
"""simple docstring"""



import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


UpperCamelCase__          =	logging.get_logger(__name__)





class 				a			(					lowerCAmelCase_      ):



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


"""simple docstring"""


from typing import TYPE_CHECKING

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


UpperCAmelCase__			=    {
    """configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__			=    [
        """GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """GPTBigCodeForSequenceClassification""",
        """GPTBigCodeForTokenClassification""",
        """GPTBigCodeForCausalLM""",
        """GPTBigCodeModel""",
        """GPTBigCodePreTrainedModel""",
    ]

if TYPE_CHECKING:
    from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_gpt_bigcode import (
            GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPTBigCodeForCausalLM,
            GPTBigCodeForSequenceClassification,
            GPTBigCodeForTokenClassification,
            GPTBigCodeModel,
            GPTBigCodePreTrainedModel,
        )


else:
    import sys

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