






from typing import TYPE_CHECKING

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


snake_case    				=		{
    """configuration_rembert""": ["""REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RemBertConfig""", """RemBertOnnxConfig"""]
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case    				=		["""RemBertTokenizer"""]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case    				=		["""RemBertTokenizerFast"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case    				=		[
			    """REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """RemBertForCausalLM""",
			    """RemBertForMaskedLM""",
			    """RemBertForMultipleChoice""",
			    """RemBertForQuestionAnswering""",
			    """RemBertForSequenceClassification""",
			    """RemBertForTokenClassification""",
			    """RemBertLayer""",
			    """RemBertModel""",
			    """RemBertPreTrainedModel""",
			    """load_tf_weights_in_rembert""",
			]


try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case    				=		[
			    """TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """TFRemBertForCausalLM""",
			    """TFRemBertForMaskedLM""",
			    """TFRemBertForMultipleChoice""",
			    """TFRemBertForQuestionAnswering""",
			    """TFRemBertForSequenceClassification""",
			    """TFRemBertForTokenClassification""",
			    """TFRemBertLayer""",
			    """TFRemBertModel""",
			    """TFRemBertPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_rembert import RemBertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_rembert_fast import RemBertTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_rembert import (
						    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RemBertForCausalLM,
						    RemBertForMaskedLM,
						    RemBertForMultipleChoice,
						    RemBertForQuestionAnswering,
						    RemBertForSequenceClassification,
						    RemBertForTokenClassification,
						    RemBertLayer,
						    RemBertModel,
						    RemBertPreTrainedModel,
						    load_tf_weights_in_rembert,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_rembert import (
						    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFRemBertForCausalLM,
						    TFRemBertForMaskedLM,
						    TFRemBertForMultipleChoice,
						    TFRemBertForQuestionAnswering,
						    TFRemBertForSequenceClassification,
						    TFRemBertForTokenClassification,
						    TFRemBertLayer,
						    TFRemBertModel,
						    TFRemBertPreTrainedModel,
						)


else:
			import sys

			snake_case    				=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)














from typing import TYPE_CHECKING

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


snake_case    				=		{"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """SwinForImageClassification""",
       """SwinForMaskedImageModeling""",
       """SwinModel""",
       """SwinPreTrainedModel""",
       """SwinBackbone""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFSwinForImageClassification""",
       """TFSwinForMaskedImageModeling""",
       """TFSwinModel""",
       """TFSwinPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_swin import (
          SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          SwinBackbone,
          SwinForImageClassification,
          SwinForMaskedImageModeling,
          SwinModel,
          SwinPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_swin import (
          TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFSwinForImageClassification,
          TFSwinForMaskedImageModeling,
          TFSwinModel,
          TFSwinPreTrainedModel,
      )

else:
   import sys

   snake_case    				=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






