

'''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)







'''simple docstring'''
from transformers import PretrainedConfig
class  _snake_case						(__SCREAMING_SNAKE_CASE):
			__A   :     Optional[Any]          ="custom"






			def __init__(   self       ,_snake_case=1       ,**_snake_case			):
				UpperCAmelCase_			:	Optional[int]      =   attribute
				super().__init__(**_snake_case			)




class  _snake_case						(__SCREAMING_SNAKE_CASE):
			__A   :     Dict          ="custom"






			def __init__(   self       ,_snake_case=1       ,**_snake_case			):
				UpperCAmelCase_			:	Optional[int]      =   attribute




