






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
						from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__lowerCamelCase						= logging.get_logger(__name__)

__lowerCamelCase						= {
    'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/config.json',
    'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/config.json',
    'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json',
    'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json',
    'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json',
    'microsoft/deberta-xlarge-mnli': 'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json',
}

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  '''deberta'''




						def __init__(    self		,	lowercase=50_265		,	lowercase=768		,	lowercase=12		,	lowercase=12		,	lowercase=3_072		,	lowercase="gelu"		,	lowercase=0.1		,	lowercase=0.1		,	lowercase=512		,	lowercase=0		,	lowercase=0.02		,	lowercase=1e-7		,	lowercase=False		,	lowercase=-1		,	lowercase=0		,	lowercase=True		,	lowercase=None		,	lowercase=0		,	lowercase="gelu"		,	**lowercase		,	)			->						Tuple:
													super().__init__(**lowercase       )

													_a  :    int			=						hidden_size
													_a  :    str			=						num_hidden_layers
													_a  :    Dict			=						num_attention_heads
													_a  :    Union[str, Any]			=						intermediate_size
													_a  :    Optional[int]			=						hidden_act
													_a  :    Tuple			=						hidden_dropout_prob
													_a  :    List[str]			=						attention_probs_dropout_prob
													_a  :    Optional[Any]			=						max_position_embeddings
													_a  :    Optional[Any]			=						type_vocab_size
													_a  :    int			=						initializer_range
													_a  :    Optional[Any]			=						relative_attention
													_a  :    Any			=						max_relative_positions
													_a  :    List[str]			=						pad_token_id
													_a  :    Dict			=						position_biased_input

													# Backwards compatibility
													if type(lowercase       ) == str:
																				_a  :    str			=						[x.strip() for x in pos_att_type.lower().split('''|'''       )]

													_a  :    List[Any]			=						pos_att_type
													_a  :    Dict			=						vocab_size
													_a  :    Tuple			=						layer_norm_eps

													_a  :    List[str]			=						kwargs.get('''pooler_hidden_size'''		,	lowercase       )
													_a  :    Optional[int]			=						pooler_dropout
													_a  :    Union[str, Any]			=						pooler_hidden_act

class      UpperCamelCase_							(				UpperCamelCase       ):




						@property
						def      snake_case__(    self       )			->						Mapping[str, Mapping[int, str]]:
													if self.task == "multiple-choice":
																				_a  :    Tuple			=						{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
													else:
																				_a  :    Union[str, Any]			=						{0: '''batch''', 1: '''sequence'''}
													if self._config.type_vocab_size > 0:
																				return OrderedDict(
																				    [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]       )
													else:
																				return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]       )




						@property
						def      snake_case__(    self       )			->						int:
													return 12







						def      snake_case__(    self		,	lowercase		,	lowercase = -1		,	lowercase = -1		,	lowercase = -1		,	lowercase = False		,	lowercase = None		,	lowercase = 3		,	lowercase = 40		,	lowercase = 40		,	lowercase = None		,	)			->						Mapping[str, Any]:
													_a  :    Any			=						super().generate_dummy_inputs(preprocessor=lowercase		,	framework=lowercase       )
													if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
																				del dummy_inputs["token_type_ids"]
													return dummy_inputs







import copy
from typing import Dict

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


__lowerCamelCase						= logging.get_logger(__name__)

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  '''semantic'''




						def __init__(    self		,	lowercase=10_000		,	lowercase=True		,	lowercase=768		,	lowercase=False		,	lowercase=False		,	lowercase=False		,	lowercase=False		,	lowercase=0.7		,	lowercase=True		,	lowercase=10_048		,	lowercase=129_595		,	lowercase=129_599		,	lowercase=10_000		,	lowercase=256		,	lowercase=49.9		,	**lowercase		,	)			->						int:
													super().__init__(
													    temperature=lowercase		,	do_sample=lowercase		,	eos_token_id=lowercase		,	renormalize_logits=lowercase		,	max_new_tokens=lowercase		,	output_scores=lowercase		,	return_dict_in_generate=lowercase		,	output_hidden_states=lowercase		,	output_attentions=lowercase		,	**lowercase		,	)

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

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  '''coarse_acoustics'''




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

													_a  :    str			=						coarse_semantic_pad_token
													_a  :    Any			=						coarse_rate_hz
													_a  :    Dict			=						n_coarse_codebooks
													_a  :    str			=						coarse_infer_token
													_a  :    List[Any]			=						max_coarse_input_length
													_a  :    Union[str, Any]			=						max_coarse_history
													_a  :    Any			=						sliding_window_len

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  '''fine_acoustics'''




						def __init__(    self		,	lowercase=0.5		,	lowercase=512		,	lowercase=1_024		,	lowercase=8		,	**lowercase		,	)			->						str:
													super().__init__(temperature=lowercase       )

													_a  :    Tuple			=						max_fine_history_length
													_a  :    Tuple			=						max_fine_input_length
													_a  :    Any			=						n_fine_codebooks

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  '''bark'''
						lowercase									=  True




						def __init__(    self		,	lowercase = None		,	lowercase = None		,	lowercase = None		,	lowercase=24_000		,	lowercase=1_024		,	**lowercase		,	)			->						List[str]:
													if semantic_config is None:
																				_a  :    str			=						{}
																				logger.info('''semantic_config is None. initializing the semantic model with default values.'''       )

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

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

													_a  :    List[Any]			=						BarkSemanticGenerationConfig(**lowercase       )
													_a  :    Union[str, Any]			=						BarkCoarseGenerationConfig(**lowercase       )
													_a  :    str			=						BarkFineGenerationConfig(**lowercase       )

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




						@classmethod
						def      snake_case__(    cls		,	lowercase		,	lowercase		,	lowercase		,	**lowercase		,	)			->						List[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()		,	**lowercase		,	)







						def      snake_case__(    self       )			->						List[str]:
													_a  :    Any			=						copy.deepcopy(self.__dict__       )

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

													_a  :    List[str]			=						self.__class__.model_type
													return output