


from typing import TYPE_CHECKING

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


snake_case_						:			str													=							{
    '''configuration_rembert''': ['''REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RemBertConfig''', '''RemBertOnnxConfig''']
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case_						:			Union[str, Any]													=							['''RemBertTokenizer''']

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

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case_						:			Optional[Any]													=							[
							    '''REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''RemBertForCausalLM''',
							    '''RemBertForMaskedLM''',
							    '''RemBertForMultipleChoice''',
							    '''RemBertForQuestionAnswering''',
							    '''RemBertForSequenceClassification''',
							    '''RemBertForTokenClassification''',
							    '''RemBertLayer''',
							    '''RemBertModel''',
							    '''RemBertPreTrainedModel''',
							    '''load_tf_weights_in_rembert''',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case_						:			Optional[int]													=							[
							    '''TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFRemBertForCausalLM''',
							    '''TFRemBertForMaskedLM''',
							    '''TFRemBertForMultipleChoice''',
							    '''TFRemBertForQuestionAnswering''',
							    '''TFRemBertForSequenceClassification''',
							    '''TFRemBertForTokenClassification''',
							    '''TFRemBertLayer''',
							    '''TFRemBertModel''',
							    '''TFRemBertPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_rembert import RemBertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_rembert_fast import RemBertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_rembert import (
														    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RemBertForCausalLM,
														    RemBertForMaskedLM,
														    RemBertForMultipleChoice,
														    RemBertForQuestionAnswering,
														    RemBertForSequenceClassification,
														    RemBertForTokenClassification,
														    RemBertLayer,
														    RemBertModel,
														    RemBertPreTrainedModel,
														    load_tf_weights_in_rembert,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_rembert import (
														    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRemBertForCausalLM,
														    TFRemBertForMaskedLM,
														    TFRemBertForMultipleChoice,
														    TFRemBertForQuestionAnswering,
														    TFRemBertForSequenceClassification,
														    TFRemBertForTokenClassification,
														    TFRemBertLayer,
														    TFRemBertModel,
														    TFRemBertPreTrainedModel,
														)


else:
							import sys

							snake_case_						:			int													=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


snake_case_						:			Optional[Any]													=							{
    '''configuration_audio_spectrogram_transformer''': [
        '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ASTConfig''',
    ]
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case_						:			Optional[Any]													=							[
							    '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''ASTForAudioClassification''',
							    '''ASTModel''',
							    '''ASTPreTrainedModel''',
							]

try:
							if not is_speech_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case_						:			List[Any]													=							['''ASTFeatureExtractor''']

if TYPE_CHECKING:
							from .configuration_audio_spectrogram_transformer import (
							    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    ASTConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_audio_spectrogram_transformer import (
														    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ASTForAudioClassification,
														    ASTModel,
														    ASTPreTrainedModel,
														)

							try:
														if not is_speech_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
							import sys

							snake_case_						:			Optional[Any]													=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)