





from typing import TYPE_CHECKING

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


__UpperCamelCase:	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:
			__UpperCamelCase:	int = ["""FunnelTokenizerFast"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:	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:
			__UpperCamelCase:	str = [
			    """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

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









from ..utils import DummyObject, requires_backends


class       __lowerCAmelCase					(       metaclass=_UpperCamelCase      ):


	'''simple docstring'''





	_A													=				["note_seq"]

	def __init__(						self:						int,					*lowerCamelCase_:						Optional[int],					**lowerCamelCase_:						Tuple			):
			requires_backends(self,					['note_seq']			)

	@classmethod
	def    snake_case__(						cls:						Optional[Any],					*lowerCamelCase_:						Tuple,					**lowerCamelCase_:						Optional[Any]			):
			requires_backends(cls,					['note_seq']			)





	@classmethod
	def    snake_case__(						cls:						Any,					*lowerCamelCase_:						Dict,					**lowerCamelCase_:						str			):
			requires_backends(cls,					['note_seq']			)


