






"""simple docstring"""





from typing import TYPE_CHECKING

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


__lowerCamelCase							=  {
    'configuration_roberta_prelayernorm': [
        'ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'RobertaPreLayerNormConfig',
        'RobertaPreLayerNormOnnxConfig',
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCamelCase							=  [
							    'ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'RobertaPreLayerNormForCausalLM',
							    'RobertaPreLayerNormForMaskedLM',
							    'RobertaPreLayerNormForMultipleChoice',
							    'RobertaPreLayerNormForQuestionAnswering',
							    'RobertaPreLayerNormForSequenceClassification',
							    'RobertaPreLayerNormForTokenClassification',
							    'RobertaPreLayerNormModel',
							    'RobertaPreLayerNormPreTrainedModel',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCamelCase							=  [
							    'TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFRobertaPreLayerNormForCausalLM',
							    'TFRobertaPreLayerNormForMaskedLM',
							    'TFRobertaPreLayerNormForMultipleChoice',
							    'TFRobertaPreLayerNormForQuestionAnswering',
							    'TFRobertaPreLayerNormForSequenceClassification',
							    'TFRobertaPreLayerNormForTokenClassification',
							    'TFRobertaPreLayerNormMainLayer',
							    'TFRobertaPreLayerNormModel',
							    'TFRobertaPreLayerNormPreTrainedModel',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCamelCase							=  [
							    'FlaxRobertaPreLayerNormForCausalLM',
							    'FlaxRobertaPreLayerNormForMaskedLM',
							    'FlaxRobertaPreLayerNormForMultipleChoice',
							    'FlaxRobertaPreLayerNormForQuestionAnswering',
							    'FlaxRobertaPreLayerNormForSequenceClassification',
							    'FlaxRobertaPreLayerNormForTokenClassification',
							    'FlaxRobertaPreLayerNormModel',
							    'FlaxRobertaPreLayerNormPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_roberta_prelayernorm import (
							    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    RobertaPreLayerNormConfig,
							    RobertaPreLayerNormOnnxConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_roberta_prelayernorm import (
														    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RobertaPreLayerNormForCausalLM,
														    RobertaPreLayerNormForMaskedLM,
														    RobertaPreLayerNormForMultipleChoice,
														    RobertaPreLayerNormForQuestionAnswering,
														    RobertaPreLayerNormForSequenceClassification,
														    RobertaPreLayerNormForTokenClassification,
														    RobertaPreLayerNormModel,
														    RobertaPreLayerNormPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_roberta_prelayernorm import (
														    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRobertaPreLayerNormForCausalLM,
														    TFRobertaPreLayerNormForMaskedLM,
														    TFRobertaPreLayerNormForMultipleChoice,
														    TFRobertaPreLayerNormForQuestionAnswering,
														    TFRobertaPreLayerNormForSequenceClassification,
														    TFRobertaPreLayerNormForTokenClassification,
														    TFRobertaPreLayerNormMainLayer,
														    TFRobertaPreLayerNormModel,
														    TFRobertaPreLayerNormPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_roberta_prelayernorm import (
														    FlaxRobertaPreLayerNormForCausalLM,
														    FlaxRobertaPreLayerNormForMaskedLM,
														    FlaxRobertaPreLayerNormForMultipleChoice,
														    FlaxRobertaPreLayerNormForQuestionAnswering,
														    FlaxRobertaPreLayerNormForSequenceClassification,
														    FlaxRobertaPreLayerNormForTokenClassification,
														    FlaxRobertaPreLayerNormModel,
														    FlaxRobertaPreLayerNormPreTrainedModel,
														)

else:
							import sys

							__lowerCamelCase							=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)










from typing import TYPE_CHECKING

from ...utils import _LazyModule


__lowerCamelCase			:	str					    =	{"""tokenization_byt5""": ["""ByT5Tokenizer"""]}


if TYPE_CHECKING:
    from .tokenization_byta import ByTaTokenizer
else:
    import sys

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