



import os
import sys

_lowercase						  =		os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


_lowercase						  =		[
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]



@add_start_docstrings(AutoConfig.__doc__	)
def 		lowerCAmelCase__    (					*UpperCamelCase_				:       Dict ,       **UpperCamelCase_				:       Tuple	)->  Optional[Any]:
    return AutoConfig.from_pretrained(*UpperCamelCase_ ,       **UpperCamelCase_	)



@add_start_docstrings(AutoTokenizer.__doc__	)
def 		lowerCAmelCase__    (					*UpperCamelCase_				:       int ,       **UpperCamelCase_				:       Optional[int]	)->  str:
    return AutoTokenizer.from_pretrained(*UpperCamelCase_ ,       **UpperCamelCase_	)



@add_start_docstrings(AutoModel.__doc__	)
def 		lowerCAmelCase__    (					*UpperCamelCase_				:       List[Any] ,       **UpperCamelCase_				:       List[Any]	)->  List[Any]:
    return AutoModel.from_pretrained(*UpperCamelCase_ ,       **UpperCamelCase_	)



@add_start_docstrings(AutoModelForCausalLM.__doc__	)
def 		lowerCAmelCase__    (					*UpperCamelCase_				:       int ,       **UpperCamelCase_				:       Dict	)->  Any:
    return AutoModelForCausalLM.from_pretrained(*UpperCamelCase_ ,       **UpperCamelCase_	)



@add_start_docstrings(AutoModelForMaskedLM.__doc__	)
def 		lowerCAmelCase__    (					*UpperCamelCase_				:       Optional[int] ,       **UpperCamelCase_				:       str	)->  List[Any]:
    return AutoModelForMaskedLM.from_pretrained(*UpperCamelCase_ ,       **UpperCamelCase_	)



@add_start_docstrings(AutoModelForSequenceClassification.__doc__	)
def 		lowerCAmelCase__    (					*UpperCamelCase_				:       Any ,       **UpperCamelCase_				:       Optional[Any]	)->  Optional[Any]:
    return AutoModelForSequenceClassification.from_pretrained(*UpperCamelCase_ ,       **UpperCamelCase_	)



@add_start_docstrings(AutoModelForQuestionAnswering.__doc__	)
def 		lowerCAmelCase__    (					*UpperCamelCase_				:       Any ,       **UpperCamelCase_				:       Any	)->  int:
    return AutoModelForQuestionAnswering.from_pretrained(*UpperCamelCase_ ,       **UpperCamelCase_	)








from typing import TYPE_CHECKING

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


_lowercase						  =		{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase						  =		["MobileViTFeatureExtractor"]
   _lowercase						  =		["MobileViTImageProcessor"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase						  =		[
       "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "MobileViTForImageClassification",
       "MobileViTForSemanticSegmentation",
       "MobileViTModel",
       "MobileViTPreTrainedModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase						  =		[
       "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFMobileViTForImageClassification",
       "TFMobileViTForSemanticSegmentation",
       "TFMobileViTModel",
       "TFMobileViTPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_mobilevit import MobileViTFeatureExtractor
      from .image_processing_mobilevit import MobileViTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mobilevit import (
          MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          MobileViTForImageClassification,
          MobileViTForSemanticSegmentation,
          MobileViTModel,
          MobileViTPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_mobilevit import (
          TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFMobileViTForImageClassification,
          TFMobileViTForSemanticSegmentation,
          TFMobileViTModel,
          TFMobileViTPreTrainedModel,
      )


else:
   import sys

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