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


_SCREAMING_SNAKE_CASE   		=			logging.get_logger(__name__)

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

_SCREAMING_SNAKE_CASE   		=			{
    """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"""},
}

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





def  lowercase(  UpperCamelCase_					)		->  Optional[Any]:







 '''simple docstring'''
 UpperCamelCase      =			text.strip()
 if not text:
  return []
 UpperCamelCase      =			text.split()
 return tokens






class SCREAMING_SNAKE_CASE_		(			__lowerCAmelCase		):







   def __init__(    self	:	int      ,      lowerCamelCase_	:	str=True      ,      lowerCamelCase_	:	str=None      ,      lowerCamelCase_	:	Union[str, Any]=True      ,      lowerCamelCase_	:	int=None      ,      lowerCamelCase_	:	int=True      ,      ):





    """simple docstring"""


    if never_split is None:
     UpperCamelCase      =			[]
    UpperCamelCase      =			do_lower_case
    UpperCamelCase      =			set(lowerCamelCase_							)
    UpperCamelCase      =			tokenize_chinese_chars
    UpperCamelCase      =			strip_accents
    UpperCamelCase      =			do_split_on_punc







   def 	lowerCamelCase_  (    self	:	str      ,      lowerCamelCase_	:	Optional[int]      ,      lowerCamelCase_	:	Dict=None							):





    """simple docstring"""


    UpperCamelCase      =			self.never_split.union(set(lowerCamelCase_							)							) if never_split else self.never_split
    UpperCamelCase      =			self._clean_text(lowerCamelCase_							)

    # 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:
     UpperCamelCase      =			self._tokenize_chinese_chars(lowerCamelCase_							)
    # prevents treating the same character with different unicode codepoints as different characters
    UpperCamelCase      =			unicodedata.normalize("""NFC"""      ,      lowerCamelCase_							)
    UpperCamelCase      =			whitespace_tokenize(lowerCamelCase_							)
    UpperCamelCase      =			[]
    for token in orig_tokens:
     if token not in never_split:
      if self.do_lower_case:
       UpperCamelCase      =			token.lower()
       if self.strip_accents is not False:
        UpperCamelCase      =			self._run_strip_accents(lowerCamelCase_							)
      elif self.strip_accents:
       UpperCamelCase      =			self._run_strip_accents(lowerCamelCase_							)
     split_tokens.extend(self._run_split_on_punc(lowerCamelCase_      ,      lowerCamelCase_							)							)

    UpperCamelCase      =			whitespace_tokenize(""" """.join(lowerCamelCase_							)							)
    return output_tokens







   def 	lowerCamelCase_  (    self	:	Optional[int]      ,      lowerCamelCase_	:	Optional[int]							):





    """simple docstring"""


    UpperCamelCase      =			unicodedata.normalize("""NFD"""      ,      lowerCamelCase_							)
    UpperCamelCase      =			[]
    for char in text:
     UpperCamelCase      =			unicodedata.category(lowerCamelCase_							)
     if cat == "Mn":
      continue
     output.append(lowerCamelCase_							)
    return "".join(lowerCamelCase_							)







   def 	lowerCamelCase_  (    self	:	Optional[Any]      ,      lowerCamelCase_	:	Union[str, Any]      ,      lowerCamelCase_	:	Optional[int]=None							):





    """simple docstring"""


    if not self.do_split_on_punc or (never_split is not None and text in never_split):
     return [text]
    UpperCamelCase      =			list(lowerCamelCase_							)
    UpperCamelCase      =			0
    UpperCamelCase      =			True
    UpperCamelCase      =			[]
    while i < len(lowerCamelCase_							):
     UpperCamelCase      =			chars[i]
     if _is_punctuation(lowerCamelCase_							):
      output.append([char]							)
      UpperCamelCase      =			True
     else:
      if start_new_word:
       output.append([]							)
      UpperCamelCase      =			False
      output[-1].append(lowerCamelCase_							)
     i += 1

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







   def 	lowerCamelCase_  (    self	:	str      ,      lowerCamelCase_	:	str							):





    """simple docstring"""


    UpperCamelCase      =			[]
    for char in text:
     UpperCamelCase      =			ord(lowerCamelCase_							)
     if self._is_chinese_char(lowerCamelCase_							):
      output.append(""" """							)
      output.append(lowerCamelCase_							)
      output.append(""" """							)
     else:
      output.append(lowerCamelCase_							)
    return "".join(lowerCamelCase_							)







   def 	lowerCamelCase_  (    self	:	Union[str, Any]      ,      lowerCamelCase_	:	Any							):





    """simple docstring"""


    if (
        (cp >= 0x4e00 and cp <= 0x9fff)
        or (cp >= 0x3400 and cp <= 0x4dbf)  #
        or (cp >= 0x20000 and cp <= 0x2a6df)  #
        or (cp >= 0x2a700 and cp <= 0x2b73f)  #
        or (cp >= 0x2b740 and cp <= 0x2b81f)  #
        or (cp >= 0x2b820 and cp <= 0x2ceaf)  #
        or (cp >= 0xf900 and cp <= 0xfaff)
        or (cp >= 0x2f800 and cp <= 0x2fa1f)  #
    ):  #
     return True

    return False







   def 	lowerCamelCase_  (    self	:	Union[str, Any]      ,      lowerCamelCase_	:	int							):





    """simple docstring"""


    UpperCamelCase      =			[]
    for char in text:
     UpperCamelCase      =			ord(lowerCamelCase_							)
     if cp == 0 or cp == 0xfffd or _is_control(lowerCamelCase_							):
      continue
     if _is_whitespace(lowerCamelCase_							):
      output.append(""" """							)
     else:
      output.append(lowerCamelCase_							)
    return "".join(lowerCamelCase_							)





def  lowercase(  UpperCamelCase_					)		->  List[Any]:







 '''simple docstring'''
 UpperCamelCase      =			set()
 UpperCamelCase      =			word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)					)
  UpperCamelCase      =			char
 return pairs





def  lowercase(  UpperCamelCase_					)		->  Any:







 '''simple docstring'''
 UpperCamelCase      =			text.replace("""—"""       ,				"""-"""					)
 UpperCamelCase      =			text.replace("""–"""       ,				"""-"""					)
 UpperCamelCase      =			text.replace("""―"""       ,				"""-"""					)
 UpperCamelCase      =			text.replace("""…"""       ,				"""..."""					)
 UpperCamelCase      =			text.replace("""´"""       ,				"""'"""					)
 UpperCamelCase      =			re.sub(R"""(-+|~+|!+|\"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)"""       ,				R""" \1 """       ,				UpperCamelCase_					)
 UpperCamelCase      =			re.sub(R"""\s*\n\s*"""       ,				""" \n """       ,				UpperCamelCase_					)
 UpperCamelCase      =			re.sub(R"""[^\S\n]+"""       ,				""" """       ,				UpperCamelCase_					)
 return text.strip()






class SCREAMING_SNAKE_CASE_		(			__lowerCAmelCase		):
   __lowerCAmelCase											=							VOCAB_FILES_NAMES
   __lowerCAmelCase											=							PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase											=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase											=							["""input_ids""", """attention_mask"""]







   def __init__(    self	:	Any      ,      lowerCamelCase_	:	Any      ,      lowerCamelCase_	:	List[str]      ,      lowerCamelCase_	:	Optional[int]="<unk>"      ,      **lowerCamelCase_	:	int							):





    """simple docstring"""


    super().__init__(unk_token=lowerCamelCase_      ,      **lowerCamelCase_							)

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

     UpperCamelCase      =			English()
     UpperCamelCase      =			_nlp.tokenizer
     UpperCamelCase      =			ftfy.fix_text
    except ImportError:
     logger.warning("""ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy."""							)
     UpperCamelCase      =			BasicTokenizer(do_lower_case=lowerCamelCase_							)
     UpperCamelCase      =			None

    with open(lowerCamelCase_      ,      encoding="""utf-8"""							) as vocab_handle:
     UpperCamelCase      =			json.load(lowerCamelCase_							)
    UpperCamelCase      =			{v: k for k, v in self.encoder.items()}
    with open(lowerCamelCase_      ,      encoding="""utf-8"""							) as merges_handle:
     UpperCamelCase      =			merges_handle.read().split("""\n"""							)[1:-1]
    UpperCamelCase      =			[tuple(merge.split()							) for merge in merges]
    UpperCamelCase      =			dict(zip(lowerCamelCase_      ,      range(len(lowerCamelCase_							)							)							)							)
    UpperCamelCase      =			{}







   @property
   def 	lowerCamelCase_  (    self	:	Optional[int]							):





    """simple docstring"""


    return True







   @property
   def 	lowerCamelCase_  (    self	:	str							):





    """simple docstring"""


    return len(self.encoder							)







   def 	lowerCamelCase_  (    self	:	Optional[Any]							):





    """simple docstring"""


    return dict(self.encoder      ,      **self.added_tokens_encoder							)







   def 	lowerCamelCase_  (    self	:	int      ,      lowerCamelCase_	:	int							):





    """simple docstring"""


    UpperCamelCase      =			tuple(token[:-1]							) + (token[-1] + """</w>""",)
    if token in self.cache:
     return self.cache[token]
    UpperCamelCase      =			get_pairs(lowerCamelCase_							)

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

    while True:
     UpperCamelCase      =			min(lowerCamelCase_      ,      key=lambda lowerCamelCase_							: self.bpe_ranks.get(lowerCamelCase_      ,      float("""inf"""							)							)							)
     if bigram not in self.bpe_ranks:
      break
     UpperCamelCase     ,    UpperCamelCase      =			bigram
     UpperCamelCase      =			[]
     UpperCamelCase      =			0
     while i < len(lowerCamelCase_							):
      try:
       UpperCamelCase      =			word.index(lowerCamelCase_      ,      lowerCamelCase_							)
      except ValueError:
       new_word.extend(word[i:]							)
       break
      else:
       new_word.extend(word[i:j]							)
       UpperCamelCase      =			j

      if word[i] == first and i < len(lowerCamelCase_							) - 1 and word[i + 1] == second:
       new_word.append(first + second							)
       i += 2
      else:
       new_word.append(word[i]							)
       i += 1
     UpperCamelCase      =			tuple(lowerCamelCase_							)
     UpperCamelCase      =			new_word
     if len(lowerCamelCase_							) == 1:
      break
     else:
      UpperCamelCase      =			get_pairs(lowerCamelCase_							)
    UpperCamelCase      =			""" """.join(lowerCamelCase_							)
    if word == "\n  </w>":
     UpperCamelCase      =			"""\n</w>"""
    UpperCamelCase      =			word
    return word







   def 	lowerCamelCase_  (    self	:	Any      ,      lowerCamelCase_	:	str							):





    """simple docstring"""


    UpperCamelCase      =			[]
    if self.fix_text is None:
     # Using BERT's BasicTokenizer
     UpperCamelCase      =			self.nlp.tokenize(lowerCamelCase_							)
     for token in text:
      split_tokens.extend(list(self.bpe(lowerCamelCase_							).split(""" """							)							)							)
    else:
     # Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
     UpperCamelCase      =			self.nlp(text_standardize(self.fix_text(lowerCamelCase_							)							)							)
     for token in text:
      split_tokens.extend(list(self.bpe(token.text.lower()							).split(""" """							)							)							)
    return split_tokens







   def 	lowerCamelCase_  (    self	:	List[Any]      ,      lowerCamelCase_	:	Any							):





    """simple docstring"""


    return self.encoder.get(lowerCamelCase_      ,      self.encoder.get(self.unk_token							)							)







   def 	lowerCamelCase_  (    self	:	Dict      ,      lowerCamelCase_	:	Tuple							):





    """simple docstring"""


    return self.decoder.get(lowerCamelCase_      ,      self.unk_token							)







   def 	lowerCamelCase_  (    self	:	Union[str, Any]      ,      lowerCamelCase_	:	int							):





    """simple docstring"""


    UpperCamelCase      =			"""""".join(lowerCamelCase_							).replace("""</w>"""      ,      """ """							).strip()
    return out_string







   def 	lowerCamelCase_  (    self	:	Union[str, Any]      ,      lowerCamelCase_	:	str      ,      lowerCamelCase_	:	Optional[str] = None							):





    """simple docstring"""


    if not os.path.isdir(lowerCamelCase_							):
     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
     return
    UpperCamelCase      =			os.path.join(
        lowerCamelCase_      ,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
    UpperCamelCase      =			os.path.join(
        lowerCamelCase_      ,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]							)

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

    UpperCamelCase      =			0
    with open(lowerCamelCase_      ,      """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 lowerCamelCase_							: 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!"""							)
       UpperCamelCase      =			token_index
      writer.write(""" """.join(lowerCamelCase_							) + """\n"""							)
      index += 1

    return vocab_file, merge_file

def  lowercase(  UpperCamelCase_					)		->  Optional[int]:







 '''simple docstring'''
 UpperCamelCase      =			len(UpperCamelCase_					)
 for i in range(length - 1					):
  UpperCamelCase      =			False
  for j in range(length - 1 - i					):
   if collection[j] > collection[j + 1]:
    UpperCamelCase      =			True
    UpperCamelCase     ,    UpperCamelCase      =			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()

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