

'''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()
_snake_case     =  logging.get_logger(__name__)

set_seed(770)


_snake_case     =  {
    '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',
}


_snake_case     =  {
    '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',
    },
}

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







def 					_A		(	snake_case		,							snake_case=False     )			->	Optional[Any]:
   _lowercase			:      Dict			     =   model_type
   if use_small:
      key += "_small"
   return os.path.join(__UpperCAmelCase		,							REMOTE_MODEL_PATHS[key]["file_name"]     )







def 					_A		(	snake_case		,							snake_case     )			->	Any:
   os.makedirs(__UpperCAmelCase		,							exist_ok=__UpperCAmelCase     )
   hf_hub_download(repo_id=__UpperCAmelCase		,							filename=__UpperCAmelCase		,							local_dir=__UpperCAmelCase     )







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

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

   _lowercase			:      Tuple			     =   ConfigClass(**checkpoint["model_args"]     )
   _lowercase			:      Optional[int]			     =   ModelClass(config=__UpperCAmelCase     )
   _lowercase			:      Optional[Any]			     =   GenerationConfigClass()

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

         _lowercase			:      Optional[int]			     =   state_dict.pop(__UpperCAmelCase     )

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

   return model







def 					_A		(	snake_case		,							snake_case=False		,							snake_case="text"     )			->	Optional[Any]:
   if model_type not in ("text", "coarse", "fine"):
      raise NotImplementedError()

   _lowercase			:      List[str]			     =   '''cpu'''  # do conversion on cpu

   _lowercase			:      Union[str, Any]			     =   _get_ckpt_path(__UpperCAmelCase		,							use_small=__UpperCAmelCase     )
   _lowercase			:      Tuple			     =   _load_model(__UpperCAmelCase		,							__UpperCAmelCase		,							model_type=__UpperCAmelCase		,							use_small=__UpperCAmelCase     )

   # load bark initial model
   _lowercase			:      int			     =   _bark_load_model(__UpperCAmelCase		,							"cpu"		,							model_type=__UpperCAmelCase		,							use_small=__UpperCAmelCase     )

   if model_type == "text":
      _lowercase			:      List[str]			     =   bark_model['''model''']

   if model.num_parameters(exclude_embeddings=__UpperCAmelCase     ) != 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[Any]			     =   5
   _lowercase			:      Any			     =   10

   if model_type in ["text", "coarse"]:
      _lowercase			:      Union[str, Any]			     =   torch.randint(2_56		,							(batch_size, sequence_length)		,							dtype=torch.int     )
      _lowercase			:      str			     =   bark_model(__UpperCAmelCase     )[0]

      _lowercase			:      List[str]			     =   model(__UpperCAmelCase     )

      # take last logits
      _lowercase			:      Optional[int]			     =   output_new_model_total.logits[:, [-1], :]

   else:
      _lowercase			:      Dict			     =   3
      _lowercase			:      Dict			     =   8
      _lowercase			:      List[Any]			     =   torch.randint(2_56		,							(batch_size, sequence_length, n_codes_total)		,							dtype=torch.int     )

      _lowercase			:      List[str]			     =   model(__UpperCAmelCase		,							__UpperCAmelCase     )
      _lowercase			:      int			     =   bark_model(__UpperCAmelCase		,							__UpperCAmelCase     )

      _lowercase			:      List[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(__UpperCAmelCase     ).mkdir(exist_ok=__UpperCAmelCase     )
   model.save_pretrained(__UpperCAmelCase     )







def 					_A		(	snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case		,							)			->	Union[str, Any]:
   _lowercase			:      Union[str, Any]			     =   os.path.join(__UpperCAmelCase		,							__UpperCAmelCase     )

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

   _lowercase			:      Any			     =   BarkSemanticModel.from_pretrained(__UpperCAmelCase     )
   _lowercase			:      Any			     =   BarkCoarseModel.from_pretrained(__UpperCAmelCase     )
   _lowercase			:      int			     =   BarkFineModel.from_pretrained(__UpperCAmelCase     )
   _lowercase			:      List[Any]			     =   EncodecModel.from_pretrained("facebook/encodec_24khz"     )

   _lowercase			:      Dict			     =   BarkConfig.from_sub_model_configs(
       __UpperCAmelCase		,							__UpperCAmelCase		,							__UpperCAmelCase		,							__UpperCAmelCase     )

   _lowercase			:      Any			     =   BarkGenerationConfig.from_sub_model_configs(
       semantic.generation_config		,							coarseAcoustic.generation_config		,							fineAcoustic.generation_config     )

   _lowercase			:      int			     =   BarkModel(__UpperCAmelCase     )

   _lowercase			:      str			     =   semantic
   _lowercase			:      int			     =   coarseAcoustic
   _lowercase			:      str			     =   fineAcoustic
   _lowercase			:      List[Any]			     =   codec

   _lowercase			:      int			     =   bark_generation_config

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


if __name__ == "__main__":
    _snake_case     =  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.')

    _snake_case     =  parser.parse_args()

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







"""simple docstring"""






from __future__ import annotations

from fractions import Fraction



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  bool:
   return (
       num != den and num % 1_0 == den // 1_0 and (num // 1_0) / (den % 1_0) == num / den
   )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  list[str]:
   lowercase__:   str					=      []
   lowercase__:   str					=      1_1
   lowercase__:   str					=      int('''1''' + '''0''' * digit_len )
   for num in range(__UpperCAmelCase       ,    __UpperCAmelCase ):
      while den <= 9_9:
         if (num != den) and (num % 1_0 == den // 1_0) and (den % 1_0 != 0):
            if is_digit_cancelling(__UpperCAmelCase       ,    __UpperCAmelCase ):
               solutions.append(F"""{num}/{den}""" )
         den += 1
      num += 1
      lowercase__:   Dict					=      1_0
   return solutions



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase = 2 )  ->  int:
   lowercase__:   List[str]					=      1.0
   for fraction in fraction_list(__UpperCAmelCase ):
      lowercase__:   List[str]					=      Fraction(__UpperCAmelCase )
      result *= frac.denominator / frac.numerator
   return int(__UpperCAmelCase )


if __name__ == "__main__":
       print(solution())
