



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__      :				Dict   	=			logging.get_logger(__name__)

set_seed(7_70)


a__      :				Optional[int]   	=			{
    "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__      :				Dict   	=			{
    "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__      :				Dict   	=			os.path.join(os.path.expanduser("~"), ".cache")
a__      :				str   	=			os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")

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

def   _lowerCAmelCase   (						A__						,  A__	):
      os.makedirs(A__						,  exist_ok=A__	)
      hf_hub_download(repo_id=A__						,  filename=A__						,  local_dir=A__	)

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

      # convert Bark model arguments to HF Bark model arguments
      lowercase__     =  model_args.pop('n_head'	)
      lowercase__     =  model_args.pop('n_embd'	)
      lowercase__     =  model_args.pop('n_layer'	)

      lowercase__     =  ConfigClass(**checkpoint['model_args']	)
      lowercase__     =  ModelClass(config=A__	)
      lowercase__     =  GenerationConfigClass()

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

                  lowercase__     =  state_dict.pop(A__	)

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

      return model

def   _lowerCAmelCase   (						A__						,  A__=False						,  A__="text"	):
      if model_type not in ("text", "coarse", "fine"):
            raise NotImplementedError()

      lowercase__     =  'cpu'  # do conversion on cpu

      lowercase__     =  _get_ckpt_path(A__						,  use_small=A__	)
      lowercase__     =  _load_model(A__						,  A__						,  model_type=A__						,  use_small=A__	)

      # load bark initial model
      lowercase__     =  _bark_load_model(A__						,  'cpu'						,  model_type=A__						,  use_small=A__	)

      if model_type == "text":
            lowercase__     =  bark_model['model']

      if model.num_parameters(exclude_embeddings=A__	) != 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__     =  5
      lowercase__     =  10

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

            lowercase__     =  model(A__	)

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

      else:
            lowercase__     =  3
            lowercase__     =  8
            lowercase__     =  torch.randint(256						,  (batch_size, sequence_length, n_codes_total)						,  dtype=torch.int	)

            lowercase__     =  model(A__						,  A__	)
            lowercase__     =  bark_model(A__						,  A__	)

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

def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__						,  A__						,  A__						,  ):
      lowercase__     =  os.path.join(A__						,  A__	)

      lowercase__     =  BarkSemanticConfig.from_pretrained(os.path.join(A__						,  'config.json'	)	)
      lowercase__     =  BarkCoarseConfig.from_pretrained(os.path.join(A__						,  'config.json'	)	)
      lowercase__     =  BarkFineConfig.from_pretrained(os.path.join(A__						,  'config.json'	)	)
      lowercase__     =  EncodecConfig.from_pretrained('facebook/encodec_24khz'	)

      lowercase__     =  BarkSemanticModel.from_pretrained(A__	)
      lowercase__     =  BarkCoarseModel.from_pretrained(A__	)
      lowercase__     =  BarkFineModel.from_pretrained(A__	)
      lowercase__     =  EncodecModel.from_pretrained('facebook/encodec_24khz'	)

      lowercase__     =  BarkConfig.from_sub_model_configs(
          A__						,  A__						,  A__						,  A__	)

      lowercase__     =  BarkGenerationConfig.from_sub_model_configs(
          semantic.generation_config						,  coarseAcoustic.generation_config						,  fineAcoustic.generation_config	)

      lowercase__     =  BarkModel(A__	)

      lowercase__     =  semantic
      lowercase__     =  coarseAcoustic
      lowercase__     =  fineAcoustic
      lowercase__     =  codec

      lowercase__     =  bark_generation_config

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


if __name__ == "__main__":
       a__      :				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__      :				str   	=			parser.parse_args()

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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__      :				Dict   	=			logging.get_logger(__name__)

a__      :				List[Any]   	=			{
    "facebook/s2t-small-librispeech-asr": (
        "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  int       				=    "speech_to_text"
   A    :  Optional[Any]       				=    ["past_key_values"]
   A    :  Optional[int]       				=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


   def __init__(						self  :			Optional[int]			,				lowerCAmelCase  :			Tuple=1_00_00			,				lowerCAmelCase  :			int=12			,				lowerCAmelCase  :			int=20_48			,				lowerCAmelCase  :			Union[str, Any]=4			,				lowerCAmelCase  :			str=6			,				lowerCAmelCase  :			Dict=20_48			,				lowerCAmelCase  :			Dict=4			,				lowerCAmelCase  :			Optional[int]=0.0			,				lowerCAmelCase  :			Union[str, Any]=0.0			,				lowerCAmelCase  :			int=True			,				lowerCAmelCase  :			Optional[Any]=True			,				lowerCAmelCase  :			Dict="relu"			,				lowerCAmelCase  :			Tuple=2_56			,				lowerCAmelCase  :			Dict=0.1			,				lowerCAmelCase  :			Optional[Any]=0.0			,				lowerCAmelCase  :			List[Any]=0.0			,				lowerCAmelCase  :			Any=0.02			,				lowerCAmelCase  :			List[Any]=2			,				lowerCAmelCase  :			Tuple=True			,				lowerCAmelCase  :			Tuple=1			,				lowerCAmelCase  :			List[str]=0			,				lowerCAmelCase  :			Union[str, Any]=2			,				lowerCAmelCase  :			Any=60_00			,				lowerCAmelCase  :			Optional[int]=10_24			,				lowerCAmelCase  :			Optional[Any]=2			,				lowerCAmelCase  :			Optional[Any]=(5, 5)			,				lowerCAmelCase  :			Union[str, Any]=10_24			,				lowerCAmelCase  :			List[Any]=80			,				lowerCAmelCase  :			List[str]=1			,				**lowerCAmelCase  :			List[str]			,				)	->				Dict:




         """simple docstring"""

         lowercase__     =  vocab_size
         lowercase__     =  d_model
         lowercase__     =  encoder_ffn_dim
         lowercase__     =  encoder_layers
         lowercase__     =  encoder_attention_heads
         lowercase__     =  decoder_ffn_dim
         lowercase__     =  decoder_layers
         lowercase__     =  decoder_attention_heads
         lowercase__     =  dropout
         lowercase__     =  attention_dropout
         lowercase__     =  activation_dropout
         lowercase__     =  activation_function
         lowercase__     =  init_std
         lowercase__     =  encoder_layerdrop
         lowercase__     =  decoder_layerdrop
         lowercase__     =  use_cache
         lowercase__     =  encoder_layers
         lowercase__     =  scale_embedding  # scale factor will be sqrt(d_model) if True
         lowercase__     =  max_source_positions
         lowercase__     =  max_target_positions
         lowercase__     =  num_conv_layers
         lowercase__     =  list(lowerCAmelCase)
         lowercase__     =  conv_channels
         lowercase__     =  input_feat_per_channel
         lowercase__     =  input_channels

         if len(self.conv_kernel_sizes) != self.num_conv_layers:
               raise ValueError(
                   'Configuration for convolutional module is incorrect. '
                   'It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '
                   f'''but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes)}`, '''
                   f'''`config.num_conv_layers = {self.num_conv_layers}`.''')

         super().__init__(
             pad_token_id=lowerCAmelCase			,				bos_token_id=lowerCAmelCase			,				eos_token_id=lowerCAmelCase			,				is_encoder_decoder=lowerCAmelCase			,				decoder_start_token_id=lowerCAmelCase			,				**lowerCAmelCase			,				)
