




'''simple docstring'''

from typing import TYPE_CHECKING

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


a__         =     {
    '''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__         =     ['''DebertaV2TokenizerFast''']

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''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__         =     [
		    '''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__         =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






from typing import TYPE_CHECKING

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


UpperCAmelCase__    :      Optional[Any]       					={
    '''configuration_rwkv''': ['''RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RwkvConfig''', '''RwkvOnnxConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      Optional[Any]       					=[
							    '''RWKV_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''RwkvForCausalLM''',
							    '''RwkvModel''',
							    '''RwkvPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_rwkv import (
														    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RwkvForCausalLM,
														    RwkvModel,
														    RwkvPreTrainedModel,
														)
else:
							import sys

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





