code: 
  
  
  
  
  
  
  import copy
  from typing import Dict
  
  from ...generation.configuration_utils import GenerationConfig
  from ...utils import logging
  
  
  __snake_case							      =						logging.get_logger(__name__)
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      lowercase				):
  
  
  
        """simple docstring"""
        UpperCamelCase_       :      Dict     ='semantic'
        def __init__(       self					,       SCREAMING_SNAKE_CASE_=1_0000					,       SCREAMING_SNAKE_CASE_=True					,       SCREAMING_SNAKE_CASE_=768					,       SCREAMING_SNAKE_CASE_=False					,       SCREAMING_SNAKE_CASE_=False					,       SCREAMING_SNAKE_CASE_=False					,       SCREAMING_SNAKE_CASE_=False					,       SCREAMING_SNAKE_CASE_=0.7					,       SCREAMING_SNAKE_CASE_=True					,       SCREAMING_SNAKE_CASE_=1_0048					,       SCREAMING_SNAKE_CASE_=12_9595					,       SCREAMING_SNAKE_CASE_=12_9599					,       SCREAMING_SNAKE_CASE_=1_0000					,       SCREAMING_SNAKE_CASE_=256					,       SCREAMING_SNAKE_CASE_=49.9					,       **SCREAMING_SNAKE_CASE_					,       )       ->   Any:
            super().__init__(
                temperature=SCREAMING_SNAKE_CASE_					,       do_sample=SCREAMING_SNAKE_CASE_					,       eos_token_id=SCREAMING_SNAKE_CASE_					,       renormalize_logits=SCREAMING_SNAKE_CASE_					,       max_new_tokens=SCREAMING_SNAKE_CASE_					,       output_scores=SCREAMING_SNAKE_CASE_					,       return_dict_in_generate=SCREAMING_SNAKE_CASE_					,       output_hidden_states=SCREAMING_SNAKE_CASE_					,       output_attentions=SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_					,       )
  
            UpperCamelCase :Dict										=     text_encoding_offset
            UpperCamelCase :Union[str, Any]										=     text_pad_token
            UpperCamelCase :str										=     eos_token_id
            UpperCamelCase :Tuple										=     semantic_infer_token
            UpperCamelCase :Tuple										=     semantic_vocab_size
            UpperCamelCase :int										=     max_input_semantic_length
            UpperCamelCase :List[Any]										=     semantic_rate_hz
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      lowercase				):
  
  
  
        """simple docstring"""
        UpperCamelCase_       :      Any     ='coarse_acoustics'
        def __init__(       self					,       SCREAMING_SNAKE_CASE_=True					,       SCREAMING_SNAKE_CASE_=False					,       SCREAMING_SNAKE_CASE_=False					,       SCREAMING_SNAKE_CASE_=False					,       SCREAMING_SNAKE_CASE_=False					,       SCREAMING_SNAKE_CASE_=0.7					,       SCREAMING_SNAKE_CASE_=True					,       SCREAMING_SNAKE_CASE_=1_2048					,       SCREAMING_SNAKE_CASE_=75					,       SCREAMING_SNAKE_CASE_=2					,       SCREAMING_SNAKE_CASE_=1_2050					,       SCREAMING_SNAKE_CASE_=256					,       SCREAMING_SNAKE_CASE_ = 630					,       SCREAMING_SNAKE_CASE_ = 60					,       **SCREAMING_SNAKE_CASE_					,       )       ->   Optional[Any]:
            super().__init__(
                temperature=SCREAMING_SNAKE_CASE_					,       do_sample=SCREAMING_SNAKE_CASE_					,       renormalize_logits=SCREAMING_SNAKE_CASE_					,       output_scores=SCREAMING_SNAKE_CASE_					,       return_dict_in_generate=SCREAMING_SNAKE_CASE_					,       output_hidden_states=SCREAMING_SNAKE_CASE_					,       output_attentions=SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_					,       )
  
            UpperCamelCase :Tuple										=     coarse_semantic_pad_token
            UpperCamelCase :List[Any]										=     coarse_rate_hz
            UpperCamelCase :Optional[int]										=     n_coarse_codebooks
            UpperCamelCase :Dict										=     coarse_infer_token
            UpperCamelCase :Dict										=     max_coarse_input_length
            UpperCamelCase :Dict										=     max_coarse_history
            UpperCamelCase :List[Any]										=     sliding_window_len
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      lowercase				):
  
  
  
        """simple docstring"""
        UpperCamelCase_       :      Tuple     ='fine_acoustics'
        def __init__(       self					,       SCREAMING_SNAKE_CASE_=0.5					,       SCREAMING_SNAKE_CASE_=512					,       SCREAMING_SNAKE_CASE_=1024					,       SCREAMING_SNAKE_CASE_=8					,       **SCREAMING_SNAKE_CASE_					,       )       ->   List[Any]:
            super().__init__(temperature=SCREAMING_SNAKE_CASE_				)
  
            UpperCamelCase :str										=     max_fine_history_length
            UpperCamelCase :Optional[int]										=     max_fine_input_length
            UpperCamelCase :Optional[int]										=     n_fine_codebooks
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      lowercase				):
  
  
  
        """simple docstring"""
        UpperCamelCase_       :      List[str]     ='bark'
        UpperCamelCase_       :      Optional[Any]     =True
        def __init__(       self					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_=2_4000					,       SCREAMING_SNAKE_CASE_=1024					,       **SCREAMING_SNAKE_CASE_					,       )       ->   Tuple:
            if semantic_config is None:
                UpperCamelCase :Optional[int]										=     {}
                logger.info('''semantic_config is None. initializing the semantic model with default values.'''				)
  
            if coarse_acoustics_config is None:
                UpperCamelCase :Union[str, Any]										=     {}
                logger.info('''coarse_acoustics_config is None. initializing the coarse model with default values.'''				)
  
            if fine_acoustics_config is None:
                UpperCamelCase :Dict										=     {}
                logger.info('''fine_acoustics_config is None. initializing the fine model with default values.'''				)
  
            UpperCamelCase :Tuple										=     BarkSemanticGenerationConfig(**SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :str										=     BarkCoarseGenerationConfig(**SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :Optional[int]										=     BarkFineGenerationConfig(**SCREAMING_SNAKE_CASE_				)
  
            UpperCamelCase :Dict										=     sample_rate
            UpperCamelCase :Union[str, Any]										=     codebook_size
        @classmethod
        def 					UpperCAmelCase		(       cls					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_					,       )       ->   Optional[Any]:
            return cls(
                semantic_config=semantic_config.to_dict()					,       coarse_acoustics_config=coarse_acoustics_config.to_dict()					,       fine_acoustics_config=fine_acoustics_config.to_dict()					,       **SCREAMING_SNAKE_CASE_					,       )
  
  
  
  
        def 					UpperCAmelCase		(       self				)       ->   Dict:
            UpperCamelCase :str										=     copy.deepcopy(self.__dict__				)
  
            UpperCamelCase :Tuple										=     self.semantic_config.to_dict()
            UpperCamelCase :Optional[int]										=     self.coarse_acoustics_config.to_dict()
            UpperCamelCase :List[Any]										=     self.fine_acoustics_config.to_dict()
  
            UpperCamelCase :Union[str, Any]										=     self.__class__.model_type
            return output
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __snake_case							      =						{
      """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
  }
  
  try:
   if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __snake_case							      =						["""LlamaTokenizer"""]
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __snake_case							      =						["""LlamaTokenizerFast"""]
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __snake_case							      =						[
       """OpenLlamaForCausalLM""",
       """OpenLlamaModel""",
       """OpenLlamaPreTrainedModel""",
       """OpenLlamaForSequenceClassification""",
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig
  
   try:
    if not is_sentencepiece_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from transformers import LlamaTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from transformers import LlamaTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_open_llama import (
        OpenLlamaForCausalLM,
        OpenLlamaForSequenceClassification,
        OpenLlamaModel,
        OpenLlamaPreTrainedModel,
    )
  
  
  else:
   import sys
  
   __snake_case							      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  