

"""simple docstring"""




from typing import TYPE_CHECKING

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


__A     :    Any    =		{
    'configuration_deberta_v2': ['DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaV2Config', 'DebertaV2OnnxConfig'],
    'tokenization_deberta_v2': ['DebertaV2Tokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A     :    Any    =		['DebertaV2TokenizerFast']

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A     :    Any    =		[
	    'TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFDebertaV2ForMaskedLM',
	    'TFDebertaV2ForQuestionAnswering',
	    'TFDebertaV2ForSequenceClassification',
	    'TFDebertaV2ForTokenClassification',
	    'TFDebertaV2Model',
	    'TFDebertaV2PreTrainedModel',
	]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A     :    List[Any]    =		[
	    'DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'DebertaV2ForMaskedLM',
	    'DebertaV2ForMultipleChoice',
	    'DebertaV2ForQuestionAnswering',
	    'DebertaV2ForSequenceClassification',
	    'DebertaV2ForTokenClassification',
	    'DebertaV2Model',
	    'DebertaV2PreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_deberta_va import (
	    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    DebertaVaConfig,
	    DebertaVaOnnxConfig,
	)
	from .tokenization_deberta_va import DebertaVaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_deberta_va import (
		    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFDebertaVaForMaskedLM,
		    TFDebertaVaForQuestionAnswering,
		    TFDebertaVaForSequenceClassification,
		    TFDebertaVaForTokenClassification,
		    TFDebertaVaModel,
		    TFDebertaVaPreTrainedModel,
		)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_deberta_va import (
		    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DebertaVaForMaskedLM,
		    DebertaVaForMultipleChoice,
		    DebertaVaForQuestionAnswering,
		    DebertaVaForSequenceClassification,
		    DebertaVaForTokenClassification,
		    DebertaVaModel,
		    DebertaVaPreTrainedModel,
		)

else:
	import sys

	__A     :    List[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


SCREAMING_SNAKE_CASE						:int      =			{"""configuration_yoso""": ["""YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YosoConfig"""]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE						:List[Any]      =			[
     """YOSO_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """YosoForMaskedLM""",
     """YosoForMultipleChoice""",
     """YosoForQuestionAnswering""",
     """YosoForSequenceClassification""",
     """YosoForTokenClassification""",
     """YosoLayer""",
     """YosoModel""",
     """YosoPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_yoso import (
      YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
      YosoForMaskedLM,
      YosoForMultipleChoice,
      YosoForQuestionAnswering,
      YosoForSequenceClassification,
      YosoForTokenClassification,
      YosoLayer,
      YosoModel,
      YosoPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE						:str      =			_LazyModule(__name__, globals()["""__file__"""], _import_structure)
