

"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_SCREAMING_SNAKE_CASE							:	List[Any]										=			{
    """configuration_codegen""": ["""CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CodeGenConfig""", """CodeGenOnnxConfig"""],
    """tokenization_codegen""": ["""CodeGenTokenizer"""],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE							:	int										=			["""CodeGenTokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE							:	List[str]										=			[
				    """CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """CodeGenForCausalLM""",
				    """CodeGenModel""",
				    """CodeGenPreTrainedModel""",
				]

if TYPE_CHECKING:
				from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
				from .tokenization_codegen import CodeGenTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_codegen_fast import CodeGenTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_codegen import (
								    CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    CodeGenForCausalLM,
								    CodeGenModel,
								    CodeGenPreTrainedModel,
								)

else:
				import sys

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






"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_levit import LevitImageProcessor


__lowercase          = logging.get_logger(__name__)






class 		_A    (    _a    ):







    """simple docstring"""




    def __init__(   self							:       Optional[int]							,					*__UpperCAmelCase							:       Dict							,					**__UpperCAmelCase							:       Any):
      warnings.warn(
          "The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
          " use LevitImageProcessor instead."							,					__UpperCAmelCase							,					)
      super().__init__(*__UpperCAmelCase							,					**__UpperCAmelCase)






