




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     (   _A			):

     '''simple docstring'''





     lowerCAmelCase				:       List[Any]   		=  'bark_module'
     lowerCAmelCase				:       Any   		=  ['past_key_values']

     lowerCAmelCase				:       int   		=  {
         'num_attention_heads': 'num_heads',
         'num_hidden_layers': 'num_layers',
         'vocab_size': 'input_vocab_size',
         'window_size': 'block_size',
     }
     def __init__(	self   :			Dict					,    __snake_case   :			str=10_24					,    __snake_case   :			Dict=1_00_48					,    __snake_case   :			str=1_00_48					,    __snake_case   :			int=12					,    __snake_case   :			str=12					,    __snake_case   :			Dict=7_68					,    __snake_case   :			List[str]=0.0					,    __snake_case   :			Dict=True					,    __snake_case   :			int=0.02					,    __snake_case   :			Dict=True					,    **__snake_case   :			Tuple					,    ):
         UpperCAmelCase_							 =     block_size
         UpperCAmelCase_							 =     input_vocab_size
         UpperCAmelCase_							 =     output_vocab_size
         UpperCAmelCase_							 =     num_layers
         UpperCAmelCase_							 =     num_heads
         UpperCAmelCase_							 =     hidden_size
         UpperCAmelCase_							 =     dropout
         UpperCAmelCase_							 =     bias
         UpperCAmelCase_							 =     use_cache
         UpperCAmelCase_							 =     initializer_range

         super().__init__(**__snake_case				)




     @classmethod
     def   lowerCamelCase_   (	cls   :			Union[str, Any]					,    __snake_case   :			Union[str, os.PathLike]					,    __snake_case   :			Optional[Union[str, os.PathLike]] = None					,    __snake_case   :			bool = False					,    __snake_case   :			bool = False					,    __snake_case   :			Optional[Union[str, bool]] = None					,    __snake_case   :			str = "main"					,    **__snake_case   :			Tuple					,    ):
         UpperCAmelCase_							 =     cache_dir
         UpperCAmelCase_							 =     force_download
         UpperCAmelCase_							 =     local_files_only
         UpperCAmelCase_							 =     revision

         cls._set_token_in_kwargs(__snake_case					,    __snake_case				)

         UpperCAmelCase_ ,							UpperCAmelCase_							 =     cls.get_config_dict(__snake_case					,    **__snake_case				)

         # get the config dict if we are loading from Bark
         if config_dict.get('''model_type'''				) == "bark":
             UpperCAmelCase_							 =     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(__snake_case					,    **__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 			a     (   _A			):

     '''simple docstring'''





     lowerCAmelCase				:       int   		=  '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			):

     '''simple docstring'''





     lowerCAmelCase				:       Union[str, Any]   		=  '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			):

     '''simple docstring'''





     lowerCAmelCase				:       str   		=  'fine_acoustics'
     def __init__(	self   :			List[Any]					,    __snake_case   :			Dict=True					,    __snake_case   :			Any=8					,    __snake_case   :			Any=1					,    **__snake_case   :			Optional[int]				):
         UpperCAmelCase_							 =     n_codes_total
         UpperCAmelCase_							 =     n_codes_given

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



class 			a     (   _A			):

     '''simple docstring'''





     lowerCAmelCase				:       Optional[int]   		=  'bark'
     lowerCAmelCase				:       Dict   		=  True
     def __init__(	self   :			Tuple					,    __snake_case   :			Dict = None					,    __snake_case   :			Dict = None					,    __snake_case   :			Dict = None					,    __snake_case   :			Dict = None					,    __snake_case   :			List[str]=0.02					,    **__snake_case   :			int					,    ):
         if semantic_config is None:
             UpperCAmelCase_							 =     {}
             logger.info('''semantic_config is None. initializing the semantic model with default values.'''				)

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

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

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

         UpperCAmelCase_							 =     BarkSemanticConfig(**__snake_case				)
         UpperCAmelCase_							 =     BarkCoarseConfig(**__snake_case				)
         UpperCAmelCase_							 =     BarkFineConfig(**__snake_case				)
         UpperCAmelCase_							 =     AutoConfig.for_model(**__snake_case				)

         UpperCAmelCase_							 =     initializer_range

         super().__init__(**__snake_case				)
     @classmethod
     def   lowerCamelCase_   (	cls   :			Union[str, Any]					,    __snake_case   :			BarkSemanticConfig					,    __snake_case   :			BarkCoarseConfig					,    __snake_case   :			BarkFineConfig					,    __snake_case   :			AutoConfig					,    **__snake_case   :			str					,    ):
         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()					,    **__snake_case					,    )




     def   lowerCamelCase_   (	self   :			List[str]				):
         UpperCAmelCase_							 =     copy.deepcopy(self.__dict__				)

         UpperCAmelCase_							 =     self.semantic_config.to_dict()
         UpperCAmelCase_							 =     self.coarse_acoustics_config.to_dict()
         UpperCAmelCase_							 =     self.fine_acoustics_config.to_dict()
         UpperCAmelCase_							 =     self.codec_config.to_dict()

         UpperCAmelCase_							 =     self.__class__.model_type
         return output











from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


_lowerCamelCase			 =						datasets.utils.logging.get_logger(__name__)



class 			a     (   folder_based_builder.FolderBasedBuilderConfig			):

     '''simple docstring'''





     lowerCAmelCase				:       bool   		=  None
     lowerCAmelCase				:       bool   		=  None



class 			a     (   folder_based_builder.FolderBasedBuilder			):

     '''simple docstring'''





     lowerCAmelCase				:       List[Any]   		=  datasets.Image()
     lowerCAmelCase				:       List[Any]   		=  'image'
     lowerCAmelCase				:       List[Any]   		=  ImageFolderConfig
     lowerCAmelCase				:       List[str]  # definition at the bottom of the script
     lowerCAmelCase				:       Dict   		=  ImageClassification(image_column='image'	,   label_column='label'			)


_lowerCamelCase			 =						[
    '.blp',
    '.bmp',
    '.dib',
    '.bufr',
    '.cur',
    '.pcx',
    '.dcx',
    '.dds',
    '.ps',
    '.eps',
    '.fit',
    '.fits',
    '.fli',
    '.flc',
    '.ftc',
    '.ftu',
    '.gbr',
    '.gif',
    '.grib',
    '.h5',
    '.hdf',
    '.png',
    '.apng',
    '.jp2',
    '.j2k',
    '.jpc',
    '.jpf',
    '.jpx',
    '.j2c',
    '.icns',
    '.ico',
    '.im',
    '.iim',
    '.tif',
    '.tiff',
    '.jfif',
    '.jpe',
    '.jpg',
    '.jpeg',
    '.mpg',
    '.mpeg',
    '.msp',
    '.pcd',
    '.pxr',
    '.pbm',
    '.pgm',
    '.ppm',
    '.pnm',
    '.psd',
    '.bw',
    '.rgb',
    '.rgba',
    '.sgi',
    '.ras',
    '.tga',
    '.icb',
    '.vda',
    '.vst',
    '.webp',
    '.wmf',
    '.emf',
    '.xbm',
    '.xpm',
]
_lowerCamelCase			 =						IMAGE_EXTENSIONS





