




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_		:				Dict									=     {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_		:				Optional[int]									=     [
							    "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "WavLMForAudioFrameClassification",
							    "WavLMForCTC",
							    "WavLMForSequenceClassification",
							    "WavLMForXVector",
							    "WavLMModel",
							    "WavLMPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_wavlm import (
														    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    WavLMForAudioFrameClassification,
														    WavLMForCTC,
														    WavLMForSequenceClassification,
														    WavLMForXVector,
														    WavLMModel,
														    WavLMPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase_		:				Union[str, Any]									=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)










from ... import PretrainedConfig


UpperCAmelCase_		:				List[str]									=     {
    "sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
						UpperCamelCase     						=					"""nezha"""

						def __init__(		self   :List[Any]    , __snake_case   :Optional[int]=2_11_28    , __snake_case   :Dict=7_68    , __snake_case   :str=12    , __snake_case   :List[Any]=12    , __snake_case   :Optional[int]=30_72    , __snake_case   :Any="gelu"    , __snake_case   :List[str]=0.1    , __snake_case   :Optional[int]=0.1    , __snake_case   :Dict=5_12    , __snake_case   :Optional[int]=64    , __snake_case   :Any=2    , __snake_case   :List[Any]=0.02    , __snake_case   :List[str]=1E-12    , __snake_case   :Any=0.1    , __snake_case   :str=0    , __snake_case   :int=2    , __snake_case   :str=3    , __snake_case   :Any=True    , **__snake_case   :Any    , ):
										'''simple docstring'''

										super().__init__(pad_token_id=__snake_case    , bos_token_id=__snake_case    , eos_token_id=__snake_case    , **__snake_case )

										__magic_name__	:	Tuple								=vocab_size
										__magic_name__	:	str								=hidden_size
										__magic_name__	:	Dict								=num_hidden_layers
										__magic_name__	:	str								=num_attention_heads
										__magic_name__	:	int								=hidden_act
										__magic_name__	:	Optional[int]								=intermediate_size
										__magic_name__	:	Union[str, Any]								=hidden_dropout_prob
										__magic_name__	:	Any								=attention_probs_dropout_prob
										__magic_name__	:	Union[str, Any]								=max_position_embeddings
										__magic_name__	:	str								=max_relative_position
										__magic_name__	:	Tuple								=type_vocab_size
										__magic_name__	:	str								=initializer_range
										__magic_name__	:	Tuple								=layer_norm_eps
										__magic_name__	:	Optional[int]								=classifier_dropout
										__magic_name__	:	List[str]								=use_cache




