

"""simple docstring"""
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


A									=						{
    """configuration_ernie_m""": ["""ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieMConfig"""],
}

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 A									=						[
     """ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """ErnieMForMultipleChoice""",
     """ErnieMForQuestionAnswering""",
     """ErnieMForSequenceClassification""",
     """ErnieMForTokenClassification""",
     """ErnieMModel""",
     """ErnieMPreTrainedModel""",
     """ErnieMForInformationExtraction""",
 ]


if TYPE_CHECKING:
 from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_ernie_m import ErnieMTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_ernie_m import (
      ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
      ErnieMForInformationExtraction,
      ErnieMForMultipleChoice,
      ErnieMForQuestionAnswering,
      ErnieMForSequenceClassification,
      ErnieMForTokenClassification,
      ErnieMModel,
      ErnieMPreTrainedModel,
  )


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


A									=						{
    """configuration_imagegpt""": ["""IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ImageGPTConfig""", """ImageGPTOnnxConfig"""]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 A									=						["""ImageGPTFeatureExtractor"""]
 A									=						["""ImageGPTImageProcessor"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 A									=						[
     """IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """ImageGPTForCausalImageModeling""",
     """ImageGPTForImageClassification""",
     """ImageGPTModel""",
     """ImageGPTPreTrainedModel""",
     """load_tf_weights_in_imagegpt""",
 ]


if TYPE_CHECKING:
 from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
  from .image_processing_imagegpt import ImageGPTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_imagegpt import (
      IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ImageGPTForCausalImageModeling,
      ImageGPTForImageClassification,
      ImageGPTModel,
      ImageGPTPreTrainedModel,
      load_tf_weights_in_imagegpt,
  )

else:
 import sys

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