


"""simple docstring"""
from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE					:       Optional[int]							   =							{
    """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					:       int							   =							["""MarkupLMTokenizerFast"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							[
			    """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					:       Dict							   =							_LazyModule(__name__, globals()["""__file__"""], _import_structure)










'''simple docstring'''







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE     ):
		for i, _ in enumerate(SCREAMING_SNAKE_CASE     ):
				if (i % 2 == 1) == (nums[i - 1] > nums[i]):
						SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Dict										=      nums[i], nums[i - 1]

		return nums


if __name__ == "__main__":
							print("""Enter the array elements:""")
							SCREAMING_SNAKE_CASE__     : Tuple				=   list(map(int, input().split()))
							print("""The unsorted array is:""")
							print(array)
							print("""Array after Wiggle sort:""")
							print(wiggle_sort(array))
