




'''simple docstring'''






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


_lowerCAmelCase				     = logging.get_logger(__name__)


_lowerCAmelCase				     = {
    '''suno/bark-small''': '''https://huggingface.co/suno/bark-small/resolve/main/config.json''',
    '''suno/bark''': '''https://huggingface.co/suno/bark/resolve/main/config.json''',
}

_lowerCAmelCase				     = '''\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  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				A				     =    'bark_module'
				A				     =    ['past_key_values']

				A				     =    {
				    'num_attention_heads': 'num_heads',
				    'num_hidden_layers': 'num_layers',
				    'vocab_size': 'input_vocab_size',
				    'window_size': 'block_size',
				}
				def __init__(self			,  _UpperCAmelCase=1_0_2_4			,  _UpperCAmelCase=1_0_0_4_8			,  _UpperCAmelCase=1_0_0_4_8			,  _UpperCAmelCase=1_2			,  _UpperCAmelCase=1_2			,  _UpperCAmelCase=7_6_8			,  _UpperCAmelCase=0.0			,  _UpperCAmelCase=True			,  _UpperCAmelCase=0.02			,  _UpperCAmelCase=True			,  **_UpperCAmelCase			,  )     ->	Any:
							__UpperCamelCase				:      Any									=       block_size
							__UpperCamelCase				:      Optional[Any]									=       input_vocab_size
							__UpperCamelCase				:      Tuple									=       output_vocab_size
							__UpperCamelCase				:      Optional[int]									=       num_layers
							__UpperCamelCase				:      Optional[Any]									=       num_heads
							__UpperCamelCase				:      Any									=       hidden_size
							__UpperCamelCase				:      Optional[int]									=       dropout
							__UpperCamelCase				:      Tuple									=       bias
							__UpperCamelCase				:      Optional[int]									=       use_cache
							__UpperCamelCase				:      List[str]									=       initializer_range

							super().__init__(**_UpperCAmelCase      )





				@classmethod
				def 	a_	(cls			,  _UpperCAmelCase			,  _UpperCAmelCase = None			,  _UpperCAmelCase = False			,  _UpperCAmelCase = False			,  _UpperCAmelCase = None			,  _UpperCAmelCase = "main"			,  **_UpperCAmelCase			,  )     ->	Union[str, Any]:
							__UpperCamelCase				:      Optional[Any]									=       cache_dir
							__UpperCamelCase				:      Dict									=       force_download
							__UpperCamelCase				:      int									=       local_files_only
							__UpperCamelCase				:      Tuple									=       revision

							cls._set_token_in_kwargs(_UpperCAmelCase			,  _UpperCAmelCase      )

							__UpperCamelCase				:      Any									=       cls.get_config_dict(_UpperCAmelCase			,  **_UpperCAmelCase      )

							# get the config dict if we are loading from Bark
							if config_dict.get("model_type"      ) == "bark":
										__UpperCamelCase				:      Dict									=       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(_UpperCAmelCase			,  **_UpperCAmelCase      )







@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  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				A				     =    '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  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				A				     =    '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  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				A				     =    'fine_acoustics'
				def __init__(self			,  _UpperCAmelCase=True			,  _UpperCAmelCase=8			,  _UpperCAmelCase=1			,  **_UpperCAmelCase      )     ->	List[str]:
							__UpperCamelCase				:      Optional[Any]									=       n_codes_total
							__UpperCamelCase				:      Any									=       n_codes_given

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







class  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				A				     =    'bark'
				A				     =    True
				def __init__(self			,  _UpperCAmelCase = None			,  _UpperCAmelCase = None			,  _UpperCAmelCase = None			,  _UpperCAmelCase = None			,  _UpperCAmelCase=0.02			,  **_UpperCAmelCase			,  )     ->	List[Any]:






							if semantic_config is None:
										__UpperCamelCase				:      str									=       {}
										logger.info("semantic_config is None. initializing the semantic model with default values."      )

							if coarse_acoustics_config is None:
										__UpperCamelCase				:      int									=       {}
										logger.info("coarse_acoustics_config is None. initializing the coarse model with default values."      )

							if fine_acoustics_config is None:
										__UpperCamelCase				:      Optional[int]									=       {}
										logger.info("fine_acoustics_config is None. initializing the fine model with default values."      )

							if codec_config is None:
										__UpperCamelCase				:      Optional[int]									=       {}
										logger.info("codec_config is None. initializing the codec model with default values."      )

							__UpperCamelCase				:      Optional[int]									=       BarkSemanticConfig(**_UpperCAmelCase      )
							__UpperCamelCase				:      Any									=       BarkCoarseConfig(**_UpperCAmelCase      )
							__UpperCamelCase				:      List[Any]									=       BarkFineConfig(**_UpperCAmelCase      )
							__UpperCamelCase				:      Any									=       AutoConfig.for_model(**_UpperCAmelCase      )

							__UpperCamelCase				:      List[str]									=       initializer_range

							super().__init__(**_UpperCAmelCase      )
				@classmethod
				def 	a_	(cls			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  **_UpperCAmelCase			,  )     ->	Union[str, Any]:
							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()			,  **_UpperCAmelCase			,  )





				def 	a_	(self      )     ->	List[str]:
							__UpperCamelCase				:      Union[str, Any]									=       copy.deepcopy(self.__dict__      )

							__UpperCamelCase				:      List[Any]									=       self.semantic_config.to_dict()
							__UpperCamelCase				:      Optional[int]									=       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









'''simple docstring'''






from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


_lowerCAmelCase				     = logging.get_logger(__name__)

_lowerCAmelCase				     = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCAmelCase				     = {
    '''vocab_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/vocab.json'''},
    '''merges_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/merges.txt'''},
    '''tokenizer_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/tokenizer.json'''},
}

_lowerCAmelCase				     = {
    '''openai-gpt''': 512,
}



class  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				A				     =    VOCAB_FILES_NAMES
				A				     =    PRETRAINED_VOCAB_FILES_MAP
				A				     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				A				     =    ["input_ids", "attention_mask"]
				A				     =    OpenAIGPTTokenizer
				def __init__(self			,  _UpperCAmelCase=None			,  _UpperCAmelCase=None			,  _UpperCAmelCase=None			,  _UpperCAmelCase="<unk>"			,  **_UpperCAmelCase      )     ->	str:
							super().__init__(_UpperCAmelCase			,  _UpperCAmelCase			,  tokenizer_file=_UpperCAmelCase			,  unk_token=_UpperCAmelCase			,  **_UpperCAmelCase      )
				@property
				def 	a_	(self      )     ->	Dict:
							return True





				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase = None      )     ->	Tuple[str]:
							__UpperCamelCase				:      Optional[Any]									=       self._tokenizer.model.save(_UpperCAmelCase			,  name=_UpperCAmelCase      )
							return tuple(_UpperCAmelCase      )



