





'''simple docstring'''






from typing import TYPE_CHECKING

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


A   : Dict             =			{
    'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfig'],
    'convert_funnel_original_tf_checkpoint_to_pytorch': [],
    'tokenization_funnel': ['FunnelTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : str             =			['FunnelTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : Any             =			[
		    'FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'FunnelBaseModel',
		    'FunnelForMaskedLM',
		    'FunnelForMultipleChoice',
		    'FunnelForPreTraining',
		    'FunnelForQuestionAnswering',
		    'FunnelForSequenceClassification',
		    'FunnelForTokenClassification',
		    'FunnelModel',
		    'FunnelPreTrainedModel',
		    'load_tf_weights_in_funnel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : int             =			[
		    'TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFFunnelBaseModel',
		    'TFFunnelForMaskedLM',
		    'TFFunnelForMultipleChoice',
		    'TFFunnelForPreTraining',
		    'TFFunnelForQuestionAnswering',
		    'TFFunnelForSequenceClassification',
		    'TFFunnelForTokenClassification',
		    'TFFunnelModel',
		    'TFFunnelPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
		from .tokenization_funnel import FunnelTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_funnel_fast import FunnelTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_funnel import (
				    FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    FunnelBaseModel,
				    FunnelForMaskedLM,
				    FunnelForMultipleChoice,
				    FunnelForPreTraining,
				    FunnelForQuestionAnswering,
				    FunnelForSequenceClassification,
				    FunnelForTokenClassification,
				    FunnelModel,
				    FunnelPreTrainedModel,
				    load_tf_weights_in_funnel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_funnel import (
				    TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFFunnelBaseModel,
				    TFFunnelForMaskedLM,
				    TFFunnelForMultipleChoice,
				    TFFunnelForPreTraining,
				    TFFunnelForQuestionAnswering,
				    TFFunnelForSequenceClassification,
				    TFFunnelForTokenClassification,
				    TFFunnelModel,
				    TFFunnelPreTrainedModel,
				)

else:
		import sys

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






'''simple docstring'''






from typing import TYPE_CHECKING

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


A   : Dict             =			{'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : int             =			['SpeechEncoderDecoderModel']

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

if TYPE_CHECKING:
		from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
		import sys

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





