
from typing import TYPE_CHECKING

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


_A  					= {
    "configuration_dpr": ["DPR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPRConfig"],
    "tokenization_dpr": [
        "DPRContextEncoderTokenizer",
        "DPRQuestionEncoderTokenizer",
        "DPRReaderOutput",
        "DPRReaderTokenizer",
    ],
}


try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A  					= [
       "DPRContextEncoderTokenizerFast",
       "DPRQuestionEncoderTokenizerFast",
       "DPRReaderTokenizerFast",
   ]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A  					= [
       "DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "DPRContextEncoder",
       "DPRPretrainedContextEncoder",
       "DPRPreTrainedModel",
       "DPRPretrainedQuestionEncoder",
       "DPRPretrainedReader",
       "DPRQuestionEncoder",
       "DPRReader",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A  					= [
       "TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFDPRContextEncoder",
       "TFDPRPretrainedContextEncoder",
       "TFDPRPretrainedQuestionEncoder",
       "TFDPRPretrainedReader",
       "TFDPRQuestionEncoder",
       "TFDPRReader",
   ]


if TYPE_CHECKING:
   from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
   from .tokenization_dpr import (
       DPRContextEncoderTokenizer,
       DPRQuestionEncoderTokenizer,
       DPRReaderOutput,
       DPRReaderTokenizer,
   )

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_dpr_fast import (
          DPRContextEncoderTokenizerFast,
          DPRQuestionEncoderTokenizerFast,
          DPRReaderTokenizerFast,
      )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_dpr import (
          DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DPRContextEncoder,
          DPRPretrainedContextEncoder,
          DPRPreTrainedModel,
          DPRPretrainedQuestionEncoder,
          DPRPretrainedReader,
          DPRQuestionEncoder,
          DPRReader,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_dpr import (
          TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDPRContextEncoder,
          TFDPRPretrainedContextEncoder,
          TFDPRPretrainedQuestionEncoder,
          TFDPRPretrainedReader,
          TFDPRQuestionEncoder,
          TFDPRReader,
      )

else:
   import sys

   _A  					= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



import copy
from typing import Dict

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


_A  					= logging.get_logger(__name__)







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							Tuple           =		"semantic"





     def __init__(self      :    Dict				,    _A      :    Optional[Any]=1_0_0_0_0				,    _A      :    List[Any]=True				,    _A      :    Any=7_6_8				,    _A      :    Union[str, Any]=False				,    _A      :    List[Any]=False				,    _A      :    Union[str, Any]=False				,    _A      :    Union[str, Any]=False				,    _A      :    str=0.7				,    _A      :    Optional[int]=True				,    _A      :    Any=1_0_0_4_8				,    _A      :    Optional[int]=1_2_9_5_9_5				,    _A      :    Union[str, Any]=1_2_9_5_9_9				,    _A      :    Any=1_0_0_0_0				,    _A      :    str=2_5_6				,    _A      :    int=49.9				,    **_A      :    Union[str, Any]				,    )						->						Any:
         super().__init__(
             temperature=_A				,    do_sample=_A				,    eos_token_id=_A				,    renormalize_logits=_A				,    max_new_tokens=_A				,    output_scores=_A				,    return_dict_in_generate=_A				,    output_hidden_states=_A				,    output_attentions=_A				,    **_A				,    )

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







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							List[Any]           =		"coarse_acoustics"





     def __init__(self      :    Any				,    _A      :    Union[str, Any]=True				,    _A      :    List[Any]=False				,    _A      :    Any=False				,    _A      :    Union[str, Any]=False				,    _A      :    Optional[Any]=False				,    _A      :    str=0.7				,    _A      :    int=True				,    _A      :    List[str]=1_2_0_4_8				,    _A      :    Optional[int]=7_5				,    _A      :    Any=2				,    _A      :    Optional[Any]=1_2_0_5_0				,    _A      :    Tuple=2_5_6				,    _A      :    int = 6_3_0				,    _A      :    int = 6_0				,    **_A      :    int				,    )						->						Union[str, Any]:
         super().__init__(
             temperature=_A				,    do_sample=_A				,    renormalize_logits=_A				,    output_scores=_A				,    return_dict_in_generate=_A				,    output_hidden_states=_A				,    output_attentions=_A				,    **_A				,    )

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







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							int           =		"fine_acoustics"





     def __init__(self      :    Tuple				,    _A      :    Optional[int]=0.5				,    _A      :    List[str]=5_1_2				,    _A      :    Union[str, Any]=1_0_2_4				,    _A      :    Tuple=8				,    **_A      :    Tuple				,    )						->						Tuple:
         super().__init__(temperature=_A   )

         snake_case      =					max_fine_history_length
         snake_case      =					max_fine_input_length
         snake_case      =					n_fine_codebooks







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							Optional[Any]           =		"bark"
     UpperCAmelCase__				:							Union[str, Any]           =		True





     def __init__(self      :    Union[str, Any]				,    _A      :    Dict = None				,    _A      :    Dict = None				,    _A      :    Dict = None				,    _A      :    Optional[int]=2_4_0_0_0				,    _A      :    Any=1_0_2_4				,    **_A      :    List[str]				,    )						->						Optional[Any]:
         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."   )

         snake_case      =					BarkSemanticGenerationConfig(**_A   )
         snake_case      =					BarkCoarseGenerationConfig(**_A   )
         snake_case      =					BarkFineGenerationConfig(**_A   )

         snake_case      =					sample_rate
         snake_case      =					codebook_size





     @classmethod
     def 		UpperCAmelCase(cls      :    List[Any]				,    _A      :    BarkSemanticGenerationConfig				,    _A      :    BarkCoarseGenerationConfig				,    _A      :    BarkFineGenerationConfig				,    **_A      :    Optional[int]				,    )						->						Union[str, 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()				,    **_A				,    )





     def 		UpperCAmelCase(self      :    List[str]   )						->						int:
         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.__class__.model_type
         return output

