






'''simple docstring'''





import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


_UpperCAmelCase				:	Optional[Any]    						=    logging.get_logger(__name__)


class 		lowerCAmelCase_							(     snake_case__   ):
     __lowerCamelCase					:						Dict     =    'semantic'





     def __init__(  self       ,   _UpperCAmelCase=10000       ,   _UpperCAmelCase=True       ,   _UpperCAmelCase=768       ,   _UpperCAmelCase=False       ,   _UpperCAmelCase=False       ,   _UpperCAmelCase=False       ,   _UpperCAmelCase=False       ,   _UpperCAmelCase=0.7       ,   _UpperCAmelCase=True       ,   _UpperCAmelCase=10048       ,   _UpperCAmelCase=129595       ,   _UpperCAmelCase=129599       ,   _UpperCAmelCase=10000       ,   _UpperCAmelCase=256       ,   _UpperCAmelCase=49.9       ,   **_UpperCAmelCase       ,   )		->							int:
        super().__init__(
            temperature=_UpperCAmelCase       ,   do_sample=_UpperCAmelCase       ,   eos_token_id=_UpperCAmelCase       ,   renormalize_logits=_UpperCAmelCase       ,   max_new_tokens=_UpperCAmelCase       ,   output_scores=_UpperCAmelCase       ,   return_dict_in_generate=_UpperCAmelCase       ,   output_hidden_states=_UpperCAmelCase       ,   output_attentions=_UpperCAmelCase       ,   **_UpperCAmelCase       ,   )

        lowerCamelCase											=text_encoding_offset
        lowerCamelCase											=text_pad_token
        lowerCamelCase											=eos_token_id
        lowerCamelCase											=semantic_infer_token
        lowerCamelCase											=semantic_vocab_size
        lowerCamelCase											=max_input_semantic_length
        lowerCamelCase											=semantic_rate_hz




class 		lowerCAmelCase_							(     snake_case__   ):
     __lowerCamelCase					:						int     =    'coarse_acoustics'





     def __init__(  self       ,   _UpperCAmelCase=True       ,   _UpperCAmelCase=False       ,   _UpperCAmelCase=False       ,   _UpperCAmelCase=False       ,   _UpperCAmelCase=False       ,   _UpperCAmelCase=0.7       ,   _UpperCAmelCase=True       ,   _UpperCAmelCase=12048       ,   _UpperCAmelCase=75       ,   _UpperCAmelCase=2       ,   _UpperCAmelCase=12050       ,   _UpperCAmelCase=256       ,   _UpperCAmelCase = 630       ,   _UpperCAmelCase = 60       ,   **_UpperCAmelCase       ,   )		->							List[Any]:
        super().__init__(
            temperature=_UpperCAmelCase       ,   do_sample=_UpperCAmelCase       ,   renormalize_logits=_UpperCAmelCase       ,   output_scores=_UpperCAmelCase       ,   return_dict_in_generate=_UpperCAmelCase       ,   output_hidden_states=_UpperCAmelCase       ,   output_attentions=_UpperCAmelCase       ,   **_UpperCAmelCase       ,   )

        lowerCamelCase											=coarse_semantic_pad_token
        lowerCamelCase											=coarse_rate_hz
        lowerCamelCase											=n_coarse_codebooks
        lowerCamelCase											=coarse_infer_token
        lowerCamelCase											=max_coarse_input_length
        lowerCamelCase											=max_coarse_history
        lowerCamelCase											=sliding_window_len




class 		lowerCAmelCase_							(     snake_case__   ):
     __lowerCamelCase					:						Optional[int]     =    'fine_acoustics'





     def __init__(  self       ,   _UpperCAmelCase=0.5       ,   _UpperCAmelCase=512       ,   _UpperCAmelCase=1024       ,   _UpperCAmelCase=8       ,   **_UpperCAmelCase       ,   )		->							Any:
        super().__init__(temperature=_UpperCAmelCase     )

        lowerCamelCase											=max_fine_history_length
        lowerCamelCase											=max_fine_input_length
        lowerCamelCase											=n_fine_codebooks




class 		lowerCAmelCase_							(     snake_case__   ):
     __lowerCamelCase					:						Dict     =    'bark'
     __lowerCamelCase					:						Union[str, Any]     =    True





     def __init__(  self       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase=24000       ,   _UpperCAmelCase=1024       ,   **_UpperCAmelCase       ,   )		->							Tuple:
        if semantic_config is None:
           lowerCamelCase											={}
           logger.info("""semantic_config is None. initializing the semantic model with default values."""     )

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

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

        lowerCamelCase											=BarkSemanticGenerationConfig(**_UpperCAmelCase     )
        lowerCamelCase											=BarkCoarseGenerationConfig(**_UpperCAmelCase     )
        lowerCamelCase											=BarkFineGenerationConfig(**_UpperCAmelCase     )

        lowerCamelCase											=sample_rate
        lowerCamelCase											=codebook_size





     @classmethod
     def 	lowerCamelCase       (  cls       ,   _UpperCAmelCase       ,   _UpperCAmelCase       ,   _UpperCAmelCase       ,   **_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()       ,   **_UpperCAmelCase       ,   )





     def 	lowerCamelCase       (  self     )		->							Optional[int]:
        lowerCamelCase											=copy.deepcopy(self.__dict__     )

        lowerCamelCase											=self.semantic_config.to_dict()
        lowerCamelCase											=self.coarse_acoustics_config.to_dict()
        lowerCamelCase											=self.fine_acoustics_config.to_dict()

        lowerCamelCase											=self.__class__.model_type
        return output







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_UpperCAmelCase				:	List[str]    						=    {
    'configuration_unispeech_sat': ['UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechSatConfig'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase				:	Optional[Any]    						=    [
       'UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'UniSpeechSatForAudioFrameClassification',
       'UniSpeechSatForCTC',
       'UniSpeechSatForPreTraining',
       'UniSpeechSatForSequenceClassification',
       'UniSpeechSatForXVector',
       'UniSpeechSatModel',
       'UniSpeechSatPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_unispeech_sat import (
          UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
          UniSpeechSatForAudioFrameClassification,
          UniSpeechSatForCTC,
          UniSpeechSatForPreTraining,
          UniSpeechSatForSequenceClassification,
          UniSpeechSatForXVector,
          UniSpeechSatModel,
          UniSpeechSatPreTrainedModel,
      )

else:
   import sys

   _UpperCAmelCase				:	Optional[Any]    						=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)