






"""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(770)


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      _lowerCAmelCase		(							UpperCAmelCase__       :					List[Any],						UpperCAmelCase__       :					Optional[Any]=False      )		->Dict:
      A__						:  Optional[int]		  =  model_type
      if use_small:
            key += "_small"
      return os.path.join(UpperCAmelCase__,						REMOTE_MODEL_PATHS[key]["""file_name"""]      )




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




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

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

      A__						:  Union[str, Any]		  =  ConfigClass(**checkpoint["""model_args"""]      )
      A__						:  Any		  =  ModelClass(config=UpperCAmelCase__      )
      A__						:  Optional[Any]		  =  GenerationConfigClass()

      A__						:  str		  =  model_generation_config
      A__						:  Optional[int]		  =  checkpoint["""model"""]
      # fixup checkpoint
      A__						:  Optional[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
                  A__						:  str		  =  k[len(UpperCAmelCase__      ) :]
                  for old_layer_name in new_layer_name_dict:
                        A__						:  Optional[Any]		  =  new_k.replace(UpperCAmelCase__,						new_layer_name_dict[old_layer_name]      )

                  A__						:  List[str]		  =  state_dict.pop(UpperCAmelCase__      )

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

      A__						:  Union[str, Any]		  =  """cpu"""  # do conversion on cpu

      A__						:  Optional[int]		  =  _get_ckpt_path(UpperCAmelCase__,						use_small=UpperCAmelCase__      )
      A__						:  str		  =  _load_model(UpperCAmelCase__,						UpperCAmelCase__,						model_type=UpperCAmelCase__,						use_small=UpperCAmelCase__      )

      # load bark initial model
      A__						:  Optional[int]		  =  _bark_load_model(UpperCAmelCase__,						"""cpu""",						model_type=UpperCAmelCase__,						use_small=UpperCAmelCase__      )

      if model_type == "text":
            A__						:  Union[str, 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
      A__						:  Any		  =  5
      A__						:  int		  =  1_0

      if model_type in ["text", "coarse"]:
            A__						:  int		  =  torch.randint(2_5_6,						(batch_size, sequence_length),						dtype=torch.int      )
            A__						:  Dict		  =  bark_model(UpperCAmelCase__      )[0]

            A__						:  List[Any]		  =  model(UpperCAmelCase__      )

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

      else:
            A__						:  int		  =  3
            A__						:  int		  =  8
            A__						:  Any		  =  torch.randint(2_5_6,						(batch_size, sequence_length, n_codes_total),						dtype=torch.int      )

            A__						:  List[str]		  =  model(UpperCAmelCase__,						UpperCAmelCase__      )
            A__						:  Optional[int]		  =  bark_model(UpperCAmelCase__,						UpperCAmelCase__      )

            A__						:  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      _lowerCAmelCase		(							UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					List[str],						UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					Union[str, Any],						UpperCAmelCase__       :					Dict,						)		->List[str]:
      A__						:  List[Any]		  =  os.path.join(UpperCAmelCase__,						UpperCAmelCase__      )

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

      A__						:  int		  =  BarkSemanticModel.from_pretrained(UpperCAmelCase__      )
      A__						:  str		  =  BarkCoarseModel.from_pretrained(UpperCAmelCase__      )
      A__						:  Any		  =  BarkFineModel.from_pretrained(UpperCAmelCase__      )
      A__						:  Dict		  =  EncodecModel.from_pretrained("""facebook/encodec_24khz"""      )

      A__						:  Union[str, Any]		  =  BarkConfig.from_sub_model_configs(
          UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__      )

      A__						:  Dict		  =  BarkGenerationConfig.from_sub_model_configs(
          semantic.generation_config,						coarseAcoustic.generation_config,						fineAcoustic.generation_config      )

      A__						:  Optional[Any]		  =  BarkModel(UpperCAmelCase__      )

      A__						:  Tuple		  =  semantic
      A__						:  int		  =  coarseAcoustic
      A__						:  Dict		  =  fineAcoustic
      A__						:  Union[str, Any]		  =  codec

      A__						:  Optional[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 warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


A_    =							logging.get_logger(__name__)






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							['input_values', 'attention_mask']


							def __init__(			self      :						Union[str, Any]			,    snake_case      :						int = 1			,    snake_case      :						int = 1_6000			,    snake_case      :						float = 0.0			,    snake_case      :						bool = False			,    snake_case      :						int = 80			,    snake_case      :						int = 16			,    snake_case      :						int = 64			,    snake_case      :						str = "hann_window"			,    snake_case      :						float = 1.0			,    snake_case      :						float = 80			,    snake_case      :						float = 7600			,    snake_case      :						float = 1e-10			,    snake_case      :						int = 2			,    snake_case      :						bool = True			,    **snake_case      :						Dict			,    ):
													'''simple docstring'''





													super().__init__(feature_size=snake_case			,    sampling_rate=snake_case			,    padding_value=snake_case			,    **snake_case							)
													A__						:  Optional[int]		  =  do_normalize
													A__						:  List[Any]		  =  return_attention_mask

													A__						:  Tuple		  =  num_mel_bins
													A__						:  Optional[int]		  =  hop_length
													A__						:  List[str]		  =  win_length
													A__						:  List[Any]		  =  win_function
													A__						:  Tuple		  =  frame_signal_scale
													A__						:  Optional[Any]		  =  fmin
													A__						:  str		  =  fmax
													A__						:  str		  =  mel_floor
													A__						:  Dict		  =  reduction_factor

													A__						:  int		  =  win_length * sampling_rate // 1000
													A__						:  Optional[Any]		  =  hop_length * sampling_rate // 1000
													A__						:  Optional[int]		  =  optimal_fft_length(self.sample_size							)
													A__						:  List[Any]		  =  (self.n_fft // 2) + 1

													A__						:  str		  =  window_function(window_length=self.sample_size			,    name=self.win_function			,    periodic=snake_case							)

													A__						:  Optional[Any]		  =  mel_filter_bank(
													    num_frequency_bins=self.n_freqs			,    num_mel_filters=self.num_mel_bins			,    min_frequency=self.fmin			,    max_frequency=self.fmax			,    sampling_rate=self.sampling_rate			,    norm="""slaney"""			,    mel_scale="""slaney"""			,    )

													if frame_signal_scale != 1.0:
																			warnings.warn(
																			    """The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"""			,    snake_case			,    )
													if reduction_factor != 2.0:
																			warnings.warn(
																			    """The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"""			,    snake_case			,    )


							@staticmethod
							# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
							def 						_UpperCamelCase       (			snake_case      :						List[np.ndarray]			,    snake_case      :						List[np.ndarray]			,    snake_case      :						float = 0.0							):
													'''simple docstring'''





													if attention_mask is not None:
																			A__						:  Tuple		  =  np.array(snake_case			,    np.intaa							)
																			A__						:  List[Any]		  =  []

																			for vector, length in zip(snake_case			,    attention_mask.sum(-1							)							):
																									A__						:  str		  =  (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7							)
																									if length < normed_slice.shape[0]:
																															A__						:  int		  =  padding_value

																									normed_input_values.append(snake_case							)
													else:
																			A__						:  List[Any]		  =  [(x - x.mean()) / np.sqrt(x.var() + 1e-7							) for x in input_values]

													return normed_input_values


							def 						_UpperCamelCase       (			self      :						Optional[int]			,    snake_case      :						np.ndarray			,    ):
													'''simple docstring'''





													A__						:  List[Any]		  =  spectrogram(
													    snake_case			,    window=self.window			,    frame_length=self.sample_size			,    hop_length=self.sample_stride			,    fft_length=self.n_fft			,    mel_filters=self.mel_filters			,    mel_floor=self.mel_floor			,    log_mel="""log10"""			,    )
													return log_mel_spec.T


							def __call__(			self      :						str			,    snake_case      :						Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None			,    snake_case      :						Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None			,    snake_case      :						Union[bool, str, PaddingStrategy] = False			,    snake_case      :						Optional[int] = None			,    snake_case      :						bool = False			,    snake_case      :						Optional[int] = None			,    snake_case      :						Optional[bool] = None			,    snake_case      :						Optional[Union[str, TensorType]] = None			,    snake_case      :						Optional[int] = None			,    **snake_case      :						Union[str, Any]			,    ):
													'''simple docstring'''





													if audio is None and audio_target is None:
																			raise ValueError("""You must provide either `audio` or `audio_target` values."""							)

													if sampling_rate is not None:
																			if sampling_rate != self.sampling_rate:
																									raise ValueError(
																									    F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
																									    F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
																									    F' {self.sampling_rate} and not {sampling_rate}.'							)
													else:
																			logger.warning(
																			    """It is strongly recommended to pass the ``sampling_rate`` argument to this function. """
																			    """Failing to do so can result in silent errors that might be hard to debug."""							)

													if audio is not None:
																			A__						:  Dict		  =  self._process_audio(
																			    snake_case			,    snake_case			,    snake_case			,    snake_case			,    snake_case			,    snake_case			,    snake_case			,    snake_case			,    **snake_case			,    )
													else:
																			A__						:  Dict		  =  None

													if audio_target is not None:
																			A__						:  Union[str, Any]		  =  self._process_audio(
																			    snake_case			,    snake_case			,    snake_case			,    snake_case			,    snake_case			,    snake_case			,    snake_case			,    snake_case			,    **snake_case			,    )

																			if inputs is None:
																									return inputs_target
																			else:
																									A__						:  Union[str, Any]		  =  inputs_target["""input_values"""]
																									A__						:  str		  =  inputs_target.get("""attention_mask"""							)
																									if decoder_attention_mask is not None:
																															A__						:  List[str]		  =  decoder_attention_mask

													return inputs


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,    snake_case      :						bool = False			,    snake_case      :						Union[bool, str, PaddingStrategy] = False			,    snake_case      :						Optional[int] = None			,    snake_case      :						bool = False			,    snake_case      :						Optional[int] = None			,    snake_case      :						Optional[bool] = None			,    snake_case      :						Optional[Union[str, TensorType]] = None			,    **snake_case      :						Union[str, Any]			,    ):
													'''simple docstring'''





													A__						:  List[str]		  =  isinstance(snake_case			,    np.ndarray							) and len(speech.shape							) > 1
													if is_batched_numpy and len(speech.shape							) > 2:
																			raise ValueError(F'Only mono-channel audio is supported for input to {self}'							)
													A__						:  Tuple		  =  is_batched_numpy or (
													    isinstance(snake_case			,    (list, tuple)							) and (isinstance(speech[0]			,    (np.ndarray, tuple, list)							))
													)

													if is_batched:
																			A__						:  Tuple		  =  [np.asarray(snake_case			,    dtype=np.floataa							) for speech in speech]
													elif not is_batched and not isinstance(snake_case			,    np.ndarray							):
																			A__						:  Tuple		  =  np.asarray(snake_case			,    dtype=np.floataa							)
													elif isinstance(snake_case			,    np.ndarray							) and speech.dtype is np.dtype(np.floataa							):
																			A__						:  str		  =  speech.astype(np.floataa							)

													# always return batch
													if not is_batched:
																			A__						:  Optional[Any]		  =  [speech]

													# needed to make pad() work on spectrogram inputs
													A__						:  str		  =  self.feature_size

													# convert into correct format for padding
													if is_target:
																			A__						:  str		  =  [self._extract_mel_features(snake_case							) for waveform in speech]
																			A__						:  List[str]		  =  BatchFeature({"""input_values""": features}							)
																			A__						:  Dict		  =  self.num_mel_bins
													else:
																			A__						:  Tuple		  =  BatchFeature({"""input_values""": speech}							)

													A__						:  List[str]		  =  self.pad(
													    snake_case			,    padding=snake_case			,    max_length=snake_case			,    truncation=snake_case			,    pad_to_multiple_of=snake_case			,    return_attention_mask=snake_case			,    **snake_case			,    )

													A__						:  List[str]		  =  feature_size_hack

													# convert input values to correct format
													A__						:  str		  =  padded_inputs["""input_values"""]
													if not isinstance(input_values[0]			,    np.ndarray							):
																			A__						:  Tuple		  =  [np.asarray(snake_case			,    dtype=np.floataa							) for array in input_values]
													elif (
													    not isinstance(snake_case			,    np.ndarray							)
													    and isinstance(input_values[0]			,    np.ndarray							)
													    and input_values[0].dtype is np.dtype(np.floataa							)
													):
																			A__						:  Dict		  =  [array.astype(np.floataa							) for array in input_values]
													elif isinstance(snake_case			,    np.ndarray							) and input_values.dtype is np.dtype(np.floataa							):
																			A__						:  Any		  =  input_values.astype(np.floataa							)

													# convert attention_mask to correct format
													A__						:  List[Any]		  =  padded_inputs.get("""attention_mask"""							)
													if attention_mask is not None:
																			A__						:  Any		  =  [np.asarray(snake_case			,    dtype=np.intaa							) for array in attention_mask]

													# zero-mean and unit-variance normalization
													if not is_target and self.do_normalize:
																			A__						:  Optional[int]		  =  (
																			    attention_mask
																			    if self._get_padding_strategies(snake_case			,    max_length=snake_case							) is not PaddingStrategy.DO_NOT_PAD
																			    else None
																			)
																			A__						:  Optional[int]		  =  self.zero_mean_unit_var_norm(
																			    padded_inputs["""input_values"""]			,    attention_mask=snake_case			,    padding_value=self.padding_value							)

													if return_tensors is not None:
																			A__						:  Any		  =  padded_inputs.convert_to_tensors(snake_case							)

													return padded_inputs


							def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
													'''simple docstring'''





													A__						:  List[str]		  =  super().to_dict()

													# Don't serialize these as they are derived from the other properties.
													A__						:  Dict		  =  ["""window""", """mel_filters""", """sample_size""", """sample_stride""", """n_fft""", """n_freqs"""]
													for name in names:
																			if name in output:
																									del output[name]

													return output






