


"""simple docstring"""






from typing import TYPE_CHECKING

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


_lowerCamelCase  =	{
    'configuration_markuplm': ['MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarkupLMConfig'],
    'feature_extraction_markuplm': ['MarkupLMFeatureExtractor'],
    'processing_markuplm': ['MarkupLMProcessor'],
    'tokenization_markuplm': ['MarkupLMTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	['MarkupLMTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	[
	    'MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'MarkupLMForQuestionAnswering',
	    'MarkupLMForSequenceClassification',
	    'MarkupLMForTokenClassification',
	    'MarkupLMModel',
	    'MarkupLMPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
	from .feature_extraction_markuplm import MarkupLMFeatureExtractor
	from .processing_markuplm import MarkupLMProcessor
	from .tokenization_markuplm import MarkupLMTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_markuplm_fast import MarkupLMTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_markuplm import (
		    MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MarkupLMForQuestionAnswering,
		    MarkupLMForSequenceClassification,
		    MarkupLMForTokenClassification,
		    MarkupLMModel,
		    MarkupLMPreTrainedModel,
		)


else:
	import sys

	_lowerCamelCase  =	_LazyModule(__name__, globals()['__file__'], _import_structure)







import warnings

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


_UpperCAmelCase										=					logging.get_logger(__name__)



class 				_UpperCamelCase       (						lowerCAmelCase_  ):

       def __init__(		self:			Tuple       , *_SCREAMING_SNAKE_CASE:			List[Any]       , **_SCREAMING_SNAKE_CASE:			List[Any]		)							->			None:




              """simple docstring"""


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

