





'''simple docstring'''




import operator




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_ = False					,				UpperCAmelCase_ = None							)   ->list:
	snake_case__	       =   operator.lt if reverse else operator.gt
	snake_case__	       =   solution or []

	if not arr:
		return solution

	snake_case__	       =   [arr.pop(0							)]
	for i, item in enumerate(UpperCAmelCase_							):
		if _operator(UpperCAmelCase_					,				sublist[-1]							):
			sublist.append(UpperCAmelCase_							)
			arr.pop(UpperCAmelCase_							)

    #  merging sublist into solution list
	if not solution:
		solution.extend(UpperCAmelCase_							)
	else:
		while sublist:
			snake_case__	       =   sublist.pop(0							)
			for i, xx in enumerate(UpperCAmelCase_							):
				if not _operator(UpperCAmelCase_					,				UpperCAmelCase_							):
					solution.insert(UpperCAmelCase_					,				UpperCAmelCase_							)
					break
			else:
				solution.append(UpperCAmelCase_							)

	strand_sort(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)
	return solution


if __name__ == "__main__":
			assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
			assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]










'''simple docstring'''




from typing import TYPE_CHECKING

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


a__					:			Dict          =     {
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a__					:			Union[str, Any]          =     [
			    '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''CanineForMultipleChoice''',
			    '''CanineForQuestionAnswering''',
			    '''CanineForSequenceClassification''',
			    '''CanineForTokenClassification''',
			    '''CanineLayer''',
			    '''CanineModel''',
			    '''CaninePreTrainedModel''',
			    '''load_tf_weights_in_canine''',
			]


if TYPE_CHECKING:
			from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
			from .tokenization_canine import CanineTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_canine import (
						    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CanineForMultipleChoice,
						    CanineForQuestionAnswering,
						    CanineForSequenceClassification,
						    CanineForTokenClassification,
						    CanineLayer,
						    CanineModel,
						    CaninePreTrainedModel,
						    load_tf_weights_in_canine,
						)


else:
			import sys

			a__					:			List[Any]          =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



