





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						=      logging.get_logger(__name__)


__UpperCAmelCase						=      {
    """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						=      """
    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model
    according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)
    architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        block_size (`int`, *optional*, defaults to 1024):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        input_vocab_size (`int`, *optional*, defaults to 10_048):
            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with
            regards to the chosen sub-model.
        output_vocab_size (`int`, *optional*, defaults to 10_048):
            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented
            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought
            with regards to the chosen sub-model.
        num_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the given sub-model.
        num_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer architecture.
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the \"intermediate\" (often named feed-forward) layer in the architecture.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        bias (`bool`, *optional*, defaults to `True`):
            Whether or not to use bias in the linear layers and layer norm layers.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
"""

class 		__a							(	_a      ):
       __snake_case						:   Optional[int]      =		"""bark_module"""
       __snake_case						:   Optional[Any]      =		["""past_key_values"""]

       __snake_case						:   Optional[Any]      =		{
           """num_attention_heads""": """num_heads""",
           """num_hidden_layers""": """num_layers""",
           """vocab_size""": """input_vocab_size""",
           """window_size""": """block_size""",
       }



       def __init__(     self	:   Tuple					,			UpperCAmelCase	:   Optional[Any]=10_24					,			UpperCAmelCase	:   Optional[Any]=1_00_48					,			UpperCAmelCase	:   List[str]=1_00_48					,			UpperCAmelCase	:   List[Any]=12					,			UpperCAmelCase	:   Dict=12					,			UpperCAmelCase	:   str=7_68					,			UpperCAmelCase	:   str=0.0					,			UpperCAmelCase	:   int=True					,			UpperCAmelCase	:   Optional[int]=0.02					,			UpperCAmelCase	:   int=True					,			**UpperCAmelCase	:   Optional[Any]					,			):
           lowerCAmelCase_					:	Union[str, Any]						=   block_size
           lowerCAmelCase_					:	Tuple						=   input_vocab_size
           lowerCAmelCase_					:	List[Any]						=   output_vocab_size
           lowerCAmelCase_					:	Union[str, Any]						=   num_layers
           lowerCAmelCase_					:	Any						=   num_heads
           lowerCAmelCase_					:	List[Any]						=   hidden_size
           lowerCAmelCase_					:	List[str]						=   dropout
           lowerCAmelCase_					:	Tuple						=   bias
           lowerCAmelCase_					:	List[str]						=   use_cache
           lowerCAmelCase_					:	Optional[Any]						=   initializer_range

           super().__init__(**lowerCamelCase__			)



       @classmethod
       def  A							(     cls	:   Any					,			UpperCAmelCase	:   Optional[int]					,			UpperCAmelCase	:   Any = None					,			UpperCAmelCase	:   Union[str, Any] = False					,			UpperCAmelCase	:   List[Any] = False					,			UpperCAmelCase	:   Union[str, Any] = None					,			UpperCAmelCase	:   Any = "main"					,			**UpperCAmelCase	:   int					,			):
           lowerCAmelCase_					:	List[str]						=   cache_dir
           lowerCAmelCase_					:	str						=   force_download
           lowerCAmelCase_					:	List[str]						=   local_files_only
           lowerCAmelCase_					:	List[str]						=   revision

           cls._set_token_in_kwargs(lowerCamelCase__					,			lowerCamelCase__			)

           lowerCAmelCase_					:	Tuple						=   cls.get_config_dict(lowerCamelCase__					,			**lowerCamelCase__			)

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






@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							(	_a      ):
       __snake_case						:   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 		__a							(	_a      ):
       __snake_case						:   Dict      =		"""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							(	_a      ):
       __snake_case						:   str      =		"""fine_acoustics"""



       def __init__(     self	:   Optional[Any]					,			UpperCAmelCase	:   int=True					,			UpperCAmelCase	:   Any=8					,			UpperCAmelCase	:   int=1					,			**UpperCAmelCase	:   List[str]			):
           lowerCAmelCase_					:	List[str]						=   n_codes_total
           lowerCAmelCase_					:	Dict						=   n_codes_given

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






class 		__a							(	_a      ):
       __snake_case						:   Union[str, Any]      =		"""bark"""
       __snake_case						:   Union[str, Any]      =		True



       def __init__(     self	:   Optional[Any]					,			UpperCAmelCase	:   Optional[Any] = None					,			UpperCAmelCase	:   Tuple = None					,			UpperCAmelCase	:   Dict = None					,			UpperCAmelCase	:   List[str] = None					,			UpperCAmelCase	:   Union[str, Any]=0.02					,			**UpperCAmelCase	:   Tuple					,			):
           if semantic_config is None:
               lowerCAmelCase_					:	Union[str, Any]						=   {}
               logger.info("""semantic_config is None. initializing the semantic model with default values."""			)

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

           if fine_acoustics_config is None:
               lowerCAmelCase_					:	Tuple						=   {}
               logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""			)

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

           lowerCAmelCase_					:	Optional[Any]						=   BarkSemanticConfig(**lowerCamelCase__			)
           lowerCAmelCase_					:	int						=   BarkCoarseConfig(**lowerCamelCase__			)
           lowerCAmelCase_					:	int						=   BarkFineConfig(**lowerCamelCase__			)
           lowerCAmelCase_					:	Union[str, Any]						=   AutoConfig.for_model(**lowerCamelCase__			)

           lowerCAmelCase_					:	List[Any]						=   initializer_range

           super().__init__(**lowerCamelCase__			)



       @classmethod
       def  A							(     cls	:   Optional[Any]					,			UpperCAmelCase	:   int					,			UpperCAmelCase	:   Union[str, Any]					,			UpperCAmelCase	:   List[str]					,			UpperCAmelCase	:   Tuple					,			**UpperCAmelCase	:   Dict					,			):
           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()					,			**lowerCamelCase__					,			)



       def  A							(     self	:   Optional[Any]			):
           lowerCAmelCase_					:	List[str]						=   copy.deepcopy(self.__dict__			)

           lowerCAmelCase_					:	List[str]						=   self.semantic_config.to_dict()
           lowerCAmelCase_					:	Optional[Any]						=   self.coarse_acoustics_config.to_dict()
           lowerCAmelCase_					:	Any						=   self.fine_acoustics_config.to_dict()
           lowerCAmelCase_					:	List[str]						=   self.codec_config.to_dict()

           lowerCAmelCase_					:	Union[str, Any]						=   self.__class__.model_type
           return output









import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property

class 		__a							(	unittest.TestCase      ):



       @cached_property
       def  A							(     self	:   Optional[int]			):
           return BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""			)



       @cached_property
       def  A							(     self	:   Optional[int]			):
           return BlenderbotTokenizerFast.from_pretrained("""facebook/blenderbot-3B"""			)



       def  A							(     self	:   Optional[Any]			):
           lowerCAmelCase_					:	List[str]						=   self.tokenizer_ab
           lowerCAmelCase_					:	Any						=   """ I am a small frog."""
           lowerCAmelCase_					:	Union[str, Any]						=   tok([src_text]					,			padding=UpperCAmelCase					,			truncation=UpperCAmelCase			)["""input_ids"""]
           lowerCAmelCase_					:	int						=   tok.batch_decode(UpperCAmelCase					,			skip_special_tokens=UpperCAmelCase					,			clean_up_tokenization_spaces=UpperCAmelCase			)[0]
           assert src_text == decoded



       def  A							(     self	:   List[Any]			):
           lowerCAmelCase_					:	Dict						=   self.rust_tokenizer_ab
           lowerCAmelCase_					:	str						=   """ I am a small frog."""
           lowerCAmelCase_					:	Tuple						=   tok([src_text]					,			padding=UpperCAmelCase					,			truncation=UpperCAmelCase			)["""input_ids"""]
           lowerCAmelCase_					:	int						=   tok.batch_decode(UpperCAmelCase					,			skip_special_tokens=UpperCAmelCase					,			clean_up_tokenization_spaces=UpperCAmelCase			)[0]
           assert src_text == decoded



       def  A							(     self	:   str			):
           assert self.tokenizer_ab.add_prefix_space
           assert self.tokenizer_ab([""" Sam""", """Sam"""]			).input_ids == [[55_02, 2], [55_02, 2]]



       def  A							(     self	:   List[str]			):
           assert self.rust_tokenizer_ab.add_prefix_space
           assert self.rust_tokenizer_ab([""" Sam""", """Sam"""]			).input_ids == [[55_02, 2], [55_02, 2]]


