

from typing import TYPE_CHECKING

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


_lowerCAmelCase :	Optional[Any]     				=    {'configuration_regnet': ['REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RegNetConfig']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCAmelCase :	int     				=    [
         'REGNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'RegNetForImageClassification',
         'RegNetModel',
         'RegNetPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCAmelCase :	Optional[Any]     				=    [
         'TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFRegNetForImageClassification',
         'TFRegNetModel',
         'TFRegNetPreTrainedModel',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCAmelCase :	str     				=    [
         'FlaxRegNetForImageClassification',
         'FlaxRegNetModel',
         'FlaxRegNetPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_regnet import (
              REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              RegNetForImageClassification,
              RegNetModel,
              RegNetPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_regnet import (
              TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFRegNetForImageClassification,
              TFRegNetModel,
              TFRegNetPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_regnet import (
              FlaxRegNetForImageClassification,
              FlaxRegNetModel,
              FlaxRegNetPreTrainedModel,
          )


else:
     import sys

     _lowerCAmelCase :	Union[str, Any]     				=    _LazyModule(__name__, globals()['__file__'], _import_structure)







from typing import TYPE_CHECKING

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


_lowerCAmelCase :	Optional[Any]     				=    {
    'configuration_xlm_prophetnet': ['XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMProphetNetConfig'],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCAmelCase :	Optional[Any]     				=    ['XLMProphetNetTokenizer']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCAmelCase :	List[str]     				=    [
         'XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'XLMProphetNetDecoder',
         'XLMProphetNetEncoder',
         'XLMProphetNetForCausalLM',
         'XLMProphetNetForConditionalGeneration',
         'XLMProphetNetModel',
         'XLMProphetNetPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xlm_prophetnet import (
              XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              XLMProphetNetDecoder,
              XLMProphetNetEncoder,
              XLMProphetNetForCausalLM,
              XLMProphetNetForConditionalGeneration,
              XLMProphetNetModel,
              XLMProphetNetPreTrainedModel,
          )

else:
     import sys

     _lowerCAmelCase :	List[Any]     				=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




