




"""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													=      logging.get_logger(__name__)

set_seed(7_7_0)


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



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase=False				)						->    Dict:
      lowerCAmelCase__      :			int   =	model_type
      if use_small:
            key += "_small"
      return os.path.join(__UpperCAmelCase    ,		REMOTE_MODEL_PATHS[key]["""file_name"""]				)



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    List[str]:
      os.makedirs(__UpperCAmelCase    ,		exist_ok=__UpperCAmelCase				)
      hf_hub_download(repo_id=__UpperCAmelCase    ,		filename=__UpperCAmelCase    ,		local_dir=__UpperCAmelCase				)



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase=False    ,		__UpperCAmelCase="text"				)						->    List[Any]:
      if model_type == "text":
            lowerCAmelCase__      :			Dict   =	BarkSemanticModel
            lowerCAmelCase__      :			Optional[Any]   =	BarkSemanticConfig
            lowerCAmelCase__      :			int   =	BarkSemanticGenerationConfig
      elif model_type == "coarse":
            lowerCAmelCase__      :			str   =	BarkCoarseModel
            lowerCAmelCase__      :			Optional[Any]   =	BarkCoarseConfig
            lowerCAmelCase__      :			List[str]   =	BarkCoarseGenerationConfig
      elif model_type == "fine":
            lowerCAmelCase__      :			Dict   =	BarkFineModel
            lowerCAmelCase__      :			Any   =	BarkFineConfig
            lowerCAmelCase__      :			str   =	BarkFineGenerationConfig
      else:
            raise NotImplementedError()
      lowerCAmelCase__      :			Tuple   =	f"""{model_type}_small""" if use_small else model_type
      lowerCAmelCase__      :			int   =	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"""]				)
      lowerCAmelCase__      :			Union[str, Any]   =	torch.load(__UpperCAmelCase    ,		map_location=__UpperCAmelCase				)
      # this is a hack
      lowerCAmelCase__      :			Optional[int]   =	checkpoint["""model_args"""]
      if "input_vocab_size" not in model_args:
            lowerCAmelCase__      :			List[str]   =	model_args["""vocab_size"""]
            lowerCAmelCase__      :			str   =	model_args["""vocab_size"""]
            del model_args["vocab_size"]

      # convert Bark model arguments to HF Bark model arguments
      lowerCAmelCase__      :			Dict   =	model_args.pop("""n_head"""				)
      lowerCAmelCase__      :			Tuple   =	model_args.pop("""n_embd"""				)
      lowerCAmelCase__      :			Any   =	model_args.pop("""n_layer"""				)

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

      lowerCAmelCase__      :			Tuple   =	model_generation_config
      lowerCAmelCase__      :			List[str]   =	checkpoint["""model"""]
      # fixup checkpoint
      lowerCAmelCase__      :			int   =	"""_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
                  lowerCAmelCase__      :			str   =	k[len(__UpperCAmelCase				) :]
                  for old_layer_name in new_layer_name_dict:
                        lowerCAmelCase__      :			Tuple   =	new_k.replace(__UpperCAmelCase    ,		new_layer_name_dict[old_layer_name]				)

                  lowerCAmelCase__      :			List[Any]   =	state_dict.pop(__UpperCAmelCase				)

      lowerCAmelCase__      :			List[Any]   =	set(state_dict.keys()				) - set(model.state_dict().keys()				)
      lowerCAmelCase__      :			Any   =	{k for k in extra_keys if not k.endswith(""".attn.bias"""				)}
      lowerCAmelCase__      :			Dict   =	set(model.state_dict().keys()				) - set(state_dict.keys()				)
      lowerCAmelCase__      :			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				)
      lowerCAmelCase__      :			Dict   =	model.num_parameters(exclude_embeddings=__UpperCAmelCase				)
      lowerCAmelCase__      :			List[str]   =	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 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase=False    ,		__UpperCAmelCase="text"				)						->    Optional[Any]:
      if model_type not in ("text", "coarse", "fine"):
            raise NotImplementedError()

      lowerCAmelCase__      :			Optional[int]   =	"""cpu"""  # do conversion on cpu

      lowerCAmelCase__      :			List[Any]   =	_get_ckpt_path(__UpperCAmelCase    ,		use_small=__UpperCAmelCase				)
      lowerCAmelCase__      :			Optional[Any]   =	_load_model(__UpperCAmelCase    ,		__UpperCAmelCase    ,		model_type=__UpperCAmelCase    ,		use_small=__UpperCAmelCase				)

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

      if model_type == "text":
            lowerCAmelCase__      :			Optional[Any]   =	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
      lowerCAmelCase__      :			Tuple   =	5
      lowerCAmelCase__      :			Optional[Any]   =	10

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

            lowerCAmelCase__      :			Dict   =	model(__UpperCAmelCase				)

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

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

            lowerCAmelCase__      :			str   =	model(__UpperCAmelCase    ,		__UpperCAmelCase				)
            lowerCAmelCase__      :			Tuple   =	bark_model(__UpperCAmelCase    ,		__UpperCAmelCase				)

            lowerCAmelCase__      :			Optional[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(__UpperCAmelCase				).mkdir(exist_ok=__UpperCAmelCase				)
      model.save_pretrained(__UpperCAmelCase				)



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		)						->    str:
      lowerCAmelCase__      :			Optional[Any]   =	os.path.join(__UpperCAmelCase    ,		__UpperCAmelCase				)

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

      lowerCAmelCase__      :			Any   =	BarkSemanticModel.from_pretrained(__UpperCAmelCase				)
      lowerCAmelCase__      :			Dict   =	BarkCoarseModel.from_pretrained(__UpperCAmelCase				)
      lowerCAmelCase__      :			Optional[Any]   =	BarkFineModel.from_pretrained(__UpperCAmelCase				)
      lowerCAmelCase__      :			List[Any]   =	EncodecModel.from_pretrained("""facebook/encodec_24khz"""				)

      lowerCAmelCase__      :			str   =	BarkConfig.from_sub_model_configs(
          __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)

      lowerCAmelCase__      :			Tuple   =	BarkGenerationConfig.from_sub_model_configs(
          semantic.generation_config    ,		coarseAcoustic.generation_config    ,		fineAcoustic.generation_config				)

      lowerCAmelCase__      :			int   =	BarkModel(__UpperCAmelCase				)

      lowerCAmelCase__      :			Union[str, Any]   =	semantic
      lowerCAmelCase__      :			Union[str, Any]   =	coarseAcoustic
      lowerCAmelCase__      :			Tuple   =	fineAcoustic
      lowerCAmelCase__      :			Tuple   =	codec

      lowerCAmelCase__      :			List[Any]   =	bark_generation_config

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


if __name__ == "__main__":
  _A													=      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													=      parser.parse_args()

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











"""simple docstring"""


import os
from collections import deque

import torch
from torch.utils.data import Dataset


class _lowerCamelCase    (					a_						):






   def __init__(		self       :		Tuple   ,     UpperCamelCase       :		List[Any]=""   ,     UpperCamelCase       :		List[str]="train"   )			->						List[Any]:

         """simple docstring"""
         assert os.path.isdir(UpperCamelCase   )

         lowerCAmelCase__      :			Union[str, Any]   =	[]
         lowerCAmelCase__      :			List[Any]   =	os.listdir(UpperCamelCase   )
         for story_filename in story_filenames_list:
               if "summary" in story_filename:
                     continue
               lowerCAmelCase__      :			Any   =	os.path.join(UpperCamelCase   ,     UpperCamelCase   )
               if not os.path.isfile(UpperCamelCase   ):
                     continue
               self.documents.append(UpperCamelCase   )






   def __len__(		self       :		List[Any]   )			->						int:

         """simple docstring"""
         return len(self.documents   )






   def __getitem__(		self       :		str   ,     UpperCamelCase       :		Union[str, Any]   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	self.documents[idx]
         lowerCAmelCase__      :			List[Any]   =	document_path.split("""/"""   )[-1]
         with open(UpperCamelCase   ,     encoding="""utf-8"""   ) as source:
               lowerCAmelCase__      :			List[str]   =	source.read()
               lowerCAmelCase__ ,   lowerCAmelCase__      :			Union[str, Any]   =	process_story(UpperCamelCase   )
         return document_name, story_lines, summary_lines






def 	lowercase_				(			__UpperCAmelCase				)						->    Union[str, Any]:
      lowerCAmelCase__      :			Tuple   =	list(filter(lambda __UpperCAmelCase				: len(__UpperCAmelCase				) != 0    ,		[line.strip() for line in raw_story.split("""\n"""				)]				)				)

      # for some unknown reason some lines miss a period, add it
      lowerCAmelCase__      :			List[str]   =	[_add_missing_period(__UpperCAmelCase				) for line in nonempty_lines]

      # gather article lines
      lowerCAmelCase__      :			Optional[int]   =	[]
      lowerCAmelCase__      :			Optional[int]   =	deque(__UpperCAmelCase				)
      while True:
            try:
                  lowerCAmelCase__      :			List[Any]   =	lines.popleft()
                  if element.startswith("""@highlight"""				):
                        break
                  story_lines.append(__UpperCAmelCase				)
            except IndexError:
                  # if "@highlight" is absent from the file we pop
                  # all elements until there is None, raising an exception.
                  return story_lines, []

    # gather summary lines
      lowerCAmelCase__      :			Any   =	list(filter(lambda __UpperCAmelCase				: not t.startswith("""@highlight"""				)    ,		__UpperCAmelCase				)				)

      return story_lines, summary_lines



def 	lowercase_				(			__UpperCAmelCase				)						->    Optional[int]:
      lowerCAmelCase__      :			List[str]   =	[""".""", """!""", """?""", """...""", """'""", """`""", """\"""", """\u2019""", """\u2019""", """)"""]
      if line.startswith("""@highlight"""				):
            return line
      if line[-1] in END_TOKENS:
            return line
      return line + "."



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    List[Any]:
      if len(__UpperCAmelCase				) > block_size:
            return sequence[:block_size]
      else:
            sequence.extend([pad_token_id] * (block_size - len(__UpperCAmelCase				))				)
            return sequence



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Tuple:
      lowerCAmelCase__      :			List[str]   =	torch.ones_like(__UpperCAmelCase				)
      lowerCAmelCase__      :			List[str]   =	sequence == pad_token_id
      lowerCAmelCase__      :			str   =	0
      return mask



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    List[Any]:
      lowerCAmelCase__      :			Tuple   =	[tokenizer.encode(__UpperCAmelCase				) for line in story_lines]
      lowerCAmelCase__      :			List[str]   =	[token for sentence in story_lines_token_ids for token in sentence]
      lowerCAmelCase__      :			int   =	[tokenizer.encode(__UpperCAmelCase				) for line in summary_lines]
      lowerCAmelCase__      :			Union[str, Any]   =	[token for sentence in summary_lines_token_ids for token in sentence]

      return story_token_ids, summary_token_ids



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    List[str]:
      lowerCAmelCase__      :			Tuple   =	[]
      for sequence in batch:
            lowerCAmelCase__      :			Union[str, Any]   =	-1
            lowerCAmelCase__      :			List[str]   =	[]
            for s in sequence:
                  if s == separator_token_id:
                        sentence_num += 1
                  embeddings.append(sentence_num % 2				)
            batch_embeddings.append(__UpperCAmelCase				)
      return torch.tensor(__UpperCAmelCase				)





