
"""simple docstring"""




from typing import TYPE_CHECKING

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


_A      :	Tuple       		=	{
    """configuration_openai""": ["""OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenAIGPTConfig"""],
    """tokenization_openai""": ["""OpenAIGPTTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A      :	Tuple       		=	["""OpenAIGPTTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A      :	Tuple       		=	[
       """OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """OpenAIGPTDoubleHeadsModel""",
       """OpenAIGPTForSequenceClassification""",
       """OpenAIGPTLMHeadModel""",
       """OpenAIGPTModel""",
       """OpenAIGPTPreTrainedModel""",
       """load_tf_weights_in_openai_gpt""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A      :	Union[str, Any]       		=	[
       """TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFOpenAIGPTDoubleHeadsModel""",
       """TFOpenAIGPTForSequenceClassification""",
       """TFOpenAIGPTLMHeadModel""",
       """TFOpenAIGPTMainLayer""",
       """TFOpenAIGPTModel""",
       """TFOpenAIGPTPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
   from .tokenization_openai import OpenAIGPTTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_openai_fast import OpenAIGPTTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_openai import (
          OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          OpenAIGPTDoubleHeadsModel,
          OpenAIGPTForSequenceClassification,
          OpenAIGPTLMHeadModel,
          OpenAIGPTModel,
          OpenAIGPTPreTrainedModel,
          load_tf_weights_in_openai_gpt,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_openai import (
          TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFOpenAIGPTDoubleHeadsModel,
          TFOpenAIGPTForSequenceClassification,
          TFOpenAIGPTLMHeadModel,
          TFOpenAIGPTMainLayer,
          TFOpenAIGPTModel,
          TFOpenAIGPTPreTrainedModel,
      )

else:
   import sys

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




"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


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

class 		a__						(				a_						):
   def __init__(      self     ,   *_a     ,   **_a    ):
          warnings.warn(
              "The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
              " Please use ConvNextImageProcessor instead."     ,   _a     ,   )
          super().__init__(*_a     ,   **_a    )


