCAmelCase       :   int  =    [
              ((element + mutated_hash_values[index]) % 0x1_0000_0000)
              for index, element in enumerate(self.hashes      )
          ]

      _lowerCAmelCase       :   List[str]  =    ''.join([hex(_A      )[2:].zfill(8      ) for value in self.hashes]      )







  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      return 0xFFFF_FFFF & (value << (32 - rotations)) | (value >> rotations)







class 	__UpperCamelCase    (    unittest.TestCase						):

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      import hashlib

      _lowerCAmelCase       :   Any  =    bytes('Test String'    ,'utf-8'      )
      self.assertEqual(SHAaaa(_A      ).hash    ,hashlib.shaaaa(_A      ).hexdigest()      )





def 		lowerCamelCase__		(					):

    '''simple docstring'''



    import doctest

    doctest.testmod()

    _lowerCAmelCase       :   Optional[Any]  =    argparse.ArgumentParser()
    parser.add_argument(
        '-s' ,   '--string' ,   dest='input_string' ,   default='Hello World!! Welcome to Cryptography' ,   help='Hash the string' ,   )
    parser.add_argument(
        '-f' ,   '--file' ,   dest='input_file' ,   help='Hash contents of a file'   )

    _lowerCAmelCase       :   Tuple  =    parser.parse_args()

    _lowerCAmelCase       :   List[str]  =    args.input_string

    # hash input should be a bytestring
    if args.input_file:
        with open(args.input_file ,   'rb'   ) as f:
            _lowerCAmelCase       :   int  =    f.read()
    else:
        _lowerCAmelCase       :   int  =    bytes(_lowerCamelCase ,   'utf-8'   )

    print(SHAaaa(_lowerCamelCase   ).hash   )


if __name__ == "__main__":
 main()





