
'''simple docstring'''


from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap




@total_ordering
class     __snake_case			:




		def __init__(  self					:List[Any]			,    _SCREAMING_SNAKE_CASE					:str			,    _SCREAMING_SNAKE_CASE					:int)					->			None:






								'''simple docstring'''






								UpperCamelCase_     :	Tuple					     =     word
								UpperCamelCase_     :	Dict					     =     count




		def __eq__(  self					:Any			,    _SCREAMING_SNAKE_CASE					:object)					->			bool:






								'''simple docstring'''






								if not isinstance(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE):
														return NotImplemented
								return self.count == other.count





		def __lt__(  self					:Tuple			,    _SCREAMING_SNAKE_CASE					:object)					->			bool:






								'''simple docstring'''






								if not isinstance(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE):
														return NotImplemented
								return self.count < other.count







def 					__magic_name__(       UpperCAmelCase__ :       list[str]							,				UpperCAmelCase__ :       int	):
						UpperCamelCase_     :	Heap[WordCount]					     =     Heap()
						UpperCamelCase_     :	Optional[Any]					     =     Counter(UpperCAmelCase__	)
						heap.build_max_heap(
						    [WordCount(UpperCAmelCase__							,				UpperCAmelCase__	) for word, count in count_by_word.items()]	)
						return [heap.extract_max().word for _ in range(min(UpperCAmelCase__							,				len(UpperCAmelCase__	)	)	)]


if __name__ == "__main__":
	import doctest

	doctest.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)




