

"""simple docstring"""




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


A_							:     int     =	logging.get_logger(__name__)

A_							:     Optional[Any]     =	{
    "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 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						str   						=							'deberta'

			def __init__(   self			:  Union[str, Any]      ,			__UpperCAmelCase			:  int=5_0_2_6_5      ,			__UpperCAmelCase			:  int=7_6_8      ,			__UpperCAmelCase			:  int=1_2      ,			__UpperCAmelCase			:  int=1_2      ,			__UpperCAmelCase			:  int=3_0_7_2      ,			__UpperCAmelCase			:  str="gelu"      ,			__UpperCAmelCase			:  Optional[int]=0.1      ,			__UpperCAmelCase			:  Union[str, Any]=0.1      ,			__UpperCAmelCase			:  Any=5_1_2      ,			__UpperCAmelCase			:  List[Any]=0      ,			__UpperCAmelCase			:  Dict=0.02      ,			__UpperCAmelCase			:  Optional[Any]=1e-7      ,			__UpperCAmelCase			:  Dict=False      ,			__UpperCAmelCase			:  Dict=-1      ,			__UpperCAmelCase			:  Tuple=0      ,			__UpperCAmelCase			:  Any=True      ,			__UpperCAmelCase			:  Optional[Any]=None      ,			__UpperCAmelCase			:  List[Any]=0      ,			__UpperCAmelCase			:  int="gelu"      ,			**__UpperCAmelCase			:  Union[str, Any]      ,			)				->      List[str]:
						super().__init__(**__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= hidden_size
						SCREAMING_SNAKE_CASE__									= num_hidden_layers
						SCREAMING_SNAKE_CASE__									= num_attention_heads
						SCREAMING_SNAKE_CASE__									= intermediate_size
						SCREAMING_SNAKE_CASE__									= hidden_act
						SCREAMING_SNAKE_CASE__									= hidden_dropout_prob
						SCREAMING_SNAKE_CASE__									= attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__									= max_position_embeddings
						SCREAMING_SNAKE_CASE__									= type_vocab_size
						SCREAMING_SNAKE_CASE__									= initializer_range
						SCREAMING_SNAKE_CASE__									= relative_attention
						SCREAMING_SNAKE_CASE__									= max_relative_positions
						SCREAMING_SNAKE_CASE__									= pad_token_id
						SCREAMING_SNAKE_CASE__									= position_biased_input

						# Backwards compatibility
						if type(__UpperCAmelCase    ) == str:
									SCREAMING_SNAKE_CASE__									= [x.strip() for x in pos_att_type.lower().split("""|"""    )]

						SCREAMING_SNAKE_CASE__									= pos_att_type
						SCREAMING_SNAKE_CASE__									= vocab_size
						SCREAMING_SNAKE_CASE__									= layer_norm_eps

						SCREAMING_SNAKE_CASE__									= kwargs.get("""pooler_hidden_size"""      ,			__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= pooler_dropout
						SCREAMING_SNAKE_CASE__									= pooler_hidden_act





class 				lowerCamelCase  (A__     ):

			@property
			def    SCREAMING_SNAKE_CASE			(   self			:  int    )				->      Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
									SCREAMING_SNAKE_CASE__									= {0: """batch""", 1: """choice""", 2: """sequence"""}
						else:
									SCREAMING_SNAKE_CASE__									= {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    SCREAMING_SNAKE_CASE			(   self			:  str    )				->      int:
						return 1_2





			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]      ,			__UpperCAmelCase			:  Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]      ,			__UpperCAmelCase			:  int = -1      ,			__UpperCAmelCase			:  int = -1      ,			__UpperCAmelCase			:  int = -1      ,			__UpperCAmelCase			:  bool = False      ,			__UpperCAmelCase			:  Optional["TensorType"] = None      ,			__UpperCAmelCase			:  int = 3      ,			__UpperCAmelCase			:  int = 4_0      ,			__UpperCAmelCase			:  int = 4_0      ,			__UpperCAmelCase			:  "PreTrainedTokenizerBase" = None      ,			)				->      Mapping[str, Any]:
						SCREAMING_SNAKE_CASE__									= super().generate_dummy_inputs(preprocessor=__UpperCAmelCase      ,			framework=__UpperCAmelCase    )
						if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
									del dummy_inputs["token_type_ids"]
						return dummy_inputs





"""simple docstring"""




import copy
from typing import Dict

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


A_							:     str     =	logging.get_logger(__name__)





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Any   						=							'semantic'

			def __init__(   self			:  int      ,			__UpperCAmelCase			:  Union[str, Any]=1_0_0_0_0      ,			__UpperCAmelCase			:  List[str]=True      ,			__UpperCAmelCase			:  Tuple=7_6_8      ,			__UpperCAmelCase			:  Dict=False      ,			__UpperCAmelCase			:  Optional[int]=False      ,			__UpperCAmelCase			:  Dict=False      ,			__UpperCAmelCase			:  Dict=False      ,			__UpperCAmelCase			:  Any=0.7      ,			__UpperCAmelCase			:  Optional[int]=True      ,			__UpperCAmelCase			:  str=1_0_0_4_8      ,			__UpperCAmelCase			:  Tuple=1_2_9_5_9_5      ,			__UpperCAmelCase			:  List[Any]=1_2_9_5_9_9      ,			__UpperCAmelCase			:  Union[str, Any]=1_0_0_0_0      ,			__UpperCAmelCase			:  Dict=2_5_6      ,			__UpperCAmelCase			:  int=49.9      ,			**__UpperCAmelCase			:  Any      ,			)				->      List[Any]:
						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      ,			)

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





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Union[str, Any]   						=							'coarse_acoustics'

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

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





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Union[str, Any]   						=							'fine_acoustics'

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

						SCREAMING_SNAKE_CASE__									= max_fine_history_length
						SCREAMING_SNAKE_CASE__									= max_fine_input_length
						SCREAMING_SNAKE_CASE__									= n_fine_codebooks





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Tuple   						=							'bark'
			lowerCamelCase__				:						Dict   						=							True

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

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

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

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

						SCREAMING_SNAKE_CASE__									= sample_rate
						SCREAMING_SNAKE_CASE__									= codebook_size

			@classmethod
			def    SCREAMING_SNAKE_CASE			(   cls			:  int      ,			__UpperCAmelCase			:  BarkSemanticGenerationConfig      ,			__UpperCAmelCase			:  BarkCoarseGenerationConfig      ,			__UpperCAmelCase			:  BarkFineGenerationConfig      ,			**__UpperCAmelCase			:  Optional[int]      ,			)				->      Tuple:
						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			:  Any    )				->      Any:
						SCREAMING_SNAKE_CASE__									= copy.deepcopy(self.__dict__    )

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

						SCREAMING_SNAKE_CASE__									= self.__class__.model_type
						return output


