'''simple docstring'''




from typing import TYPE_CHECKING

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


_A										= {
    'configuration_camembert': ['CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CamembertConfig', 'CamembertOnnxConfig'],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A										= ['CamembertTokenizer']

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A										= ['CamembertTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A										= [
     'CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'CamembertForCausalLM',
     'CamembertForMaskedLM',
     'CamembertForMultipleChoice',
     'CamembertForQuestionAnswering',
     'CamembertForSequenceClassification',
     'CamembertForTokenClassification',
     'CamembertModel',
     'CamembertPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A										= [
     'TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFCamembertForCausalLM',
     'TFCamembertForMaskedLM',
     'TFCamembertForMultipleChoice',
     'TFCamembertForQuestionAnswering',
     'TFCamembertForSequenceClassification',
     'TFCamembertForTokenClassification',
     'TFCamembertModel',
     'TFCamembertPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_camembert import CamembertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_camembert_fast import CamembertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_camembert import (
      CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      CamembertForCausalLM,
      CamembertForMaskedLM,
      CamembertForMultipleChoice,
      CamembertForQuestionAnswering,
      CamembertForSequenceClassification,
      CamembertForTokenClassification,
      CamembertModel,
      CamembertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_camembert import (
      TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFCamembertForCausalLM,
      TFCamembertForMaskedLM,
      TFCamembertForMultipleChoice,
      TFCamembertForQuestionAnswering,
      TFCamembertForSequenceClassification,
      TFCamembertForTokenClassification,
      TFCamembertModel,
      TFCamembertPreTrainedModel,
  )

else:
 import sys

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








'''simple docstring'''

from typing import TYPE_CHECKING

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


lowercase__					  =		{
    "configuration_swin2sr": ["SWIN2SR_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swin2SRConfig"],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__					  =		[
       "SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST",
       "Swin2SRForImageSuperResolution",
       "Swin2SRModel",
       "Swin2SRPreTrainedModel",
   ]


try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__					  =		["Swin2SRImageProcessor"]


if TYPE_CHECKING:
   from .configuration_swinasr import SWIN2SR_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinaSRConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_swinasr import (
          SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST,
          SwinaSRForImageSuperResolution,
          SwinaSRModel,
          SwinaSRPreTrainedModel,
      )

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_swinasr import SwinaSRImageProcessor


else:
   import sys

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

