


import copy
import os
from typing import Dict, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings, logging
from ..auto import AutoConfig


__UpperCAmelCase			:			Optional[int]			      =					logging.get_logger(__name__)


__UpperCAmelCase			:			List[str]			      =					{
    "suno/bark-small": "https://huggingface.co/suno/bark-small/resolve/main/config.json",
    "suno/bark": "https://huggingface.co/suno/bark/resolve/main/config.json",
}

__UpperCAmelCase			:			Dict			      =					"\n    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model\n    according to the specified arguments, defining the model architecture. Instantiating a configuration with the\n    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)\n    architecture.\n\n    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the\n    documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        block_size (`int`, *optional*, defaults to 1024):\n            The maximum sequence length that this model might ever be used with. Typically set this to something large\n            just in case (e.g., 512 or 1024 or 2048).\n        input_vocab_size (`int`, *optional*, defaults to 10_048):\n            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the\n            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with\n            regards to the chosen sub-model.\n        output_vocab_size (`int`, *optional*, defaults to 10_048):\n            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented\n            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought\n            with regards to the chosen sub-model.\n        num_layers (`int`, *optional*, defaults to 12):\n            Number of hidden layers in the given sub-model.\n        num_heads (`int`, *optional*, defaults to 12):\n            Number of attention heads for each attention layer in the Transformer architecture.\n        hidden_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the \"intermediate\" (often named feed-forward) layer in the architecture.\n        dropout (`float`, *optional*, defaults to 0.0):\n            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.\n        bias (`bool`, *optional*, defaults to `True`):\n            Whether or not to use bias in the linear layers and layer norm layers.\n        initializer_range (`float`, *optional*, defaults to 0.02):\n            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n"



class  UpperCAmelCase_						(				_a):
       '''simple docstring'''
       __UpperCamelCase   :  Tuple				=       "bark_module"
       __UpperCamelCase   :  Optional[int]				=       ["past_key_values"]

       __UpperCamelCase   :  Tuple				=       {
           "num_attention_heads": "num_heads",
           "num_hidden_layers": "num_layers",
           "vocab_size": "input_vocab_size",
           "window_size": "block_size",
       }






       def __init__(  self			,       __SCREAMING_SNAKE_CASE=1_024			,       __SCREAMING_SNAKE_CASE=10_048			,       __SCREAMING_SNAKE_CASE=10_048			,       __SCREAMING_SNAKE_CASE=12			,       __SCREAMING_SNAKE_CASE=12			,       __SCREAMING_SNAKE_CASE=768			,       __SCREAMING_SNAKE_CASE=0.0			,       __SCREAMING_SNAKE_CASE=True			,       __SCREAMING_SNAKE_CASE=0.02			,       __SCREAMING_SNAKE_CASE=True			,       **__SCREAMING_SNAKE_CASE			,       ):




              """simple docstring"""




              UpperCamelCase     :						int 						=		block_size
              UpperCamelCase     :						Optional[int] 						=		input_vocab_size
              UpperCamelCase     :						str 						=		output_vocab_size
              UpperCamelCase     :						Optional[int] 						=		num_layers
              UpperCamelCase     :						str 						=		num_heads
              UpperCamelCase     :						Any 						=		hidden_size
              UpperCamelCase     :						Optional[int] 						=		dropout
              UpperCamelCase     :						Any 						=		bias
              UpperCamelCase     :						Optional[Any] 						=		use_cache
              UpperCamelCase     :						Any 						=		initializer_range

              super().__init__(**__SCREAMING_SNAKE_CASE    )






       @classmethod
       def 			_lowercase (  cls			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE = None			,       __SCREAMING_SNAKE_CASE = False			,       __SCREAMING_SNAKE_CASE = False			,       __SCREAMING_SNAKE_CASE = None			,       __SCREAMING_SNAKE_CASE = "main"			,       **__SCREAMING_SNAKE_CASE			,       ):




              """simple docstring"""




              UpperCamelCase     :						Optional[Any] 						=		cache_dir
              UpperCamelCase     :						Union[str, Any] 						=		force_download
              UpperCamelCase     :						Tuple 						=		local_files_only
              UpperCamelCase     :						int 						=		revision

              cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )

              UpperCamelCase						,			UpperCamelCase     :						List[str] 						=		cls.get_config_dict(__SCREAMING_SNAKE_CASE			,       **__SCREAMING_SNAKE_CASE    )

              # get the config dict if we are loading from Bark
              if config_dict.get('''model_type'''    ) == "bark":
                     UpperCamelCase     :						List[Any] 						=		config_dict[f"""{cls.model_type}_config"""]

              if "model_type" in config_dict and hasattr(cls			,       '''model_type'''    ) and config_dict["model_type"] != cls.model_type:
                     logger.warning(
                         f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                         f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""    )

              return cls.from_dict(__SCREAMING_SNAKE_CASE			,       **__SCREAMING_SNAKE_CASE    )




@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkSemanticConfig",  model="BarkSemanticModel"),  "\n    Example:\n\n    ```python\n    >>> from transformers import BarkSemanticConfig, BarkSemanticModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkSemanticConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkSemanticModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```",  )
class  UpperCAmelCase_						(				_a):
       '''simple docstring'''
       __UpperCamelCase   :  List[str]				=       "semantic"




@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkCoarseConfig",  model="BarkCoarseModel"),  "\n    Example:\n\n    ```python\n    >>> from transformers import BarkCoarseConfig, BarkCoarseModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkCoarseConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkCoarseModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```",  )
class  UpperCAmelCase_						(				_a):
       '''simple docstring'''
       __UpperCamelCase   :  Optional[int]				=       "coarse_acoustics"




@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkFineConfig",  model="BarkFineModel"),  "\n        n_codes_total (`int`, *optional*, defaults to 8):\n            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.\n        n_codes_given (`int`, *optional*, defaults to 1):\n            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics\n            sub-models.\n    Example:\n\n    ```python\n    >>> from transformers import BarkFineConfig, BarkFineModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkFineConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkFineModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```",  )
class  UpperCAmelCase_						(				_a):
       '''simple docstring'''
       __UpperCamelCase   :  int				=       "fine_acoustics"






       def __init__(  self			,       __SCREAMING_SNAKE_CASE=True			,       __SCREAMING_SNAKE_CASE=8			,       __SCREAMING_SNAKE_CASE=1			,       **__SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						Dict 						=		n_codes_total
              UpperCamelCase     :						str 						=		n_codes_given

              super().__init__(tie_word_embeddings=__SCREAMING_SNAKE_CASE			,       **__SCREAMING_SNAKE_CASE    )




class  UpperCAmelCase_						(				_a):
       '''simple docstring'''
       __UpperCamelCase   :  Optional[int]				=       "bark"
       __UpperCamelCase   :  List[str]				=       True






       def __init__(  self			,       __SCREAMING_SNAKE_CASE = None			,       __SCREAMING_SNAKE_CASE = None			,       __SCREAMING_SNAKE_CASE = None			,       __SCREAMING_SNAKE_CASE = None			,       __SCREAMING_SNAKE_CASE=0.02			,       **__SCREAMING_SNAKE_CASE			,       ):




              """simple docstring"""




              if semantic_config is None:
                     UpperCamelCase     :						Dict 						=		{}
                     logger.info('''semantic_config is None. initializing the semantic model with default values.'''    )

              if coarse_acoustics_config is None:
                     UpperCamelCase     :						Any 						=		{}
                     logger.info('''coarse_acoustics_config is None. initializing the coarse model with default values.'''    )

              if fine_acoustics_config is None:
                     UpperCamelCase     :						List[str] 						=		{}
                     logger.info('''fine_acoustics_config is None. initializing the fine model with default values.'''    )

              if codec_config is None:
                     UpperCamelCase     :						Any 						=		{}
                     logger.info('''codec_config is None. initializing the codec model with default values.'''    )

              UpperCamelCase     :						Optional[Any] 						=		BarkSemanticConfig(**__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						int 						=		BarkCoarseConfig(**__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Any 						=		BarkFineConfig(**__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Any 						=		AutoConfig.for_model(**__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						Dict 						=		initializer_range

              super().__init__(**__SCREAMING_SNAKE_CASE    )






       @classmethod
       def 			_lowercase (  cls			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       **__SCREAMING_SNAKE_CASE			,       ):




              """simple docstring"""




              return cls(
                  semantic_config=semantic_config.to_dict()			,       coarse_acoustics_config=coarse_acoustics_config.to_dict()			,       fine_acoustics_config=fine_acoustics_config.to_dict()			,       codec_config=codec_config.to_dict()			,       **__SCREAMING_SNAKE_CASE			,       )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Optional[int] 						=		copy.deepcopy(self.__dict__    )

              UpperCamelCase     :						Any 						=		self.semantic_config.to_dict()
              UpperCamelCase     :						Any 						=		self.coarse_acoustics_config.to_dict()
              UpperCamelCase     :						Tuple 						=		self.fine_acoustics_config.to_dict()
              UpperCamelCase     :						int 						=		self.codec_config.to_dict()

              UpperCamelCase     :						int 						=		self.__class__.model_type
              return output





import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
  import librosa  # noqa: E402

  __UpperCAmelCase			:			Union[str, Any]			      =					True
  __UpperCAmelCase			:			Optional[int]			      =					""
except Exception as e:
  __UpperCAmelCase			:			Union[str, Any]			      =					False
  __UpperCAmelCase			:			Tuple			      =					(
      f'''Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it.'''
  )


from PIL import Image  # noqa: E402



class  UpperCAmelCase_						(				_a,  _a):
       '''simple docstring'''
       __UpperCamelCase   :  Tuple				=       "mel_config.json"






       @register_to_config
       def __init__(  self			,       __SCREAMING_SNAKE_CASE = 256			,       __SCREAMING_SNAKE_CASE = 256			,       __SCREAMING_SNAKE_CASE = 22_050			,       __SCREAMING_SNAKE_CASE = 2_048			,       __SCREAMING_SNAKE_CASE = 512			,       __SCREAMING_SNAKE_CASE = 80			,       __SCREAMING_SNAKE_CASE = 32			,       ):




              """simple docstring"""




              UpperCamelCase     :						Union[str, Any] 						=		hop_length
              UpperCamelCase     :						List[str] 						=		sample_rate
              UpperCamelCase     :						List[Any] 						=		n_fft
              UpperCamelCase     :						List[str] 						=		top_db
              UpperCamelCase     :						Tuple 						=		n_iter
              self.set_resolution(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Any 						=		None

              if not _librosa_can_be_imported:
                     raise ValueError(_import_error    )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						Any 						=		x_res
              UpperCamelCase     :						Union[str, Any] 						=		y_res
              UpperCamelCase     :						Any 						=		self.y_res
              UpperCamelCase     :						Union[str, Any] 						=		self.x_res * self.hop_length - 1






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE = None			,       __SCREAMING_SNAKE_CASE = None    ):




              """simple docstring"""




              if audio_file is not None:
                     UpperCamelCase						,			UpperCamelCase     :						Tuple 						=		librosa.load(__SCREAMING_SNAKE_CASE			,       mono=__SCREAMING_SNAKE_CASE			,       sr=self.sr    )
              else:
                     UpperCamelCase     :						Tuple 						=		raw_audio

              # Pad with silence if necessary.
              if len(self.audio    ) < self.x_res * self.hop_length:
                     UpperCamelCase     :						List[str] 						=		np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio    ),)    )]    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              return len(self.audio    ) // self.slice_size






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE = 0    ):




              """simple docstring"""




              return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]






       def 			_lowercase (  self    ):




              """simple docstring"""




              return self.sr






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						List[str] 						=		librosa.feature.melspectrogram(
                  y=self.get_audio_slice(__SCREAMING_SNAKE_CASE    )			,       sr=self.sr			,       n_fft=self.n_fft			,       hop_length=self.hop_length			,       n_mels=self.n_mels    )
              UpperCamelCase     :						Optional[int] 						=		librosa.power_to_db(__SCREAMING_SNAKE_CASE			,       ref=np.max			,       top_db=self.top_db    )
              UpperCamelCase     :						Optional[int] 						=		(((log_S + self.top_db) * 255 / self.top_db).clip(0			,       255    ) + 0.5).astype(np.uinta    )
              UpperCamelCase     :						List[Any] 						=		Image.fromarray(__SCREAMING_SNAKE_CASE    )
              return image






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						int 						=		np.frombuffer(image.tobytes()			,       dtype='''uint8'''    ).reshape((image.height, image.width)    )
              UpperCamelCase     :						Tuple 						=		bytedata.astype('''float'''    ) * self.top_db / 255 - self.top_db
              UpperCamelCase     :						List[Any] 						=		librosa.db_to_power(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Optional[int] 						=		librosa.feature.inverse.mel_to_audio(
                  __SCREAMING_SNAKE_CASE			,       sr=self.sr			,       n_fft=self.n_fft			,       hop_length=self.hop_length			,       n_iter=self.n_iter    )
              return audio

