'''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


__snake_case       :   Any  		=    logging.get_logger(__name__)


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

__snake_case       :   Optional[int]  		=    '''
    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 							lowercase_    (     _A						):
    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:





     """simple docstring"""

     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__(**UpperCamelCase__				)







    @classmethod
    def   lowerCamelCase_    (	cls       ,     UpperCamelCase__       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = False       ,     UpperCamelCase__ = False       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = "main"       ,     **UpperCamelCase__       ,     )				->       "PretrainedConfig":





     """simple docstring"""

     UpperCAmelCase_				       =					cache_dir
     UpperCAmelCase_				       =					force_download
     UpperCAmelCase_				       =					local_files_only
     UpperCAmelCase_				       =					revision

     cls._set_token_in_kwargs(UpperCamelCase__       ,     UpperCamelCase__				)

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

     # 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(UpperCamelCase__       ,     **UpperCamelCase__				)


@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkSemanticConfig""" ,  model="""BarkSemanticModel"""						) ,  """
    Example:

    ```python
    >>> from transformers import BarkSemanticConfig, BarkSemanticModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkSemanticConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkSemanticModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```""" ,  )
class 							lowercase_    (     _A						):
    a_             =   """semantic"""


@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkCoarseConfig""" ,  model="""BarkCoarseModel"""						) ,  """
    Example:

    ```python
    >>> from transformers import BarkCoarseConfig, BarkCoarseModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkCoarseConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkCoarseModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```""" ,  )
class 							lowercase_    (     _A						):
    a_             =   """coarse_acoustics"""


@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkFineConfig""" ,  model="""BarkFineModel"""						) ,  """
        n_codes_total (`int`, *optional*, defaults to 8):
            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.
        n_codes_given (`int`, *optional*, defaults to 1):
            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics
            sub-models.
    Example:

    ```python
    >>> from transformers import BarkFineConfig, BarkFineModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkFineConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkFineModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```""" ,  )
class 							lowercase_    (     _A						):
    a_             =   """fine_acoustics"""







    def __init__(	self       ,     UpperCamelCase__=True       ,     UpperCamelCase__=8       ,     UpperCamelCase__=1       ,     **UpperCamelCase__				)				->       List[str]:





     """simple docstring"""

     UpperCAmelCase_				       =					n_codes_total
     UpperCAmelCase_				       =					n_codes_given

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


class 							lowercase_    (     _A						):
    a_             =   """bark"""
    a_             =   True







    def __init__(	self       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__=0.02       ,     **UpperCamelCase__       ,     )				->       Optional[int]:





     """simple docstring"""

     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(**UpperCamelCase__				)
     UpperCAmelCase_				       =					BarkCoarseConfig(**UpperCamelCase__				)
     UpperCAmelCase_				       =					BarkFineConfig(**UpperCamelCase__				)
     UpperCAmelCase_				       =					AutoConfig.for_model(**UpperCamelCase__				)

     UpperCAmelCase_				       =					initializer_range

     super().__init__(**UpperCamelCase__				)







    @classmethod
    def   lowerCamelCase_    (	cls       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     **UpperCamelCase__       ,     )				->       Dict:





     """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()       ,     **UpperCamelCase__       ,     )







    def   lowerCamelCase_    (	self				)				->       Any:





     """simple docstring"""

     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

'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__snake_case       :   int  		=    {
    '''configuration_videomae''': ['''VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VideoMAEConfig'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __snake_case       :   Optional[Any]  		=    [
          '''VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''VideoMAEForPreTraining''',
          '''VideoMAEModel''',
          '''VideoMAEPreTrainedModel''',
          '''VideoMAEForVideoClassification''',
      ]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __snake_case       :   str  		=    ['''VideoMAEFeatureExtractor''']
      __snake_case       :   Any  		=    ['''VideoMAEImageProcessor''']

if TYPE_CHECKING:
      from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_videomae import (
                VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST,
                VideoMAEForPreTraining,
                VideoMAEForVideoClassification,
                VideoMAEModel,
                VideoMAEPreTrainedModel,
            )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_videomae import VideoMAEFeatureExtractor
            from .image_processing_videomae import VideoMAEImageProcessor

else:
      import sys

      __snake_case       :   List[str]  		=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
