

from typing import TYPE_CHECKING

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


UpperCamelCase__	:   Any								=     {"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__	:   int								=     [
           "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
           "GPTJForCausalLM",
           "GPTJForQuestionAnswering",
           "GPTJForSequenceClassification",
           "GPTJModel",
           "GPTJPreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__	:   Union[str, Any]								=     [
           "TFGPTJForCausalLM",
           "TFGPTJForQuestionAnswering",
           "TFGPTJForSequenceClassification",
           "TFGPTJModel",
           "TFGPTJPreTrainedModel",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__	:   List[str]								=     [
           "FlaxGPTJForCausalLM",
           "FlaxGPTJModel",
           "FlaxGPTJPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gptj import (
                  GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTJForCausalLM,
                  GPTJForQuestionAnswering,
                  GPTJForSequenceClassification,
                  GPTJModel,
                  GPTJPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_gptj import (
                  TFGPTJForCausalLM,
                  TFGPTJForQuestionAnswering,
                  TFGPTJForSequenceClassification,
                  TFGPTJModel,
                  TFGPTJPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
       import sys

       UpperCamelCase__	:   Any								=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





import warnings

from ...utils import logging
from .image_processing_vilt import ViltImageProcessor


UpperCamelCase__	:   Optional[Any]								=     logging.get_logger(__name__)



class    __snake_case	(     lowerCAmelCase__							):

  def __init__(       self    ,		*_A    ,		**_A):
      warnings.warn(
          'The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
          ' use ViltImageProcessor instead.'    ,		_A    ,		)
      super().__init__(*_A    ,		**_A)


