




from typing import TYPE_CHECKING

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


a_				:    Union[str, Any]										=     {'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLNetConfig''']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a_				:    Optional[int]										=     ['''XLNetTokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a_				:    List[str]										=     ['''XLNetTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a_				:    Union[str, Any]										=     [
					    '''XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''XLNetForMultipleChoice''',
					    '''XLNetForQuestionAnswering''',
					    '''XLNetForQuestionAnsweringSimple''',
					    '''XLNetForSequenceClassification''',
					    '''XLNetForTokenClassification''',
					    '''XLNetLMHeadModel''',
					    '''XLNetModel''',
					    '''XLNetPreTrainedModel''',
					    '''load_tf_weights_in_xlnet''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a_				:    str										=     [
					    '''TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFXLNetForMultipleChoice''',
					    '''TFXLNetForQuestionAnsweringSimple''',
					    '''TFXLNetForSequenceClassification''',
					    '''TFXLNetForTokenClassification''',
					    '''TFXLNetLMHeadModel''',
					    '''TFXLNetMainLayer''',
					    '''TFXLNetModel''',
					    '''TFXLNetPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xlnet import XLNetTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xlnet_fast import XLNetTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlnet import (
										    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLNetForMultipleChoice,
										    XLNetForQuestionAnswering,
										    XLNetForQuestionAnsweringSimple,
										    XLNetForSequenceClassification,
										    XLNetForTokenClassification,
										    XLNetLMHeadModel,
										    XLNetModel,
										    XLNetPreTrainedModel,
										    load_tf_weights_in_xlnet,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xlnet import (
										    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXLNetForMultipleChoice,
										    TFXLNetForQuestionAnsweringSimple,
										    TFXLNetForSequenceClassification,
										    TFXLNetForTokenClassification,
										    TFXLNetLMHeadModel,
										    TFXLNetMainLayer,
										    TFXLNetModel,
										    TFXLNetPreTrainedModel,
										)

else:
					import sys

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









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

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


a_				:    List[Any]										=     {
    '''configuration_encodec''': [
        '''ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EncodecConfig''',
    ],
    '''feature_extraction_encodec''': ['''EncodecFeatureExtractor'''],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a_				:    int										=     [
					    '''ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''EncodecModel''',
					    '''EncodecPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_encodec import (
					    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    EncodecConfig,
					)
					from .feature_extraction_encodec import EncodecFeatureExtractor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_encodec import (
										    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
										    EncodecModel,
										    EncodecPreTrainedModel,
										)

else:
					import sys

					a_				:    Tuple										=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



