
"""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()
_A			:       Union[str, Any]   		=       logging.get_logger(__name__)

set_seed(7_70)


_A			:       Dict   		=       {
    """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""",
}


_A			:       Tuple   		=       {
    """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""",
    },
}

_A			:       Optional[Any]   		=       os.path.dirname(os.path.abspath(__file__))
_A			:       int   		=       os.path.join(os.path.expanduser("""~"""), """.cache""")
_A			:       Any   		=       os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")


def   __magic_name__	(      __snake_case				:       Optional[Any]     , __snake_case				:       Dict=False  ) ->				Optional[int]:
       lowercase    :				str       		=							model_type
       if use_small:
              key += "_small"
       return os.path.join(__snake_case     , REMOTE_MODEL_PATHS[key]["file_name"]  )


def   __magic_name__	(      __snake_case				:       Optional[Any]     , __snake_case				:       Tuple  ) ->				Optional[Any]:
       os.makedirs(__snake_case     , exist_ok=__snake_case  )
       hf_hub_download(repo_id=__snake_case     , filename=__snake_case     , local_dir=__snake_case  )


def   __magic_name__	(      __snake_case				:       str     , __snake_case				:       Union[str, Any]     , __snake_case				:       List[Any]=False     , __snake_case				:       Tuple="text"  ) ->				Optional[Any]:







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

       # convert Bark model arguments to HF Bark model arguments
       lowercase    :				Tuple       		=							model_args.pop("n_head"  )
       lowercase    :				Optional[Any]       		=							model_args.pop("n_embd"  )
       lowercase    :				List[Any]       		=							model_args.pop("n_layer"  )

       lowercase    :				Union[str, Any]       		=							ConfigClass(**checkpoint["model_args"]  )
       lowercase    :				str       		=							ModelClass(config=__snake_case  )
       lowercase    :				str       		=							GenerationConfigClass()

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

                     lowercase    :				List[str]       		=							state_dict.pop(__snake_case  )

       lowercase    :				Tuple       		=							set(state_dict.keys()  ) - set(model.state_dict().keys()  )
       lowercase    :				int       		=							{k for k in extra_keys if not k.endswith(".attn.bias"  )}
       lowercase    :				int       		=							set(model.state_dict().keys()  ) - set(state_dict.keys()  )
       lowercase    :				int       		=							{k for k in missing_keys if not k.endswith(".attn.bias"  )}
       if len(__snake_case  ) != 0:
              raise ValueError(f"""extra keys found: {extra_keys}"""  )
       if len(__snake_case  ) != 0:
              raise ValueError(f"""missing keys: {missing_keys}"""  )
       model.load_state_dict(__snake_case     , strict=__snake_case  )
       lowercase    :				List[Any]       		=							model.num_parameters(exclude_embeddings=__snake_case  )
       lowercase    :				Tuple       		=							checkpoint['''best_val_loss'''].item()
       logger.info(f"""model loaded: {round(n_params/1E6     , 1  )}M params, {round(__snake_case     , 3  )} loss"""  )
       model.eval()
       model.to(__snake_case  )
       del checkpoint, state_dict

       return model


def   __magic_name__	(      __snake_case				:       List[str]     , __snake_case				:       Tuple=False     , __snake_case				:       List[str]="text"  ) ->				Dict:







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

       lowercase    :				List[Any]       		=							'''cpu'''  # do conversion on cpu

       lowercase    :				List[Any]       		=							_get_ckpt_path(__snake_case     , use_small=__snake_case  )
       lowercase    :				int       		=							_load_model(__snake_case     , __snake_case     , model_type=__snake_case     , use_small=__snake_case  )

       # load bark initial model
       lowercase    :				Optional[Any]       		=							_bark_load_model(__snake_case     , "cpu"     , model_type=__snake_case     , use_small=__snake_case  )

       if model_type == "text":
              lowercase    :				Dict       		=							bark_model['''model''']

       if model.num_parameters(exclude_embeddings=__snake_case  ) != 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
       lowercase    :				List[str]       		=							5
       lowercase    :				List[str]       		=							10

       if model_type in ["text", "coarse"]:
              lowercase    :				Dict       		=							torch.randint(256     , (batch_size, sequence_length)     , dtype=torch.int  )
              lowercase    :				str       		=							bark_model(__snake_case  )[0]

              lowercase    :				Optional[int]       		=							model(__snake_case  )

              # take last logits
              lowercase    :				str       		=							output_new_model_total.logits[:, [-1], :]

       else:
              lowercase    :				Any       		=							3
              lowercase    :				List[Any]       		=							8
              lowercase    :				Tuple       		=							torch.randint(256     , (batch_size, sequence_length, n_codes_total)     , dtype=torch.int  )

              lowercase    :				Tuple       		=							model(__snake_case     , __snake_case  )
              lowercase    :				str       		=							bark_model(__snake_case     , __snake_case  )

              lowercase    :				str       		=							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(__snake_case  ).mkdir(exist_ok=__snake_case  )
       model.save_pretrained(__snake_case  )


def   __magic_name__	(      __snake_case				:       List[Any]     , __snake_case				:       str     , __snake_case				:       Optional[Any]     , __snake_case				:       str     , __snake_case				:       str     , __snake_case				:       Dict     , ) ->				Tuple:
       lowercase    :				List[str]       		=							os.path.join(__snake_case     , __snake_case  )

       lowercase    :				str       		=							BarkSemanticConfig.from_pretrained(os.path.join(__snake_case     , "config.json"  )  )
       lowercase    :				Optional[int]       		=							BarkCoarseConfig.from_pretrained(os.path.join(__snake_case     , "config.json"  )  )
       lowercase    :				Tuple       		=							BarkFineConfig.from_pretrained(os.path.join(__snake_case     , "config.json"  )  )
       lowercase    :				List[Any]       		=							EncodecConfig.from_pretrained("facebook/encodec_24khz"  )

       lowercase    :				Union[str, Any]       		=							BarkSemanticModel.from_pretrained(__snake_case  )
       lowercase    :				Dict       		=							BarkCoarseModel.from_pretrained(__snake_case  )
       lowercase    :				List[Any]       		=							BarkFineModel.from_pretrained(__snake_case  )
       lowercase    :				str       		=							EncodecModel.from_pretrained("facebook/encodec_24khz"  )

       lowercase    :				Tuple       		=							BarkConfig.from_sub_model_configs(
           __snake_case     , __snake_case     , __snake_case     , __snake_case  )

       lowercase    :				Union[str, Any]       		=							BarkGenerationConfig.from_sub_model_configs(
           semantic.generation_config     , coarseAcoustic.generation_config     , fineAcoustic.generation_config  )

       lowercase    :				int       		=							BarkModel(__snake_case  )

       lowercase    :				List[str]       		=							semantic
       lowercase    :				Any       		=							coarseAcoustic
       lowercase    :				Tuple       		=							fineAcoustic
       lowercase    :				List[Any]       		=							codec

       lowercase    :				int       		=							bark_generation_config

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


if __name__ == "__main__":
   _A			:       List[Any]   		=       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.""")

   _A			:       int   		=       parser.parse_args()

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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE = 1  ,			SCREAMING_SNAKE_CASE = 1_000	):
    '''simple docstring'''







    __UpperCamelCase     :Union[str, Any]			  =		1
    __UpperCamelCase     :Any			  =		0

    for divide_by_number in range(SCREAMING_SNAKE_CASE  ,			digit + 1	):
        __UpperCamelCase     :list[int]			  =		[]
        __UpperCamelCase     :Optional[int]			  =		numerator
        for _ in range(1  ,			digit + 1	):
            if now_divide in has_been_divided:
                if longest_list_length < len(SCREAMING_SNAKE_CASE	):
                    __UpperCamelCase     :Optional[Any]			  =		len(SCREAMING_SNAKE_CASE	)
                    __UpperCamelCase     :int			  =		divide_by_number
            else:
                has_been_divided.append(SCREAMING_SNAKE_CASE	)
                __UpperCamelCase     :Any			  =		now_divide * 10 % divide_by_number

    return the_digit


# Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()





