




'''simple docstring'''





from typing import TYPE_CHECKING

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


lowercase__								=						{'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

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

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase__								=						[
           '''TFGPTJForCausalLM''',
           '''TFGPTJForQuestionAnswering''',
           '''TFGPTJForSequenceClassification''',
           '''TFGPTJModel''',
           '''TFGPTJPreTrainedModel''',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase__								=						[
           '''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

       lowercase__								=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


a__         =     logging.get_logger(__name__)





class __magic_name__(						__lowerCAmelCase      ):






						def __init__(   self						:						List[str]					,   *__UpperCamelCase						:						Tuple					,   **__UpperCamelCase						:						Tuple       ):



									'''simple docstring'''






									warnings.warn(
									    """The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
									    """ use ViTImageProcessor instead."""					,   __UpperCamelCase					,   )
									super().__init__(*__UpperCamelCase					,   **__UpperCamelCase       )