


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A	:		Optional[Any]    =       {'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A	:		Union[str, Any]    =       [
	    '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

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





from collections import OrderedDict
from typing import Mapping

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


A	:		Union[str, Any]    =       logging.get_logger(__name__)

A	:		int    =       {
    'andreasmadsen/efficient_mlm_m0.40': (
        'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
    ),
}



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			'''roberta-prelayernorm'''

					def __init__(self   :						Dict				, _UpperCAmelCase   :						List[Any]=5_0265				, _UpperCAmelCase   :						List[Any]=768				, _UpperCAmelCase   :						str=12				, _UpperCAmelCase   :						Any=12				, _UpperCAmelCase   :						Any=3072				, _UpperCAmelCase   :						int="gelu"				, _UpperCAmelCase   :						Tuple=0.1				, _UpperCAmelCase   :						Tuple=0.1				, _UpperCAmelCase   :						Any=512				, _UpperCAmelCase   :						Dict=2				, _UpperCAmelCase   :						int=0.02				, _UpperCAmelCase   :						List[Any]=1E-1_2				, _UpperCAmelCase   :						Tuple=1				, _UpperCAmelCase   :						int=0				, _UpperCAmelCase   :						int=2				, _UpperCAmelCase   :						Optional[Any]="absolute"				, _UpperCAmelCase   :						int=True				, _UpperCAmelCase   :						Optional[int]=None				, **_UpperCAmelCase   :						Any				, )						->       List[str]:




							"""simple docstring"""


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

							lowercase__					      =		vocab_size
							lowercase__					      =		hidden_size
							lowercase__					      =		num_hidden_layers
							lowercase__					      =		num_attention_heads
							lowercase__					      =		hidden_act
							lowercase__					      =		intermediate_size
							lowercase__					      =		hidden_dropout_prob
							lowercase__					      =		attention_probs_dropout_prob
							lowercase__					      =		max_position_embeddings
							lowercase__					      =		type_vocab_size
							lowercase__					      =		initializer_range
							lowercase__					      =		layer_norm_eps
							lowercase__					      =		position_embedding_type
							lowercase__					      =		use_cache
							lowercase__					      =		classifier_dropout






class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					@property
					def   lowerCamelCase__					(self   :						Any		)						->       Mapping[str, Mapping[int, str]]:




							"""simple docstring"""


							if self.task == "multiple-choice":
									lowercase__					      =		{0: """batch""", 1: """choice""", 2: """sequence"""}
							else:
									lowercase__					      =		{0: """batch""", 1: """sequence"""}
							return OrderedDict(
							    [
							        ("""input_ids""", dynamic_axis),
							        ("""attention_mask""", dynamic_axis),
							    ]		)

