





from typing import TYPE_CHECKING

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


UpperCamelCase__ :			Any       	=			{
    """configuration_flaubert""": ["""FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FlaubertConfig""", """FlaubertOnnxConfig"""],
    """tokenization_flaubert""": ["""FlaubertTokenizer"""],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__ :			List[Any]       	=			[
				    """FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """FlaubertForMultipleChoice""",
				    """FlaubertForQuestionAnswering""",
				    """FlaubertForQuestionAnsweringSimple""",
				    """FlaubertForSequenceClassification""",
				    """FlaubertForTokenClassification""",
				    """FlaubertModel""",
				    """FlaubertWithLMHeadModel""",
				    """FlaubertPreTrainedModel""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__ :			int       	=			[
				    """TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFFlaubertForMultipleChoice""",
				    """TFFlaubertForQuestionAnsweringSimple""",
				    """TFFlaubertForSequenceClassification""",
				    """TFFlaubertForTokenClassification""",
				    """TFFlaubertModel""",
				    """TFFlaubertPreTrainedModel""",
				    """TFFlaubertWithLMHeadModel""",
				]


if TYPE_CHECKING:
				from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
				from .tokenization_flaubert import FlaubertTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flaubert import (
								    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    FlaubertForMultipleChoice,
								    FlaubertForQuestionAnswering,
								    FlaubertForQuestionAnsweringSimple,
								    FlaubertForSequenceClassification,
								    FlaubertForTokenClassification,
								    FlaubertModel,
								    FlaubertPreTrainedModel,
								    FlaubertWithLMHeadModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_flaubert import (
								    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFFlaubertForMultipleChoice,
								    TFFlaubertForQuestionAnsweringSimple,
								    TFFlaubertForSequenceClassification,
								    TFFlaubertForTokenClassification,
								    TFFlaubertModel,
								    TFFlaubertPreTrainedModel,
								    TFFlaubertWithLMHeadModel,
								)

else:
				import sys

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









from typing import TYPE_CHECKING

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


UpperCamelCase__ :			Any       	=			{
    """configuration_splinter""": ["""SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SplinterConfig"""],
    """tokenization_splinter""": ["""SplinterTokenizer"""],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__ :			Any       	=			["""SplinterTokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__ :			Union[str, Any]       	=			[
				    """SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """SplinterForQuestionAnswering""",
				    """SplinterForPreTraining""",
				    """SplinterLayer""",
				    """SplinterModel""",
				    """SplinterPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
				from .tokenization_splinter import SplinterTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_splinter_fast import SplinterTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_splinter import (
								    SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SplinterForPreTraining,
								    SplinterForQuestionAnswering,
								    SplinterLayer,
								    SplinterModel,
								    SplinterPreTrainedModel,
								)


else:
				import sys

				UpperCamelCase__ :			List[str]       	=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


