


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	:							Union[str, Any]           =  logging.get_logger(__name__)

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

__SCREAMING_SNAKE_CASE	:							List[str]           =  {
    "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	:							Union[str, Any]           =  {
    "openai-gpt": 512,
}

def 		snake_case_   (				lowercase__     :    Optional[int]     ):






				'''simple docstring'''






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


class        __lowerCamelCase      (				_SCREAMING_SNAKE_CASE ):


				"""simple docstring"""







				def __init__(				self     :    List[str]	,							lowerCamelCase_     :    Any=True	,							lowerCamelCase_     :    Optional[int]=None	,							lowerCamelCase_     :    str=True	,							lowerCamelCase_     :    Optional[Any]=None	,							lowerCamelCase_     :    Optional[Any]=True	,							):
								if never_split is None:
												_lowerCAmelCase  						=[]
								_lowerCAmelCase  						=do_lower_case
								_lowerCAmelCase  						=set(A_					)
								_lowerCAmelCase  						=tokenize_chinese_chars
								_lowerCAmelCase  						=strip_accents
								_lowerCAmelCase  						=do_split_on_punc






				def        lowerCAmelCase__			(				self     :    List[Any]	,							lowerCamelCase_     :    List[str]	,							lowerCamelCase_     :    Dict=None					):
								_lowerCAmelCase  						=self.never_split.union(set(A_					)					) if never_split else self.never_split
								_lowerCAmelCase  						=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:
												_lowerCAmelCase  						=self._tokenize_chinese_chars(A_					)
								# prevents treating the same character with different unicode codepoints as different characters
								_lowerCAmelCase  						=unicodedata.normalize("""NFC"""	,							A_					)
								_lowerCAmelCase  						=whitespace_tokenize(A_					)
								_lowerCAmelCase  						=[]
								for token in orig_tokens:
												if token not in never_split:
																if self.do_lower_case:
																				_lowerCAmelCase  						=token.lower()
																				if self.strip_accents is not False:
																								_lowerCAmelCase  						=self._run_strip_accents(A_					)
																elif self.strip_accents:
																				_lowerCAmelCase  						=self._run_strip_accents(A_					)
												split_tokens.extend(self._run_split_on_punc(A_	,							A_					)					)

								_lowerCAmelCase  						=whitespace_tokenize(""" """.join(A_					)					)
								return output_tokens






				def        lowerCAmelCase__			(				self     :    str	,							lowerCamelCase_     :    Tuple					):
								_lowerCAmelCase  						=unicodedata.normalize("""NFD"""	,							A_					)
								_lowerCAmelCase  						=[]
								for char in text:
												_lowerCAmelCase  						=unicodedata.category(A_					)
												if cat == "Mn":
																continue
												output.append(A_					)
								return "".join(A_					)






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

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






				def        lowerCAmelCase__			(				self     :    Optional[Any]	,							lowerCamelCase_     :    Union[str, Any]					):
								_lowerCAmelCase  						=[]
								for char in text:
												_lowerCAmelCase  						=ord(A_					)
												if self._is_chinese_char(A_					):
																output.append(""" """					)
																output.append(A_					)
																output.append(""" """					)
												else:
																output.append(A_					)
								return "".join(A_					)






				def        lowerCAmelCase__			(				self     :    Union[str, Any]	,							lowerCamelCase_     :    Optional[int]					):
								# 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 >= 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     :    str	,							lowerCamelCase_     :    Any					):
								_lowerCAmelCase  						=[]
								for char in text:
												_lowerCAmelCase  						=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 		snake_case_   (				lowercase__     :    str     ):






				'''simple docstring'''






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

def 		snake_case_   (				lowercase__     :    Tuple     ):






				'''simple docstring'''






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





class        __lowerCamelCase      (				_SCREAMING_SNAKE_CASE ):


				"""simple docstring"""







				a_:		Optional[int]					      =	VOCAB_FILES_NAMES
				a_:		str					      =	PRETRAINED_VOCAB_FILES_MAP
				a_:		Any					      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				a_:		Optional[Any]					      =	["input_ids", "attention_mask"]






				def __init__(				self     :    Union[str, Any]	,							lowerCamelCase_     :    List[Any]	,							lowerCamelCase_     :    Optional[Any]	,							lowerCamelCase_     :    Dict="<unk>"	,							**lowerCamelCase_     :    Any					):
								super().__init__(unk_token=A_	,							**A_					)

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

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

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






				@property
				def        lowerCAmelCase__			(				self     :    str					):
								return True






				@property
				def        lowerCAmelCase__			(				self     :    int					):
								return len(self.encoder					)






				def        lowerCAmelCase__			(				self     :    Optional[Any]					):
								return dict(self.encoder	,							**self.added_tokens_encoder					)






				def        lowerCAmelCase__			(				self     :    Optional[int]	,							lowerCamelCase_     :    Optional[Any]					):
								_lowerCAmelCase  						=tuple(token[:-1]					) + (token[-1] + """</w>""",)
								if token in self.cache:
												return self.cache[token]
								_lowerCAmelCase  						=get_pairs(A_					)

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

								while True:
												_lowerCAmelCase  						=min(A_	,							key=lambda lowerCamelCase_					: self.bpe_ranks.get(A_	,							float("""inf"""					)					)					)
												if bigram not in self.bpe_ranks:
																break
												_lowerCAmelCase						,							_lowerCAmelCase  						=bigram
												_lowerCAmelCase  						=[]
												_lowerCAmelCase  						=0
												while i < len(A_					):
																try:
																				_lowerCAmelCase  						=word.index(A_	,							A_					)
																except ValueError:
																				new_word.extend(word[i:]					)
																				break
																else:
																				new_word.extend(word[i:j]					)
																				_lowerCAmelCase  						=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
												_lowerCAmelCase  						=tuple(A_					)
												_lowerCAmelCase  						=new_word
												if len(A_					) == 1:
																break
												else:
																_lowerCAmelCase  						=get_pairs(A_					)
								_lowerCAmelCase  						=""" """.join(A_					)
								if word == "\n  </w>":
												_lowerCAmelCase  						="""\n</w>"""
								_lowerCAmelCase  						=word
								return word






				def        lowerCAmelCase__			(				self     :    List[str]	,							lowerCamelCase_     :    str					):
								_lowerCAmelCase  						=[]
								if self.fix_text is None:
												# Using BERT's BasicTokenizer
												_lowerCAmelCase  						=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)
												_lowerCAmelCase  						=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        lowerCAmelCase__			(				self     :    Tuple	,							lowerCamelCase_     :    List[str]					):
								return self.encoder.get(A_	,							self.encoder.get(self.unk_token					)					)






				def        lowerCAmelCase__			(				self     :    List[str]	,							lowerCamelCase_     :    int					):
								return self.decoder.get(A_	,							self.unk_token					)






				def        lowerCAmelCase__			(				self     :    Union[str, Any]	,							lowerCamelCase_     :    Optional[int]					):
								_lowerCAmelCase  						="""""".join(A_					).replace("""</w>"""	,							""" """					).strip()
								return out_string






				def        lowerCAmelCase__			(				self     :    int	,							lowerCamelCase_     :    str	,							lowerCamelCase_     :    List[Any] = None					):
								if not os.path.isdir(A_					):
												logger.error(F"Vocabulary path ({save_directory}) should be a directory"					)
												return
								_lowerCAmelCase  						=os.path.join(
								    A_	,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)
								_lowerCAmelCase  						=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"""					)

								_lowerCAmelCase  						=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 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!"""					)
																				_lowerCAmelCase  						=token_index
																writer.write(""" """.join(A_					) + """\n"""					)
																index += 1

								return vocab_file, merge_file



def  A			(   lowercase					)  ->    int:
		'''simple docstring'''

		barcode //= 10  # exclude the last digit
		UpperCamelCase            =	False
		UpperCamelCase            =	0

		# extract and check each digit
		while barcode != 0:
				UpperCamelCase            =	1 if checker else 3
				s += mult * (barcode % 10)
				barcode //= 10
				UpperCamelCase            =	not checker

		return (10 - (s % 10)) % 10


def  A			(   lowercase					)  ->    bool:
		'''simple docstring'''

		return len(str(lowercase					)					) == 13 and get_check_digit(lowercase					) == barcode % 10


def  A			(   lowercase					)  ->    int:
		'''simple docstring'''

		if str(lowercase					).isalpha():
				UpperCamelCase            =	f'''Barcode \'{barcode}\' has alphabetic characters.'''
				raise ValueError(lowercase					)
		elif int(lowercase					) < 0:
				raise ValueError('The entered barcode has a negative value. Try again.'					)
		else:
				return int(lowercase					)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					_UpperCAmelCase   :					List[str]    =			get_barcode(input("Barcode: ").strip())

					if is_valid(barcode):
										print(F'''\'{barcode}\' is a valid barcode.''')
					else:
										print(F'''\'{barcode}\' is NOT a valid barcode.''')
