





"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case	:						Tuple					=					logging.get_logger(__name__)

_snake_case	:						int					=					{
    'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json',
}






class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				'''mpnet'''


					def __init__(   self     :str	,			__UpperCamelCase     :Optional[Any]=3_05_27	,			__UpperCamelCase     :Optional[int]=7_68	,			__UpperCamelCase     :Union[str, Any]=12	,			__UpperCamelCase     :List[str]=12	,			__UpperCamelCase     :str=30_72	,			__UpperCamelCase     :List[Any]="gelu"	,			__UpperCamelCase     :str=0.1	,			__UpperCamelCase     :List[Any]=0.1	,			__UpperCamelCase     :Optional[Any]=5_12	,			__UpperCamelCase     :List[str]=0.02	,			__UpperCamelCase     :Optional[Any]=1e-12	,			__UpperCamelCase     :str=32	,			__UpperCamelCase     :Union[str, Any]=1	,			__UpperCamelCase     :List[str]=0	,			__UpperCamelCase     :List[str]=2	,			**__UpperCamelCase     :str	,			):
												super().__init__(pad_token_id=__UpperCamelCase	,			bos_token_id=__UpperCamelCase	,			eos_token_id=__UpperCamelCase	,			**__UpperCamelCase						)

												A						=    vocab_size
												A						=    hidden_size
												A						=    num_hidden_layers
												A						=    num_attention_heads
												A						=    hidden_act
												A						=    intermediate_size
												A						=    hidden_dropout_prob
												A						=    attention_probs_dropout_prob
												A						=    max_position_embeddings
												A						=    initializer_range
												A						=    layer_norm_eps
												A						=    relative_attention_num_buckets









"""simple docstring"""
from typing import TYPE_CHECKING

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


_snake_case	:						Any					=					{
    'configuration_unispeech_sat': ['UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechSatConfig'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_snake_case	:						str					=					[
		    'UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'UniSpeechSatForAudioFrameClassification',
		    'UniSpeechSatForCTC',
		    'UniSpeechSatForPreTraining',
		    'UniSpeechSatForSequenceClassification',
		    'UniSpeechSatForXVector',
		    'UniSpeechSatModel',
		    'UniSpeechSatPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_unispeech_sat import (
				    UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    UniSpeechSatForAudioFrameClassification,
				    UniSpeechSatForCTC,
				    UniSpeechSatForPreTraining,
				    UniSpeechSatForSequenceClassification,
				    UniSpeechSatForXVector,
				    UniSpeechSatModel,
				    UniSpeechSatPreTrainedModel,
				)

else:
		import sys

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


