



"""simple docstring"""
import copy
from typing import Dict

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


__a     =    logging.get_logger(__name__)
class     lowerCamelCase  (      _lowerCAmelCase    ):






					'''simple docstring'''





					_A		:      List[str]						=					"""semantic"""






					def __init__(	self:							Any						,					snake_case:							Optional[int]=10_000						,					snake_case:							Tuple=True						,					snake_case:							List[Any]=768						,					snake_case:							Any=False						,					snake_case:							Tuple=False						,					snake_case:							Tuple=False						,					snake_case:							Dict=False						,					snake_case:							Any=0.7						,					snake_case:							int=True						,					snake_case:							Tuple=10_048						,					snake_case:							List[str]=129_595						,					snake_case:							List[str]=129_599						,					snake_case:							List[str]=10_000						,					snake_case:							Optional[Any]=256						,					snake_case:							Optional[int]=4_9.9						,					**snake_case:							Optional[int]						,					)       ->     Tuple:
											super().__init__(
											    temperature=snake_case						,					do_sample=snake_case						,					eos_token_id=snake_case						,					renormalize_logits=snake_case						,					max_new_tokens=snake_case						,					output_scores=snake_case						,					return_dict_in_generate=snake_case						,					output_hidden_states=snake_case						,					output_attentions=snake_case						,					**snake_case						,					)

											snake_case_			:int						=      text_encoding_offset
											snake_case_			:Optional[int]						=      text_pad_token
											snake_case_			:List[Any]						=      eos_token_id
											snake_case_			:int						=      semantic_infer_token
											snake_case_			:List[Any]						=      semantic_vocab_size
											snake_case_			:Union[str, Any]						=      max_input_semantic_length
											snake_case_			:List[Any]						=      semantic_rate_hz

class     lowerCamelCase  (      _lowerCAmelCase    ):






					'''simple docstring'''





					_A		:      Optional[Any]						=					"""coarse_acoustics"""






					def __init__(	self:							Dict						,					snake_case:							Optional[Any]=True						,					snake_case:							List[str]=False						,					snake_case:							Dict=False						,					snake_case:							Any=False						,					snake_case:							List[str]=False						,					snake_case:							Optional[Any]=0.7						,					snake_case:							Dict=True						,					snake_case:							str=12_048						,					snake_case:							Optional[Any]=75						,					snake_case:							str=2						,					snake_case:							str=12_050						,					snake_case:							int=256						,					snake_case:							int = 630						,					snake_case:							int = 60						,					**snake_case:							List[str]						,					)       ->     Dict:
											super().__init__(
											    temperature=snake_case						,					do_sample=snake_case						,					renormalize_logits=snake_case						,					output_scores=snake_case						,					return_dict_in_generate=snake_case						,					output_hidden_states=snake_case						,					output_attentions=snake_case						,					**snake_case						,					)

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

class     lowerCamelCase  (      _lowerCAmelCase    ):






					'''simple docstring'''





					_A		:      List[Any]						=					"""fine_acoustics"""






					def __init__(	self:							List[Any]						,					snake_case:							Dict=0.5						,					snake_case:							int=512						,					snake_case:							Any=1_024						,					snake_case:							Dict=8						,					**snake_case:							Dict						,					)       ->     str:
											super().__init__(temperature=snake_case						)

											snake_case_			:int						=      max_fine_history_length
											snake_case_			:Any						=      max_fine_input_length
											snake_case_			:Union[str, Any]						=      n_fine_codebooks

class     lowerCamelCase  (      _lowerCAmelCase    ):






					'''simple docstring'''





					_A		:      Any						=					"""bark"""
					_A		:      Any						=					True






					def __init__(	self:							Any						,					snake_case:							Dict = None						,					snake_case:							Dict = None						,					snake_case:							Dict = None						,					snake_case:							int=24_000						,					snake_case:							List[Any]=1_024						,					**snake_case:							Optional[Any]						,					)       ->     Dict:
											if semantic_config is None:
																	snake_case_			:int						=      {}
																	logger.info("""semantic_config is None. initializing the semantic model with default values."""						)

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

											if fine_acoustics_config is None:
																	snake_case_			:List[str]						=      {}
																	logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""						)

											snake_case_			:List[Any]						=      BarkSemanticGenerationConfig(**snake_case						)
											snake_case_			:List[str]						=      BarkCoarseGenerationConfig(**snake_case						)
											snake_case_			:Union[str, Any]						=      BarkFineGenerationConfig(**snake_case						)

											snake_case_			:List[str]						=      sample_rate
											snake_case_			:str						=      codebook_size






					@classmethod
					def        lowerCAmelCase_    (	cls:							str						,					snake_case:							BarkSemanticGenerationConfig						,					snake_case:							BarkCoarseGenerationConfig						,					snake_case:							BarkFineGenerationConfig						,					**snake_case:							Tuple						,					)       ->     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()						,					**snake_case						,					)






					def        lowerCAmelCase_    (	self:							Optional[int]						)       ->     List[str]:
											snake_case_			:List[str]						=      copy.deepcopy(self.__dict__						)

											snake_case_			:Tuple						=      self.semantic_config.to_dict()
											snake_case_			:Optional[Any]						=      self.coarse_acoustics_config.to_dict()
											snake_case_			:Optional[Any]						=      self.fine_acoustics_config.to_dict()

											snake_case_			:int						=      self.__class__.model_type
											return output





"""simple docstring"""
from typing import TYPE_CHECKING

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


__a     =    {
    "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a     =    ["XLMProphetNetTokenizer"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a     =    [
						    "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "XLMProphetNetDecoder",
						    "XLMProphetNetEncoder",
						    "XLMProphetNetForCausalLM",
						    "XLMProphetNetForConditionalGeneration",
						    "XLMProphetNetModel",
						    "XLMProphetNetPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_xlm_prophetnet import (
												    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    XLMProphetNetDecoder,
												    XLMProphetNetEncoder,
												    XLMProphetNetForCausalLM,
												    XLMProphetNetForConditionalGeneration,
												    XLMProphetNetModel,
												    XLMProphetNetPreTrainedModel,
												)

else:
						import sys

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