


'''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_  :				Optional[Any]						= {'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}

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

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

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_  :				List[str]						= [
         '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_  :				List[Any]						= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


lowerCamelCase__ :   List[str]					=							logging.get_logger(__name__)





class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 def __init__(							self  :		str				,				*_lowerCAmelCase  :		Optional[Any]				,				**_lowerCAmelCase  :		Optional[Any]						):
       warnings.warn(
           'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
           ' Please use ImageGPTImageProcessor instead.'				,				_lowerCAmelCase				,				)
       super().__init__(*_lowerCAmelCase				,				**_lowerCAmelCase						)