

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__       =	logging.get_logger(__name__)

UpperCAmelCase__       =	{
    "MIT/ast-finetuned-audioset-10-10-0.4593": (
        "https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"
    ),
}







class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					'''audio-spectrogram-transformer'''



			def __init__(						self       :  Union[str, Any]			,		A       :  str=7_68			,		A       :  Tuple=12			,		A       :  Dict=12			,		A       :  Union[str, Any]=30_72			,		A       :  int="gelu"			,		A       :  Optional[int]=0.0			,		A       :  Union[str, Any]=0.0			,		A       :  Dict=0.0_2			,		A       :  List[str]=1E-12			,		A       :  int=16			,		A       :  int=True			,		A       :  List[str]=10			,		A       :  Any=10			,		A       :  List[Any]=10_24			,		A       :  Optional[int]=1_28			,		**A       :  Optional[int]			,		)       ->   Any:



						"""simple docstring"""






						super().__init__(**A)

						_UpperCAmelCase			=       hidden_size
						_UpperCAmelCase			=       num_hidden_layers
						_UpperCAmelCase			=       num_attention_heads
						_UpperCAmelCase			=       intermediate_size
						_UpperCAmelCase			=       hidden_act
						_UpperCAmelCase			=       hidden_dropout_prob
						_UpperCAmelCase			=       attention_probs_dropout_prob
						_UpperCAmelCase			=       initializer_range
						_UpperCAmelCase			=       layer_norm_eps
						_UpperCAmelCase			=       patch_size
						_UpperCAmelCase			=       qkv_bias
						_UpperCAmelCase			=       frequency_stride
						_UpperCAmelCase			=       time_stride
						_UpperCAmelCase			=       max_length
						_UpperCAmelCase			=       num_mel_bins





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase__       =	{
    "configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
    "tokenization_deberta": ["DebertaTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	["DebertaTokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "DebertaForMaskedLM",
							    "DebertaForQuestionAnswering",
							    "DebertaForSequenceClassification",
							    "DebertaForTokenClassification",
							    "DebertaModel",
							    "DebertaPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFDebertaForMaskedLM",
							    "TFDebertaForQuestionAnswering",
							    "TFDebertaForSequenceClassification",
							    "TFDebertaForTokenClassification",
							    "TFDebertaModel",
							    "TFDebertaPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
							from .tokenization_deberta import DebertaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_deberta_fast import DebertaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_deberta import (
														    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DebertaForMaskedLM,
														    DebertaForQuestionAnswering,
														    DebertaForSequenceClassification,
														    DebertaForTokenClassification,
														    DebertaModel,
														    DebertaPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_deberta import (
														    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDebertaForMaskedLM,
														    TFDebertaForQuestionAnswering,
														    TFDebertaForSequenceClassification,
														    TFDebertaForTokenClassification,
														    TFDebertaModel,
														    TFDebertaPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
