
'''simple docstring'''


from __future__ import annotations

from collections.abc import Iterator
from typing import Any




class     __snake_case			:




		def __init__(  self					:Tuple			,    _SCREAMING_SNAKE_CASE					:Any)					->			None:






								'''simple docstring'''






								UpperCamelCase_     :	Any					     =     data
								UpperCamelCase_     :	Node | None					     =     None





		def __str__(  self					:Any)					->			str:






								'''simple docstring'''






								return F"""{self.data}"""







class     __snake_case			:




		def __init__(  self					:Tuple)					->			None:






								'''simple docstring'''






								UpperCamelCase_     :	Node | None					     =     None
								UpperCamelCase_     :	Node | None					     =     None




		def __iter__(  self					:str)					->			Iterator[Any]:






								'''simple docstring'''






								UpperCamelCase_     :	Optional[Any]					     =     self.front
								while node:
														yield node.data
														UpperCamelCase_     :	Optional[int]					     =     node.next




		def __len__(  self					:Dict)					->			int:






								'''simple docstring'''






								return len(tuple(iter(self)))




		def __str__(  self					:Any)					->			str:






								'''simple docstring'''






								return " <- ".join(str(_SCREAMING_SNAKE_CASE) for item in self)




		def 	_snake_case	(  self					:Optional[int])					->			bool:






								'''simple docstring'''






								return len(self) == 0




		def 	_snake_case	(  self					:str			,    _SCREAMING_SNAKE_CASE					:Any)					->			None:






								'''simple docstring'''






								UpperCamelCase_     :	Tuple					     =     Node(_SCREAMING_SNAKE_CASE)
								if self.is_empty():
														UpperCamelCase_     :	str					     =     node
								else:
														assert isinstance(self.rear			,    _SCREAMING_SNAKE_CASE)
														UpperCamelCase_     :	Union[str, Any]					     =     node
														UpperCamelCase_     :	Optional[int]					     =     node




		def 	_snake_case	(  self					:List[str])					->			Any:






								'''simple docstring'''






								if self.is_empty():
														raise IndexError("""dequeue from empty queue""")
								assert isinstance(self.front			,    _SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	int					     =     self.front
								UpperCamelCase_     :	Optional[int]					     =     self.front.next
								if self.front is None:
														UpperCamelCase_     :	List[Any]					     =     None
								return node.data





		def 	_snake_case	(  self					:List[str])					->			None:






								'''simple docstring'''






								UpperCamelCase_     :	Optional[int]					     =     None


if __name__ == "__main__":
	from doctest import testmod

	testmod()






'''simple docstring'''


from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE						=      {
    "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:
	__SCREAMING_SNAKE_CASE						=      ["MarkupLMTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__SCREAMING_SNAKE_CASE						=      [
	    "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

	__SCREAMING_SNAKE_CASE						=      _LazyModule(__name__, globals()["__file__"], _import_structure)




