




'''simple docstring'''





import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


lowercase__								=						logging.get_logger(__name__)

lowercase__								=						{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

lowercase__								=						{
    """vocab_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/vocab.json"""},
    """merges_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/merges.txt"""},
}

lowercase__								=						{
    """openai-gpt""": 5_1_2,
}


def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			text.strip()
   if not text:
      return []
   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			text.split()
   return tokens






class     lowercase  ( __UpperCAmelCase):






      def __init__(self			,	a__=True			,	a__=None			,	a__=True			,	a__=None			,	a__=True			,	)							-> Dict:
         if never_split is None:
            SCREAMING_SNAKE_CASE__      :					Any							    =			[]
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			do_lower_case
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			set(a__       )
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			tokenize_chinese_chars
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			strip_accents
         SCREAMING_SNAKE_CASE__      :					int							    =			do_split_on_punc






      def  A__(self			,	a__			,	a__=None       )							-> List[Any]:
         SCREAMING_SNAKE_CASE__      :					Dict							    =			self.never_split.union(set(a__       )       ) if never_split else self.never_split
         SCREAMING_SNAKE_CASE__      :					int							    =			self._clean_text(a__       )

         # This was added on November 1st, 2018 for the multilingual and Chinese
         # models. This is also applied to the English models now, but it doesn't
         # matter since the English models were not trained on any Chinese data
         # and generally don't have any Chinese data in them (there are Chinese
         # characters in the vocabulary because Wikipedia does have some Chinese
         # words in the English Wikipedia.).
         if self.tokenize_chinese_chars:
            SCREAMING_SNAKE_CASE__      :					Any							    =			self._tokenize_chinese_chars(a__       )
         # prevents treating the same character with different unicode codepoints as different characters
         SCREAMING_SNAKE_CASE__      :					Dict							    =			unicodedata.normalize('''NFC'''			,	a__       )
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			whitespace_tokenize(a__       )
         SCREAMING_SNAKE_CASE__      :					str							    =			[]
         for token in orig_tokens:
            if token not in never_split:
               if self.do_lower_case:
                  SCREAMING_SNAKE_CASE__      :					Dict							    =			token.lower()
                  if self.strip_accents is not False:
                     SCREAMING_SNAKE_CASE__      :					List[str]							    =			self._run_strip_accents(a__       )
               elif self.strip_accents:
                  SCREAMING_SNAKE_CASE__      :					Tuple							    =			self._run_strip_accents(a__       )
            split_tokens.extend(self._run_split_on_punc(a__			,	a__       )       )

         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			whitespace_tokenize(''' '''.join(a__       )       )
         return output_tokens






      def  A__(self			,	a__       )							-> Optional[int]:
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			unicodedata.normalize('''NFD'''			,	a__       )
         SCREAMING_SNAKE_CASE__      :					Any							    =			[]
         for char in text:
            SCREAMING_SNAKE_CASE__      :					str							    =			unicodedata.category(a__       )
            if cat == "Mn":
               continue
            output.append(a__       )
         return "".join(a__       )






      def  A__(self			,	a__			,	a__=None       )							-> Tuple:
         if not self.do_split_on_punc or (never_split is not None and text in never_split):
            return [text]
         SCREAMING_SNAKE_CASE__      :					Any							    =			list(a__       )
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			0
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			True
         SCREAMING_SNAKE_CASE__      :					Any							    =			[]
         while i < len(a__       ):
            SCREAMING_SNAKE_CASE__      :					int							    =			chars[i]
            if _is_punctuation(a__       ):
               output.append([char]       )
               SCREAMING_SNAKE_CASE__      :					Dict							    =			True
            else:
               if start_new_word:
                  output.append([]       )
               SCREAMING_SNAKE_CASE__      :					Any							    =			False
               output[-1].append(a__       )
            i += 1

         return ["".join(a__       ) for x in output]






      def  A__(self			,	a__       )							-> Tuple:
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			[]
         for char in text:
            SCREAMING_SNAKE_CASE__      :					int							    =			ord(a__       )
            if self._is_chinese_char(a__       ):
               output.append(''' '''       )
               output.append(a__       )
               output.append(''' '''       )
            else:
               output.append(a__       )
         return "".join(a__       )






      def  A__(self			,	a__       )							-> Any:
         # This defines a "chinese character" as anything in the CJK Unicode block:
         #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
         #
         # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
         # despite its name. The modern Korean Hangul alphabet is a different block,
         # as is Japanese Hiragana and Katakana. Those alphabets are used to write
         # space-separated words, so they are not treated specially and handled
         # like the all of the other languages.
         if (
             (cp >= 0x4E00 and cp <= 0x9FFF)
             or (cp >= 0x3400 and cp <= 0x4DBF)  #
             or (cp >= 0x2_0000 and cp <= 0x2_A6DF)  #
             or (cp >= 0x2_A700 and cp <= 0x2_B73F)  #
             or (cp >= 0x2_B740 and cp <= 0x2_B81F)  #
             or (cp >= 0x2_B820 and cp <= 0x2_CEAF)  #
             or (cp >= 0xF900 and cp <= 0xFAFF)
             or (cp >= 0x2_F800 and cp <= 0x2_FA1F)  #
         ):  #
            return True

         return False






      def  A__(self			,	a__       )							-> List[str]:
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			[]
         for char in text:
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			ord(a__       )
            if cp == 0 or cp == 0xFFFD or _is_control(a__       ):
               continue
            if _is_whitespace(a__       ):
               output.append(''' '''       )
            else:
               output.append(a__       )
         return "".join(a__       )


def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					int							    =			set()
   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			word[0]
   for char in word[1:]:
      pairs.add((prev_char, char) )
      SCREAMING_SNAKE_CASE__      :					List[Any]							    =			char
   return pairs


def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			text.replace('''—'''						,  '''-''' )
   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			text.replace('''–'''						,  '''-''' )
   SCREAMING_SNAKE_CASE__      :					str							    =			text.replace('''―'''						,  '''-''' )
   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			text.replace('''…'''						,  '''...''' )
   SCREAMING_SNAKE_CASE__      :					List[str]							    =			text.replace('''´'''						,  '''\'''' )
   SCREAMING_SNAKE_CASE__      :					str							    =			re.sub(R'''(-+|~+|!+|\"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)'''						,  R''' \1 '''						,  _SCREAMING_SNAKE_CASE )
   SCREAMING_SNAKE_CASE__      :					str							    =			re.sub(R'''\s*\n\s*'''						,  ''' \n '''						,  _SCREAMING_SNAKE_CASE )
   SCREAMING_SNAKE_CASE__      :					int							    =			re.sub(R'''[^\S\n]+'''						,  ''' '''						,  _SCREAMING_SNAKE_CASE )
   return text.strip()







class     lowercase  ( __UpperCAmelCase):
      __lowercase					=	VOCAB_FILES_NAMES
      __lowercase					=	PRETRAINED_VOCAB_FILES_MAP
      __lowercase					=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __lowercase					=	["""input_ids""", """attention_mask"""]






      def __init__(self			,	a__			,	a__			,	a__="<unk>"			,	**a__       )							-> Union[str, Any]:
         super().__init__(unk_token=a__			,	**a__       )

         try:
            import ftfy
            from spacy.lang.en import English

            SCREAMING_SNAKE_CASE__      :					Dict							    =			English()
            SCREAMING_SNAKE_CASE__      :					Any							    =			_nlp.tokenizer
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			ftfy.fix_text
         except ImportError:
            logger.warning('''ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.'''       )
            SCREAMING_SNAKE_CASE__      :					Any							    =			BasicTokenizer(do_lower_case=a__       )
            SCREAMING_SNAKE_CASE__      :					int							    =			None

         with open(a__			,	encoding='''utf-8'''       ) as vocab_handle:
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			json.load(a__       )
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			{v: k for k, v in self.encoder.items()}
         with open(a__			,	encoding='''utf-8'''       ) as merges_handle:
            SCREAMING_SNAKE_CASE__      :					str							    =			merges_handle.read().split('''\n'''       )[1:-1]
         SCREAMING_SNAKE_CASE__      :					str							    =			[tuple(merge.split()       ) for merge in merges]
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			dict(zip(a__			,	range(len(a__       )       )       )       )
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			{}






      @property
      def  A__(self       )							-> List[str]:
         return True






      @property
      def  A__(self       )							-> str:
         return len(self.encoder       )






      def  A__(self       )							-> List[str]:
         return dict(self.encoder			,	**self.added_tokens_encoder       )






      def  A__(self			,	a__       )							-> Optional[int]:
         SCREAMING_SNAKE_CASE__      :					Any							    =			tuple(token[:-1]       ) + (token[-1] + '''</w>''',)
         if token in self.cache:
            return self.cache[token]
         SCREAMING_SNAKE_CASE__      :					str							    =			get_pairs(a__       )

         if not pairs:
            return token + "</w>"

         while True:
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			min(a__			,	key=lambda a__       : self.bpe_ranks.get(a__			,	float('''inf'''       )       )       )
            if bigram not in self.bpe_ranks:
               break
            SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			bigram
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			[]
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			0
            while i < len(a__       ):
               try:
                  SCREAMING_SNAKE_CASE__      :					List[Any]							    =			word.index(a__			,	a__       )
               except ValueError:
                  new_word.extend(word[i:]       )
                  break
               else:
                  new_word.extend(word[i:j]       )
                  SCREAMING_SNAKE_CASE__      :					str							    =			j

               if word[i] == first and i < len(a__       ) - 1 and word[i + 1] == second:
                  new_word.append(first + second       )
                  i += 2
               else:
                  new_word.append(word[i]       )
                  i += 1
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			tuple(a__       )
            SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			new_word
            if len(a__       ) == 1:
               break
            else:
               SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			get_pairs(a__       )
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			''' '''.join(a__       )
         if word == "\n  </w>":
            SCREAMING_SNAKE_CASE__      :					Any							    =			'''\n</w>'''
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			word
         return word






      def  A__(self			,	a__       )							-> str:
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			[]
         if self.fix_text is None:
            # Using BERT's BasicTokenizer
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			self.nlp.tokenize(a__       )
            for token in text:
               split_tokens.extend(list(self.bpe(a__       ).split(''' '''       )       )       )
         else:
            # Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
            SCREAMING_SNAKE_CASE__      :					Dict							    =			self.nlp(text_standardize(self.fix_text(a__       )       )       )
            for token in text:
               split_tokens.extend(list(self.bpe(token.text.lower()       ).split(''' '''       )       )       )
         return split_tokens






      def  A__(self			,	a__       )							-> List[str]:
         return self.encoder.get(a__			,	self.encoder.get(self.unk_token       )       )






      def  A__(self			,	a__       )							-> str:
         return self.decoder.get(a__			,	self.unk_token       )






      def  A__(self			,	a__       )							-> str:
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			''''''.join(a__       ).replace('''</w>'''			,	''' '''       ).strip()
         return out_string






      def  A__(self			,	a__			,	a__ = None       )							-> str:
         if not os.path.isdir(a__       ):
            logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""       )
            return
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			os.path.join(
             a__			,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )
         SCREAMING_SNAKE_CASE__      :					int							    =			os.path.join(
             a__			,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']       )

         with open(a__			,	'''w'''			,	encoding='''utf-8'''       ) as f:
            f.write(json.dumps(self.encoder			,	indent=2			,	sort_keys=a__			,	ensure_ascii=a__       ) + '''\n'''       )

         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			0
         with open(a__			,	'''w'''			,	encoding='''utf-8'''       ) as writer:
            writer.write('''#version: 0.2\n'''       )
            for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,	key=lambda a__       : kv[1]       ):
               if index != token_index:
                  logger.warning(
                      f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                      ''' Please check that the tokenizer is not corrupted!'''       )
                  SCREAMING_SNAKE_CASE__      :					Tuple							    =			token_index
               writer.write(''' '''.join(a__       ) + '''\n'''       )
               index += 1

         return vocab_file, merge_file



'''simple docstring'''







def       _a		(						_SCREAMING_SNAKE_CASE		: str	):
						_SCREAMING_SNAKE_CASE					=			len(_SCREAMING_SNAKE_CASE	)
						for i in range(length - 1	):
												_SCREAMING_SNAKE_CASE					=			False
												for j in range(length - 1 - i	):
																		if collection[j] > collection[j + 1]:
																								_SCREAMING_SNAKE_CASE					=			True
																								_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			collection[j + 1], collection[j]
												if not swapped:
																		break  # Stop iteration if the collection is sorted.
						return collection


if __name__ == "__main__":
						import doctest
						import time

						doctest.testmod()

						_snake_case					: Union[str, Any]     			=					input("""Enter numbers separated by a comma:""").strip()
						_snake_case					: List[Any]     			=					[int(item) for item in user_input.split(""",""")]
						_snake_case					: Optional[int]     			=					time.process_time()
						print(*bubble_sort(unsorted), sep=""",""")
						print(F"Processing time: {(time.process_time() - start)%1E9 + 7}")