



from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy






class 	lowercase		(				_SCREAMING_SNAKE_CASE):






 """simple docstring"""


 a__   :      Tuple	     =							'MarkupLMFeatureExtractor'
 a__   :      Dict	     =							('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
 a__   :      str	     =							True




 def __call__(			self			:		str		,						__UpperCAmelCase			:		Union[str, Any]=None		,						__UpperCAmelCase			:		Any=None		,						__UpperCAmelCase			:		str=None		,						__UpperCAmelCase			:		List[Any]=None		,						__UpperCAmelCase			:		int=None		,						__UpperCAmelCase			:		bool = True		,						__UpperCAmelCase			:		Union[bool, str, PaddingStrategy] = False		,						__UpperCAmelCase			:		Union[bool, str, TruncationStrategy] = None		,						__UpperCAmelCase			:		Optional[int] = None		,						__UpperCAmelCase			:		int = 0		,						__UpperCAmelCase			:		Optional[int] = None		,						__UpperCAmelCase			:		Optional[bool] = None		,						__UpperCAmelCase			:		Optional[bool] = None		,						__UpperCAmelCase			:		bool = False		,						__UpperCAmelCase			:		bool = False		,						__UpperCAmelCase			:		bool = False		,						__UpperCAmelCase			:		bool = False		,						__UpperCAmelCase			:		bool = True		,						__UpperCAmelCase			:		Optional[Union[str, TensorType]] = None		,						**__UpperCAmelCase			:		List[Any]		,						)    ->				List[str]:
       # first, create nodes and xpaths
       if self.parse_html:
             if html_strings is None:
                   raise ValueError("""Make sure to pass HTML strings in case `parse_html` is set to `True`"""  )

             if nodes is not None or xpaths is not None or node_labels is not None:
                   raise ValueError(
                       """Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"""  )

             UpperCAmelCase_=     self.feature_extractor(UpperCamelCase__  )
             UpperCAmelCase_=     features["""nodes"""]
             UpperCAmelCase_=     features["""xpaths"""]
       else:
             if html_strings is not None:
                   raise ValueError("""You have passed HTML strings but `parse_html` is set to `False`."""  )
             if nodes is None or xpaths is None:
                   raise ValueError("""Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"""  )

        # # second, apply the tokenizer
       if questions is not None and self.parse_html:
             if isinstance(UpperCamelCase__		,						UpperCamelCase__  ):
                   UpperCAmelCase_=     [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

       UpperCAmelCase_=     self.tokenizer(
           text=questions if questions is not None else nodes		,						text_pair=nodes if questions is not None else None		,						xpaths=UpperCamelCase__		,						node_labels=UpperCamelCase__		,						add_special_tokens=UpperCamelCase__		,						padding=UpperCamelCase__		,						truncation=UpperCamelCase__		,						max_length=UpperCamelCase__		,						stride=UpperCamelCase__		,						pad_to_multiple_of=UpperCamelCase__		,						return_token_type_ids=UpperCamelCase__		,						return_attention_mask=UpperCamelCase__		,						return_overflowing_tokens=UpperCamelCase__		,						return_special_tokens_mask=UpperCamelCase__		,						return_offsets_mapping=UpperCamelCase__		,						return_length=UpperCamelCase__		,						verbose=UpperCamelCase__		,						return_tensors=UpperCamelCase__		,						**UpperCamelCase__		,						)

       return encoded_inputs




 def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict		,						*__UpperCAmelCase			:		int		,						**__UpperCAmelCase			:		Optional[Any]  )    ->				Dict:
       return self.tokenizer.batch_decode(*UpperCamelCase__		,						**UpperCamelCase__  )




 def 						_SCREAMING_SNAKE_CASE	(			self			:		Any		,						*__UpperCAmelCase			:		Union[str, Any]		,						**__UpperCAmelCase			:		Tuple  )    ->				Optional[int]:
       return self.tokenizer.decode(*UpperCamelCase__		,						**UpperCamelCase__  )




 @property
 def 						_SCREAMING_SNAKE_CASE	(			self			:		Any  )    ->				List[Any]:
       UpperCAmelCase_=     self.tokenizer.model_input_names
       return tokenizer_input_names









import copy
from typing import Dict

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


__A       					=		logging.get_logger(__name__)






class 	lowercase		(				snake_case__):






	"""simple docstring"""


	a__   :      Optional[int]	     =							"semantic"




	def __init__(			self			:		Any		,						__UpperCAmelCase			:		List[str]=10_000		,						__UpperCAmelCase			:		List[Any]=True		,						__UpperCAmelCase			:		int=768		,						__UpperCAmelCase			:		Dict=False		,						__UpperCAmelCase			:		str=False		,						__UpperCAmelCase			:		Optional[int]=False		,						__UpperCAmelCase			:		int=False		,						__UpperCAmelCase			:		Union[str, Any]=0.7		,						__UpperCAmelCase			:		Optional[Any]=True		,						__UpperCAmelCase			:		int=10_048		,						__UpperCAmelCase			:		Dict=129_595		,						__UpperCAmelCase			:		Optional[Any]=129_599		,						__UpperCAmelCase			:		Tuple=10_000		,						__UpperCAmelCase			:		List[Any]=256		,						__UpperCAmelCase			:		Union[str, Any]=49.9		,						**__UpperCAmelCase			:		Optional[Any]		,						)    ->				Tuple:
							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		,						)

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






class 	lowercase		(				snake_case__):






	"""simple docstring"""


	a__   :      Any	     =							"coarse_acoustics"




	def __init__(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		Optional[Any]=True		,						__UpperCAmelCase			:		Optional[int]=False		,						__UpperCAmelCase			:		Union[str, Any]=False		,						__UpperCAmelCase			:		List[str]=False		,						__UpperCAmelCase			:		Any=False		,						__UpperCAmelCase			:		Union[str, Any]=0.7		,						__UpperCAmelCase			:		List[Any]=True		,						__UpperCAmelCase			:		Optional[int]=12_048		,						__UpperCAmelCase			:		Optional[Any]=75		,						__UpperCAmelCase			:		str=2		,						__UpperCAmelCase			:		Optional[int]=12_050		,						__UpperCAmelCase			:		Union[str, Any]=256		,						__UpperCAmelCase			:		int = 630		,						__UpperCAmelCase			:		int = 60		,						**__UpperCAmelCase			:		int		,						)    ->				Union[str, Any]:
							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		,						)

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






class 	lowercase		(				snake_case__):






	"""simple docstring"""


	a__   :      Tuple	     =							"fine_acoustics"




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

							UpperCAmelCase_=     max_fine_history_length
							UpperCAmelCase_=     max_fine_input_length
							UpperCAmelCase_=     n_fine_codebooks






class 	lowercase		(				snake_case__):






	"""simple docstring"""


	a__   :      Any	     =							"bark"
	a__   :      List[str]	     =							True




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

							if coarse_acoustics_config is None:
													UpperCAmelCase_=     {}
													logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""  )

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

							UpperCAmelCase_=     BarkSemanticGenerationConfig(**__UpperCAmelCase  )
							UpperCAmelCase_=     BarkCoarseGenerationConfig(**__UpperCAmelCase  )
							UpperCAmelCase_=     BarkFineGenerationConfig(**__UpperCAmelCase  )

							UpperCAmelCase_=     sample_rate
							UpperCAmelCase_=     codebook_size




	@classmethod
	def 						_SCREAMING_SNAKE_CASE	(			cls			:		str		,						__UpperCAmelCase			:		BarkSemanticGenerationConfig		,						__UpperCAmelCase			:		BarkCoarseGenerationConfig		,						__UpperCAmelCase			:		BarkFineGenerationConfig		,						**__UpperCAmelCase			:		Any		,						)    ->				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()		,						**__UpperCAmelCase		,						)




	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				Optional[Any]:
							UpperCAmelCase_=     copy.deepcopy(self.__dict__  )

							UpperCAmelCase_=     self.semantic_config.to_dict()
							UpperCAmelCase_=     self.coarse_acoustics_config.to_dict()
							UpperCAmelCase_=     self.fine_acoustics_config.to_dict()

							UpperCAmelCase_=     self.__class__.model_type
							return output




