


from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap

@total_ordering
class  lowercase_  :







      def __init__(      self :							List[str]		,  _lowercase :							str		,  _lowercase :							int    ):
        lowerCAmelCase__    :    List[str]								=					word
        lowerCAmelCase__    :    Dict								=					count







      def __eq__(      self :							Tuple		,  _lowercase :							object    ):
        if not isinstance(_lowercase		,  _lowercase    ):
          return NotImplemented
        return self.count == other.count







      def __lt__(      self :							Dict		,  _lowercase :							object    ):
        if not isinstance(_lowercase		,  _lowercase    ):
          return NotImplemented
        return self.count < other.count





def     lowercase__	(  lowerCamelCase     :  list[str]				,	lowerCamelCase     :  int				)					->     list[str]:
  lowerCAmelCase__    :    Heap[WordCount]								=					Heap()
  lowerCAmelCase__    :    Any								=					Counter(lowerCamelCase				)
  heap.build_max_heap(
      [WordCount(lowerCamelCase				,	lowerCamelCase				) for word, count in count_by_word.items()]				)
  return [heap.extract_max().word for _ in range(min(lowerCamelCase				,	len(lowerCamelCase				)				)				)]


if __name__ == "__main__":
   import doctest

   doctest.testmod()




"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase    				=      {
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase    				=      ["LayoutLMTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase    				=      [
       "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LayoutLMForMaskedLM",
       "LayoutLMForSequenceClassification",
       "LayoutLMForTokenClassification",
       "LayoutLMForQuestionAnswering",
       "LayoutLMModel",
       "LayoutLMPreTrainedModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase    				=      [
       "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFLayoutLMForMaskedLM",
       "TFLayoutLMForSequenceClassification",
       "TFLayoutLMForTokenClassification",
       "TFLayoutLMForQuestionAnswering",
       "TFLayoutLMMainLayer",
       "TFLayoutLMModel",
       "TFLayoutLMPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
   from .tokenization_layoutlm import LayoutLMTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_layoutlm import (
          LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMForMaskedLM,
          LayoutLMForQuestionAnswering,
          LayoutLMForSequenceClassification,
          LayoutLMForTokenClassification,
          LayoutLMModel,
          LayoutLMPreTrainedModel,
      )
   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_layoutlm import (
          TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForQuestionAnswering,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForTokenClassification,
          TFLayoutLMMainLayer,
          TFLayoutLMModel,
          TFLayoutLMPreTrainedModel,
      )

else:
   import sys

   __UpperCAmelCase    				=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
