


import copy
from typing import Dict

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


snake_case__     							= logging.get_logger(__name__)



class     snake_case__  (      _A  ):
				__lowerCAmelCase		=      '''semantic'''
				def __init__(  self     ,      _UpperCAmelCase=1_0000     ,      _UpperCAmelCase=True     ,      _UpperCAmelCase=768     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=0.7     ,      _UpperCAmelCase=True     ,      _UpperCAmelCase=1_0048     ,      _UpperCAmelCase=12_9595     ,      _UpperCAmelCase=12_9599     ,      _UpperCAmelCase=1_0000     ,      _UpperCAmelCase=256     ,      _UpperCAmelCase=49.9     ,      **_UpperCAmelCase     ,      ):




										"""simple docstring"""




										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     ,      )

										__magic_name__				:							str				=      text_encoding_offset
										__magic_name__				:							Dict				=      text_pad_token
										__magic_name__				:							Tuple				=      eos_token_id
										__magic_name__				:							Union[str, Any]				=      semantic_infer_token
										__magic_name__				:							Dict				=      semantic_vocab_size
										__magic_name__				:							Any				=      max_input_semantic_length
										__magic_name__				:							Optional[Any]				=      semantic_rate_hz






class     snake_case__  (      _A  ):
				__lowerCAmelCase		=      '''coarse_acoustics'''
				def __init__(  self     ,      _UpperCAmelCase=True     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=0.7     ,      _UpperCAmelCase=True     ,      _UpperCAmelCase=1_2048     ,      _UpperCAmelCase=75     ,      _UpperCAmelCase=2     ,      _UpperCAmelCase=1_2050     ,      _UpperCAmelCase=256     ,      _UpperCAmelCase = 630     ,      _UpperCAmelCase = 60     ,      **_UpperCAmelCase     ,      ):




										"""simple docstring"""




										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     ,      )

										__magic_name__				:							Optional[Any]				=      coarse_semantic_pad_token
										__magic_name__				:							str				=      coarse_rate_hz
										__magic_name__				:							str				=      n_coarse_codebooks
										__magic_name__				:							List[Any]				=      coarse_infer_token
										__magic_name__				:							Optional[Any]				=      max_coarse_input_length
										__magic_name__				:							Any				=      max_coarse_history
										__magic_name__				:							List[Any]				=      sliding_window_len






class     snake_case__  (      _A  ):
				__lowerCAmelCase		=      '''fine_acoustics'''
				def __init__(  self     ,      _UpperCAmelCase=0.5     ,      _UpperCAmelCase=512     ,      _UpperCAmelCase=1024     ,      _UpperCAmelCase=8     ,      **_UpperCAmelCase     ,      ):




										"""simple docstring"""




										super().__init__(temperature=_UpperCAmelCase      )

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






class     snake_case__  (      _A  ):
				__lowerCAmelCase		=      '''bark'''
				__lowerCAmelCase		=      True
				def __init__(  self     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase=2_4000     ,      _UpperCAmelCase=1024     ,      **_UpperCAmelCase     ,      ):




										"""simple docstring"""




										if semantic_config is None:
																__magic_name__				:							int				=      {}
																logger.info('semantic_config is None. initializing the semantic model with default values.'      )

										if coarse_acoustics_config is None:
																__magic_name__				:							Optional[int]				=      {}
																logger.info('coarse_acoustics_config is None. initializing the coarse model with default values.'      )

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

										__magic_name__				:							Optional[Any]				=      BarkSemanticGenerationConfig(**_UpperCAmelCase      )
										__magic_name__				:							str				=      BarkCoarseGenerationConfig(**_UpperCAmelCase      )
										__magic_name__				:							List[str]				=      BarkFineGenerationConfig(**_UpperCAmelCase      )

										__magic_name__				:							List[str]				=      sample_rate
										__magic_name__				:							Any				=      codebook_size
				@classmethod
				def  UpperCamelCase_						(  cls     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      **_UpperCAmelCase     ,      ):




										"""simple docstring"""




										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  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Any				=      copy.deepcopy(self.__dict__      )

										__magic_name__				:							Optional[int]				=      self.semantic_config.to_dict()
										__magic_name__				:							Tuple				=      self.coarse_acoustics_config.to_dict()
										__magic_name__				:							Dict				=      self.fine_acoustics_config.to_dict()

										__magic_name__				:							Optional[int]				=      self.__class__.model_type
										return output






from typing import TYPE_CHECKING

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


snake_case__     							= {
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__     							= [
			    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'FlaubertForMultipleChoice',
			    'FlaubertForQuestionAnswering',
			    'FlaubertForQuestionAnsweringSimple',
			    'FlaubertForSequenceClassification',
			    'FlaubertForTokenClassification',
			    'FlaubertModel',
			    'FlaubertWithLMHeadModel',
			    'FlaubertPreTrainedModel',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__     							= [
			    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFFlaubertForMultipleChoice',
			    'TFFlaubertForQuestionAnsweringSimple',
			    'TFFlaubertForSequenceClassification',
			    'TFFlaubertForTokenClassification',
			    'TFFlaubertModel',
			    'TFFlaubertPreTrainedModel',
			    'TFFlaubertWithLMHeadModel',
			]


if TYPE_CHECKING:
			from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
			from .tokenization_flaubert import FlaubertTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flaubert import (
						    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FlaubertForMultipleChoice,
						    FlaubertForQuestionAnswering,
						    FlaubertForQuestionAnsweringSimple,
						    FlaubertForSequenceClassification,
						    FlaubertForTokenClassification,
						    FlaubertModel,
						    FlaubertPreTrainedModel,
						    FlaubertWithLMHeadModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_flaubert import (
						    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFFlaubertForMultipleChoice,
						    TFFlaubertForQuestionAnsweringSimple,
						    TFFlaubertForSequenceClassification,
						    TFFlaubertForTokenClassification,
						    TFFlaubertModel,
						    TFFlaubertPreTrainedModel,
						    TFFlaubertWithLMHeadModel,
						)

else:
			import sys

			snake_case__     							= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


