
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


lowercase__				:				Dict						=      logging.get_logger(__name__)

lowercase__				:				List[Any]						=      {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

lowercase__				:				Tuple						=      {
    "vocab_file": {
        "microsoft/layoutlm-base-uncased": (
            "https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt"
        ),
        "microsoft/layoutlm-large-uncased": (
            "https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "microsoft/layoutlm-base-uncased": (
            "https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json"
        ),
        "microsoft/layoutlm-large-uncased": (
            "https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json"
        ),
    },
}

lowercase__				:				int						=      {
    "microsoft/layoutlm-base-uncased": 5_1_2,
    "microsoft/layoutlm-large-uncased": 5_1_2,
}

lowercase__				:				Tuple						=      {
    "microsoft/layoutlm-base-uncased": {"do_lower_case": True},
    "microsoft/layoutlm-large-uncased": {"do_lower_case": True},
}






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							_snake_case  		=	VOCAB_FILES_NAMES
							_snake_case  		=	PRETRAINED_VOCAB_FILES_MAP
							_snake_case  		=	PRETRAINED_INIT_CONFIGURATION
							_snake_case  		=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_snake_case  		=	LayoutLMTokenizer



							def __init__(	self  ,						SCREAMING_SNAKE_CASE_=None  ,						SCREAMING_SNAKE_CASE_=None  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_="[UNK]"  ,						SCREAMING_SNAKE_CASE_="[SEP]"  ,						SCREAMING_SNAKE_CASE_="[PAD]"  ,						SCREAMING_SNAKE_CASE_="[CLS]"  ,						SCREAMING_SNAKE_CASE_="[MASK]"  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=None  ,						**SCREAMING_SNAKE_CASE_  ,						)-> Tuple:





													'''simple docstring'''






													super().__init__(
													    SCREAMING_SNAKE_CASE_  ,						tokenizer_file=SCREAMING_SNAKE_CASE_  ,						do_lower_case=SCREAMING_SNAKE_CASE_  ,						unk_token=SCREAMING_SNAKE_CASE_  ,						sep_token=SCREAMING_SNAKE_CASE_  ,						pad_token=SCREAMING_SNAKE_CASE_  ,						cls_token=SCREAMING_SNAKE_CASE_  ,						mask_token=SCREAMING_SNAKE_CASE_  ,						tokenize_chinese_chars=SCREAMING_SNAKE_CASE_  ,						strip_accents=SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_  ,						)

													__UpperCamelCase  	=      json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
													if (
													    normalizer_state.get('''lowercase'''  ,						SCREAMING_SNAKE_CASE_      ) != do_lower_case
													    or normalizer_state.get('''strip_accents'''  ,						SCREAMING_SNAKE_CASE_      ) != strip_accents
													    or normalizer_state.get('''handle_chinese_chars'''  ,						SCREAMING_SNAKE_CASE_      ) != tokenize_chinese_chars
													):
																			__UpperCamelCase  	=      getattr(SCREAMING_SNAKE_CASE_  ,						normalizer_state.pop('''type'''      )      )
																			__UpperCamelCase  	=      do_lower_case
																			__UpperCamelCase  	=      strip_accents
																			__UpperCamelCase  	=      tokenize_chinese_chars
																			__UpperCamelCase  	=      normalizer_class(**SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      do_lower_case



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=None      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

													if token_ids_a:
																			output += token_ids_a + [self.sep_token_id]

													return output



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = None      )-> List[int]:





													'''simple docstring'''






													__UpperCamelCase  	=      [self.sep_token_id]
													__UpperCamelCase  	=      [self.cls_token_id]
													if token_ids_a is None:
																			return len(cls + token_ids_a + sep      ) * [0]
													return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = None      )-> Tuple[str]:





													'''simple docstring'''






													__UpperCamelCase  	=      self._tokenizer.model.save(SCREAMING_SNAKE_CASE_  ,						name=SCREAMING_SNAKE_CASE_      )
													return tuple(SCREAMING_SNAKE_CASE_      )






'''simple docstring'''





from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap



@total_ordering
class 			lowerCAmelCase__ :



  '''simple docstring'''






  def __init__(self	:			Tuple						,			lowerCAmelCase_	:			str						,			lowerCAmelCase_	:			int     ) ->							None:
        snake_case_    							=				word
        snake_case_    							=				count

  def __eq__(self	:			List[Any]						,			lowerCAmelCase_	:			object     ) ->							bool:
        if not isinstance(lowerCAmelCase_						,			lowerCAmelCase_     ):
              return NotImplemented
        return self.count == other.count


  def __lt__(self	:			Tuple						,			lowerCAmelCase_	:			object     ) ->							bool:
        if not isinstance(lowerCAmelCase_						,			lowerCAmelCase_     ):
              return NotImplemented
        return self.count < other.count

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


if __name__ == "__main__":
     import doctest

     doctest.testmod()

