
"""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


__magic_name__		       =				logging.get_logger(__name__)

__magic_name__		       =				{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

__magic_name__		       =				{
    '''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'''},
}

__magic_name__		       =				{
    '''openai-gpt''': 5_12,
}




def     __lowerCamelCase  (		UpperCamelCase__      ):


	"""simple docstring"""






	_UpperCAmelCase =						text.strip()
	if not text:
		return []
	_UpperCAmelCase =						text.split()
	return tokens


class    _lowerCAmelCase						(						lowerCamelCase__						):



							def __init__(      self ,						a_=True ,						a_=None ,						a_=True ,						a_=None ,						a_=True ,						)							->							Dict:
								if never_split is None:
									_UpperCAmelCase =						[]
								_UpperCAmelCase =						do_lower_case
								_UpperCAmelCase =						set(SCREAMING_SNAKE_CASE_   )
								_UpperCAmelCase =						tokenize_chinese_chars
								_UpperCAmelCase =						strip_accents
								_UpperCAmelCase =						do_split_on_punc



							def       _a   (      self ,						a_ ,						a_=None   )							->							Optional[Any]:
								_UpperCAmelCase =						self.never_split.union(set(SCREAMING_SNAKE_CASE_   )   ) if never_split else self.never_split
								_UpperCAmelCase =						self._clean_text(SCREAMING_SNAKE_CASE_   )

								# 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(SCREAMING_SNAKE_CASE_   )
								# prevents treating the same character with different unicode codepoints as different characters
								_UpperCAmelCase =						unicodedata.normalize("NFC" ,						SCREAMING_SNAKE_CASE_   )
								_UpperCAmelCase =						whitespace_tokenize(SCREAMING_SNAKE_CASE_   )
								_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(SCREAMING_SNAKE_CASE_   )
										elif self.strip_accents:
											_UpperCAmelCase =						self._run_strip_accents(SCREAMING_SNAKE_CASE_   )
									split_tokens.extend(self._run_split_on_punc(SCREAMING_SNAKE_CASE_ ,						SCREAMING_SNAKE_CASE_   )   )

								_UpperCAmelCase =						whitespace_tokenize(" ".join(SCREAMING_SNAKE_CASE_   )   )
								return output_tokens



							def       _a   (      self ,						a_   )							->							List[Any]:
								_UpperCAmelCase =						unicodedata.normalize("NFD" ,						SCREAMING_SNAKE_CASE_   )
								_UpperCAmelCase =						[]
								for char in text:
									_UpperCAmelCase =						unicodedata.category(SCREAMING_SNAKE_CASE_   )
									if cat == "Mn":
										continue
									output.append(SCREAMING_SNAKE_CASE_   )
								return "".join(SCREAMING_SNAKE_CASE_   )



							def       _a   (      self ,						a_ ,						a_=None   )							->							int:
								if not self.do_split_on_punc or (never_split is not None and text in never_split):
									return [text]
								_UpperCAmelCase =						list(SCREAMING_SNAKE_CASE_   )
								_UpperCAmelCase =						0
								_UpperCAmelCase =						True
								_UpperCAmelCase =						[]
								while i < len(SCREAMING_SNAKE_CASE_   ):
									_UpperCAmelCase =						chars[i]
									if _is_punctuation(SCREAMING_SNAKE_CASE_   ):
										output.append([char]   )
										_UpperCAmelCase =						True
									else:
										if start_new_word:
											output.append([]   )
										_UpperCAmelCase =						False
										output[-1].append(SCREAMING_SNAKE_CASE_   )
									i += 1

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



							def       _a   (      self ,						a_   )							->							str:
								_UpperCAmelCase =						[]
								for char in text:
									_UpperCAmelCase =						ord(SCREAMING_SNAKE_CASE_   )
									if self._is_chinese_char(SCREAMING_SNAKE_CASE_   ):
										output.append(" "   )
										output.append(SCREAMING_SNAKE_CASE_   )
										output.append(" "   )
									else:
										output.append(SCREAMING_SNAKE_CASE_   )
								return "".join(SCREAMING_SNAKE_CASE_   )



							def       _a   (      self ,						a_   )							->							Dict:
								# 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 >= 0X4_e_0_0 and cp <= 0X9_f_f_f)
								    or (cp >= 0X3_4_0_0 and cp <= 0X4_d_b_f)  #
								    or (cp >= 0X2_0_0_0_0 and cp <= 0X2_a_6_d_f)  #
								    or (cp >= 0X2_a_7_0_0 and cp <= 0X2_b_7_3_f)  #
								    or (cp >= 0X2_b_7_4_0 and cp <= 0X2_b_8_1_f)  #
								    or (cp >= 0X2_b_8_2_0 and cp <= 0X2_c_e_a_f)  #
								    or (cp >= 0Xf_9_0_0 and cp <= 0Xf_a_f_f)
								    or (cp >= 0X2_f_8_0_0 and cp <= 0X2_f_a_1_f)  #
								):  #
									return True

								return False



							def       _a   (      self ,						a_   )							->							Optional[int]:
								_UpperCAmelCase =						[]
								for char in text:
									_UpperCAmelCase =						ord(SCREAMING_SNAKE_CASE_   )
									if cp == 0 or cp == 0Xf_f_f_d or _is_control(SCREAMING_SNAKE_CASE_   ):
										continue
									if _is_whitespace(SCREAMING_SNAKE_CASE_   ):
										output.append(" "   )
									else:
										output.append(SCREAMING_SNAKE_CASE_   )
								return "".join(SCREAMING_SNAKE_CASE_   )





def     __lowerCamelCase  (		UpperCamelCase__      ):


	"""simple docstring"""






	_UpperCAmelCase =						set()
	_UpperCAmelCase =						word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)      )
		_UpperCAmelCase =						char
	return pairs




def     __lowerCamelCase  (		UpperCamelCase__      ):


	"""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    _lowerCAmelCase						(						lowerCamelCase__						):
							lowercase_     :     Optional[int]       =							VOCAB_FILES_NAMES
							lowercase_     :     List[str]       =							PRETRAINED_VOCAB_FILES_MAP
							lowercase_     :     int       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_     :     Any       =							['''input_ids''', '''attention_mask''']



							def __init__(      self ,						a_ ,						a_ ,						a_="<unk>" ,						**a_   )							->							Tuple:
								super().__init__(unk_token=SCREAMING_SNAKE_CASE_ ,						**SCREAMING_SNAKE_CASE_   )

								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=SCREAMING_SNAKE_CASE_   )
									_UpperCAmelCase =						None

								with open(SCREAMING_SNAKE_CASE_ ,						encoding="utf-8"   ) as vocab_handle:
									_UpperCAmelCase =						json.load(SCREAMING_SNAKE_CASE_   )
								_UpperCAmelCase =						{v: k for k, v in self.encoder.items()}
								with open(SCREAMING_SNAKE_CASE_ ,						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(SCREAMING_SNAKE_CASE_ ,						range(len(SCREAMING_SNAKE_CASE_   )   )   )   )
								_UpperCAmelCase =						{}



							@property
							def       _a   (      self   )							->							str:
								return True



							@property
							def       _a   (      self   )							->							Union[str, Any]:
								return len(self.encoder   )



							def       _a   (      self   )							->							Dict:
								return dict(self.encoder ,						**self.added_tokens_encoder   )



							def       _a   (      self ,						a_   )							->							List[Any]:
								_UpperCAmelCase =						tuple(token[:-1]   ) + (token[-1] + "</w>",)
								if token in self.cache:
									return self.cache[token]
								_UpperCAmelCase =						get_pairs(SCREAMING_SNAKE_CASE_   )

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

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

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



							def       _a   (      self ,						a_   )							->							Optional[int]:
								_UpperCAmelCase =						[]
								if self.fix_text is None:
									# Using BERT's BasicTokenizer
									_UpperCAmelCase =						self.nlp.tokenize(SCREAMING_SNAKE_CASE_   )
									for token in text:
										split_tokens.extend(list(self.bpe(SCREAMING_SNAKE_CASE_   ).split(" "   )   )   )
								else:
									# Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
									_UpperCAmelCase =						self.nlp(text_standardize(self.fix_text(SCREAMING_SNAKE_CASE_   )   )   )
									for token in text:
										split_tokens.extend(list(self.bpe(token.text.lower()   ).split(" "   )   )   )
								return split_tokens



							def       _a   (      self ,						a_   )							->							Any:
								return self.encoder.get(SCREAMING_SNAKE_CASE_ ,						self.encoder.get(self.unk_token   )   )



							def       _a   (      self ,						a_   )							->							Optional[Any]:
								return self.decoder.get(SCREAMING_SNAKE_CASE_ ,						self.unk_token   )



							def       _a   (      self ,						a_   )							->							Union[str, Any]:
								_UpperCAmelCase =						"".join(SCREAMING_SNAKE_CASE_   ).replace("</w>" ,						" "   ).strip()
								return out_string



							def       _a   (      self ,						a_ ,						a_ = None   )							->							Any:
								if not os.path.isdir(SCREAMING_SNAKE_CASE_   ):
									logger.error(f"Vocabulary path ({save_directory}) should be a directory"   )
									return
								_UpperCAmelCase =						os.path.join(
								    SCREAMING_SNAKE_CASE_ ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )
								_UpperCAmelCase =						os.path.join(
								    SCREAMING_SNAKE_CASE_ ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]   )

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

								_UpperCAmelCase =						0
								with open(SCREAMING_SNAKE_CASE_ ,						"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!"   )
											_UpperCAmelCase =						token_index
										writer.write(" ".join(SCREAMING_SNAKE_CASE_   ) + "\n"   )
										index += 1

								return vocab_file, merge_file






"""simple docstring"""
from .stack import Stack




def     __lowerCamelCase  (		UpperCamelCase__      ):


	"""simple docstring"""






	_UpperCAmelCase =						Stack()
	_UpperCAmelCase =						{"(": ")", "[": "]", "{": "}"}
	for bracket in parentheses:
		if bracket in bracket_pairs:
			stack.push(UpperCamelCase__      )
		elif bracket in (")", "]", "}"):
			if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
				return False
	return stack.is_empty()


if __name__ == "__main__":
			from doctest import testmod

			testmod()

			__magic_name__		       =				['''((()))''', '''((())''', '''(()))''']
			print('''Balanced parentheses demonstration:\n''')
			for example in examples:
						__magic_name__		       =				'''''' if balanced_parentheses(example) else '''not '''
						print(f'''{example} is {not_str}balanced''')




