




'''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


UpperCAmelCase_				:     List[str]     			=						logging.get_logger(__name__)


UpperCAmelCase_				:     Dict     			=						{
    '''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_				:     List[Any]     			=						'''
    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 			lowerCAmelCase      (       __lowerCAmelCase):
       __lowercase     :					str       				=	'''bark_module'''
       __lowercase     :					Dict       				=	['''past_key_values''']

       __lowercase     :					Optional[Any]       				=	{
           '''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=1024			,     __SCREAMING_SNAKE_CASE=1_0048			,     __SCREAMING_SNAKE_CASE=1_0048			,     __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			,     )					->     Any:



            '''simple docstring'''




            __snake_case			    =     block_size
            __snake_case			    =     input_vocab_size
            __snake_case			    =     output_vocab_size
            __snake_case			    =     num_layers
            __snake_case			    =     num_heads
            __snake_case			    =     hidden_size
            __snake_case			    =     dropout
            __snake_case			    =     bias
            __snake_case			    =     use_cache
            __snake_case			    =     initializer_range

            super().__init__(**__SCREAMING_SNAKE_CASE     )




       @classmethod
       def 					lowerCAmelCase					(      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			,     )					->     "PretrainedConfig":



            '''simple docstring'''




            __snake_case			    =     cache_dir
            __snake_case			    =     force_download
            __snake_case			    =     local_files_only
            __snake_case			    =     revision

            cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )

            __snake_case    ,			__snake_case			    =     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":
                 __snake_case			    =     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''') ,      '''
    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 			lowerCAmelCase      (       __lowerCAmelCase):
       __lowercase     :					int       				=	'''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 			lowerCAmelCase      (       __lowerCAmelCase):
       __lowercase     :					List[str]       				=	'''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 			lowerCAmelCase      (       __lowerCAmelCase):
       __lowercase     :					Any       				=	'''fine_acoustics'''


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



            '''simple docstring'''




            __snake_case			    =     n_codes_total
            __snake_case			    =     n_codes_given

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





class 			lowerCAmelCase      (       __lowerCAmelCase):
       __lowercase     :					Optional[int]       				=	'''bark'''
       __lowercase     :					Any       				=	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			,     )					->     Dict:



            '''simple docstring'''




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

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

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

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

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

            __snake_case			    =     initializer_range

            super().__init__(**__SCREAMING_SNAKE_CASE     )


       @classmethod
       def 					lowerCAmelCase					(      cls			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     **__SCREAMING_SNAKE_CASE			,     )					->     Tuple:



            '''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 					lowerCAmelCase					(      self     )					->     List[str]:



            '''simple docstring'''




            __snake_case			    =     copy.deepcopy(self.__dict__     )

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

            __snake_case			    =     self.__class__.model_type
            return output












'''simple docstring'''



from typing import TYPE_CHECKING

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


UpperCAmelCase_				:     Dict     			=						{
    '''configuration_markuplm''': ['''MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MarkupLMConfig'''],
    '''feature_extraction_markuplm''': ['''MarkupLMFeatureExtractor'''],
    '''processing_markuplm''': ['''MarkupLMProcessor'''],
    '''tokenization_markuplm''': ['''MarkupLMTokenizer'''],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_				:     List[Any]     			=						['''MarkupLMTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_				:     int     			=						[
         '''MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''MarkupLMForQuestionAnswering''',
         '''MarkupLMForSequenceClassification''',
         '''MarkupLMForTokenClassification''',
         '''MarkupLMModel''',
         '''MarkupLMPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
     from .feature_extraction_markuplm import MarkupLMFeatureExtractor
     from .processing_markuplm import MarkupLMProcessor
     from .tokenization_markuplm import MarkupLMTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_markuplm_fast import MarkupLMTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_markuplm import (
              MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              MarkupLMForQuestionAnswering,
              MarkupLMForSequenceClassification,
              MarkupLMForTokenClassification,
              MarkupLMModel,
              MarkupLMPreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase_				:     Dict     			=						_LazyModule(__name__, globals()['''__file__'''], _import_structure)






