



import copy
from typing import Dict

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


_lowerCamelCase         =	logging.get_logger(__name__)

class    _SCREAMING_SNAKE_CASE						(_a			):
     lowerCAmelCase       =      """semantic"""





     def __init__(							self			:				Dict  ,	UpperCamelCase			:				List[str]=1_0_0_0_0  ,	UpperCamelCase			:				int=True  ,	UpperCamelCase			:				str=7_6_8  ,	UpperCamelCase			:				str=False  ,	UpperCamelCase			:				str=False  ,	UpperCamelCase			:				str=False  ,	UpperCamelCase			:				Optional[int]=False  ,	UpperCamelCase			:				Any=0.7  ,	UpperCamelCase			:				Any=True  ,	UpperCamelCase			:				Dict=1_0_0_4_8  ,	UpperCamelCase			:				Optional[Any]=1_2_9_5_9_5  ,	UpperCamelCase			:				Dict=1_2_9_5_9_9  ,	UpperCamelCase			:				Optional[Any]=1_0_0_0_0  ,	UpperCamelCase			:				Dict=2_5_6  ,	UpperCamelCase			:				Dict=4_9.9  ,	**UpperCamelCase			:				Optional[int]  ,	)->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  ,	)

      __SCREAMING_SNAKE_CASE							:     Tuple							=						text_encoding_offset
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						text_pad_token
      __SCREAMING_SNAKE_CASE							:     List[str]							=						eos_token_id
      __SCREAMING_SNAKE_CASE							:     str							=						semantic_infer_token
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						semantic_vocab_size
      __SCREAMING_SNAKE_CASE							:     Any							=						max_input_semantic_length
      __SCREAMING_SNAKE_CASE							:     Dict							=						semantic_rate_hz






class    _SCREAMING_SNAKE_CASE						(_a			):
     lowerCAmelCase       =      """coarse_acoustics"""





     def __init__(							self			:				int  ,	UpperCamelCase			:				Tuple=True  ,	UpperCamelCase			:				List[str]=False  ,	UpperCamelCase			:				List[str]=False  ,	UpperCamelCase			:				Union[str, Any]=False  ,	UpperCamelCase			:				Any=False  ,	UpperCamelCase			:				str=0.7  ,	UpperCamelCase			:				int=True  ,	UpperCamelCase			:				Dict=1_2_0_4_8  ,	UpperCamelCase			:				Optional[Any]=7_5  ,	UpperCamelCase			:				List[str]=2  ,	UpperCamelCase			:				Tuple=1_2_0_5_0  ,	UpperCamelCase			:				Union[str, Any]=2_5_6  ,	UpperCamelCase			:				Optional[int] = 6_3_0  ,	UpperCamelCase			:				int = 6_0  ,	**UpperCamelCase			:				Dict  ,	)->Dict:
      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  ,	)

      __SCREAMING_SNAKE_CASE							:     List[str]							=						coarse_semantic_pad_token
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						coarse_rate_hz
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						n_coarse_codebooks
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						coarse_infer_token
      __SCREAMING_SNAKE_CASE							:     Any							=						max_coarse_input_length
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						max_coarse_history
      __SCREAMING_SNAKE_CASE							:     Tuple							=						sliding_window_len






class    _SCREAMING_SNAKE_CASE						(_a			):
     lowerCAmelCase       =      """fine_acoustics"""





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

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						max_fine_history_length
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						max_fine_input_length
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						n_fine_codebooks






class    _SCREAMING_SNAKE_CASE						(_a			):
     lowerCAmelCase       =      """bark"""
     lowerCAmelCase       =      True





     def __init__(							self			:				List[str]  ,	UpperCamelCase			:				Dict = None  ,	UpperCamelCase			:				Any = None  ,	UpperCamelCase			:				Optional[Any] = None  ,	UpperCamelCase			:				Optional[int]=2_4_0_0_0  ,	UpperCamelCase			:				Union[str, Any]=1_0_2_4  ,	**UpperCamelCase			:				int  ,	)->Tuple:
      if semantic_config is None:
       __SCREAMING_SNAKE_CASE							:     Optional[int]							=						{}
       logger.info("semantic_config is None. initializing the semantic model with default values."					)

      if coarse_acoustics_config is None:
       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						{}
       logger.info("coarse_acoustics_config is None. initializing the coarse model with default values."					)

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

      __SCREAMING_SNAKE_CASE							:     Any							=						BarkSemanticGenerationConfig(**_A					)
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						BarkCoarseGenerationConfig(**_A					)
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						BarkFineGenerationConfig(**_A					)

      __SCREAMING_SNAKE_CASE							:     Tuple							=						sample_rate
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						codebook_size





     @classmethod
     def 						__snake_case     (							cls			:				Union[str, Any]  ,	UpperCamelCase			:				str  ,	UpperCamelCase			:				str  ,	UpperCamelCase			:				str  ,	**UpperCamelCase			:				Any  ,	)->int:
      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 						__snake_case     (							self			:				int					)->str:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						copy.deepcopy(self.__dict__					)

      __SCREAMING_SNAKE_CASE							:     int							=						self.semantic_config.to_dict()
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self.coarse_acoustics_config.to_dict()
      __SCREAMING_SNAKE_CASE							:     Tuple							=						self.fine_acoustics_config.to_dict()

      __SCREAMING_SNAKE_CASE							:     int							=						self.__class__.model_type
      return output









from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase   					=  logging.get_logger(__name__)

lowerCamelCase   					=  {
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}





class      snake_case_      (					_a				):


							"""simple docstring"""





							__UpperCAmelCase      ="""prophetnet"""
							__UpperCAmelCase      =["""past_key_values"""]
							__UpperCAmelCase      ={
							    """num_attention_heads""": """num_encoder_attention_heads""",
							}




							def __init__(			self       ,    _A = 0.1       ,    _A = "gelu"       ,    _A = 3_0_5_2_2       ,    _A = 1_0_2_4       ,    _A = 4_0_9_6       ,    _A = 1_2       ,    _A = 1_6       ,    _A = 4_0_9_6       ,    _A = 1_2       ,    _A = 1_6       ,    _A = 0.1       ,    _A = 0.1       ,    _A = 5_1_2       ,    _A = 0.02       ,    _A = True       ,    _A = True       ,    _A = 0       ,    _A = 2       ,    _A = 3_2       ,    _A = 1_2_8       ,    _A = False       ,    _A = 0.0       ,    _A = True       ,    _A = 0       ,    _A = 1       ,    _A = 2       ,    **_A       ,    ):
												__lowerCAmelCase       =				vocab_size
												__lowerCAmelCase       =				hidden_size
												__lowerCAmelCase       =				encoder_ffn_dim
												__lowerCAmelCase       =				num_encoder_layers
												__lowerCAmelCase       =				num_encoder_attention_heads
												__lowerCAmelCase       =				decoder_ffn_dim
												__lowerCAmelCase       =				num_decoder_layers
												__lowerCAmelCase       =				num_decoder_attention_heads
												__lowerCAmelCase       =				max_position_embeddings
												__lowerCAmelCase       =				init_std  # Normal(0, this parameter)
												__lowerCAmelCase       =				activation_function

												# parameters for prophetnet
												__lowerCAmelCase       =				ngram
												__lowerCAmelCase       =				num_buckets
												__lowerCAmelCase       =				relative_max_distance
												__lowerCAmelCase       =				disable_ngram_loss
												__lowerCAmelCase       =				eps

												# 3 Types of Dropout
												__lowerCAmelCase       =				attention_dropout
												__lowerCAmelCase       =				activation_dropout
												__lowerCAmelCase       =				dropout

												__lowerCAmelCase       =				use_cache

												super().__init__(
												    pad_token_id=_A       ,    bos_token_id=_A       ,    eos_token_id=_A       ,    is_encoder_decoder=_A       ,    add_cross_attention=_A       ,    decoder_start_token_id=_A       ,    **_A       ,    )




							@property
							def 							A__	(			self				):
												return self.num_encoder_layers + self.num_decoder_layers




							@num_hidden_layers.setter
							def 							A__	(			self       ,    _A				):
												raise NotImplementedError(
												    'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
												    ' `num_decoder_layers`.'				)






