


import os
import sys

UpperCAmelCase      =   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,
)


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






@add_start_docstrings(AutoConfig.__doc__ )
def 	__lowerCAmelCase      (*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )->  List[Any]:




      """simple docstring"""

      return AutoConfig.from_pretrained(*__lowerCAmelCase , **__lowerCAmelCase )






@add_start_docstrings(AutoTokenizer.__doc__ )
def 	__lowerCAmelCase      (*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )->  Tuple:




      """simple docstring"""

      return AutoTokenizer.from_pretrained(*__lowerCAmelCase , **__lowerCAmelCase )






@add_start_docstrings(AutoModel.__doc__ )
def 	__lowerCAmelCase      (*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )->  Any:




      """simple docstring"""

      return AutoModel.from_pretrained(*__lowerCAmelCase , **__lowerCAmelCase )






@add_start_docstrings(AutoModelForCausalLM.__doc__ )
def 	__lowerCAmelCase      (*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )->  Union[str, Any]:




      """simple docstring"""

      return AutoModelForCausalLM.from_pretrained(*__lowerCAmelCase , **__lowerCAmelCase )






@add_start_docstrings(AutoModelForMaskedLM.__doc__ )
def 	__lowerCAmelCase      (*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )->  str:




      """simple docstring"""

      return AutoModelForMaskedLM.from_pretrained(*__lowerCAmelCase , **__lowerCAmelCase )






@add_start_docstrings(AutoModelForSequenceClassification.__doc__ )
def 	__lowerCAmelCase      (*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )->  str:




      """simple docstring"""

      return AutoModelForSequenceClassification.from_pretrained(*__lowerCAmelCase , **__lowerCAmelCase )






@add_start_docstrings(AutoModelForQuestionAnswering.__doc__ )
def 	__lowerCAmelCase      (*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )->  Optional[Any]:




      """simple docstring"""

      return AutoModelForQuestionAnswering.from_pretrained(*__lowerCAmelCase , **__lowerCAmelCase )



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase      =   {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase      =   [
       """VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """ViTMSNModel""",
       """ViTMSNForImageClassification""",
       """ViTMSNPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit_msn import (
          VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTMSNForImageClassification,
          ViTMSNModel,
          ViTMSNPreTrainedModel,
      )

else:
   import sys

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