




"""simple docstring"""


from typing import TYPE_CHECKING

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


A_      =    {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      =    [
         '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''OPTForCausalLM''',
         '''OPTModel''',
         '''OPTPreTrainedModel''',
         '''OPTForSequenceClassification''',
         '''OPTForQuestionAnswering''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      =    ['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      =    [
         '''FlaxOPTForCausalLM''',
         '''FlaxOPTModel''',
         '''FlaxOPTPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_opt import (
              OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              OPTForCausalLM,
              OPTForQuestionAnswering,
              OPTForSequenceClassification,
              OPTModel,
              OPTPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

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_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
    '''configuration_maskformer_swin''': ['''MaskFormerSwinConfig'''],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      =    ['''MaskFormerFeatureExtractor''']
     A_      =    ['''MaskFormerImageProcessor''']


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      =    [
         '''MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''MaskFormerForInstanceSegmentation''',
         '''MaskFormerModel''',
         '''MaskFormerPreTrainedModel''',
     ]
     A_      =    [
         '''MaskFormerSwinBackbone''',
         '''MaskFormerSwinModel''',
         '''MaskFormerSwinPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
     from .configuration_maskformer_swin import MaskFormerSwinConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_maskformer import MaskFormerFeatureExtractor
          from .image_processing_maskformer import MaskFormerImageProcessor
     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_maskformer import (
              MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              MaskFormerForInstanceSegmentation,
              MaskFormerModel,
              MaskFormerPreTrainedModel,
          )
          from .modeling_maskformer_swin import (
              MaskFormerSwinBackbone,
              MaskFormerSwinModel,
              MaskFormerSwinPreTrainedModel,
          )


else:
     import sys

     A_      =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)


