"""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


_SCREAMING_SNAKE_CASE  :		int        =      logging.get_logger(__name__)

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

_SCREAMING_SNAKE_CASE  :		Optional[int]        =      {
    '''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''': 5_1_2,
}



def 			A__						(  lowercase_    :  Union[str, Any]	)     ->       List[str]:
			__lowerCamelCase								=				text.strip()
			if not text:
						return []
			__lowerCamelCase								=				text.split()
			return tokens






class     _SCREAMING_SNAKE_CASE       ( UpperCAmelCase							):



			'''simple docstring'''



			def __init__(  self   ,							_SCREAMING_SNAKE_CASE=True   ,							_SCREAMING_SNAKE_CASE=None   ,							_SCREAMING_SNAKE_CASE=True   ,							_SCREAMING_SNAKE_CASE=None   ,							_SCREAMING_SNAKE_CASE=True   ,							):


						'''simple docstring'''

						if never_split is None:
									__lowerCamelCase								=				[]
						__lowerCamelCase								=				do_lower_case
						__lowerCamelCase								=				set(_SCREAMING_SNAKE_CASE  )
						__lowerCamelCase								=				tokenize_chinese_chars
						__lowerCamelCase								=				strip_accents
						__lowerCamelCase								=				do_split_on_punc







			def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE=None  ):


						'''simple docstring'''

						__lowerCamelCase								=				self.never_split.union(set(_SCREAMING_SNAKE_CASE  )  ) if never_split else self.never_split
						__lowerCamelCase								=				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:
									__lowerCamelCase								=				self._tokenize_chinese_chars(_SCREAMING_SNAKE_CASE  )
						# prevents treating the same character with different unicode codepoints as different characters
						__lowerCamelCase								=				unicodedata.normalize('NFC'   ,							_SCREAMING_SNAKE_CASE  )
						__lowerCamelCase								=				whitespace_tokenize(_SCREAMING_SNAKE_CASE  )
						__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(_SCREAMING_SNAKE_CASE  )
												elif self.strip_accents:
															__lowerCamelCase								=				self._run_strip_accents(_SCREAMING_SNAKE_CASE  )
									split_tokens.extend(self._run_split_on_punc(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  )  )

						__lowerCamelCase								=				whitespace_tokenize(' '.join(_SCREAMING_SNAKE_CASE  )  )
						return output_tokens







			def       _A							(  self   ,							_SCREAMING_SNAKE_CASE  ):


						'''simple docstring'''

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







			def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE=None  ):


						'''simple docstring'''

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

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







			def       _A							(  self   ,							_SCREAMING_SNAKE_CASE  ):


						'''simple docstring'''

						__lowerCamelCase								=				[]
						for char in text:
									__lowerCamelCase								=				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   ,							_SCREAMING_SNAKE_CASE  ):


						'''simple docstring'''

						if (
						    (cp >= 0x4_e00 and cp <= 0x9_fff)
						    or (cp >= 0x3_400 and cp <= 0x4_dbf)  #
						    or (cp >= 0x20_000 and cp <= 0x2a_6df)  #
						    or (cp >= 0x2a_700 and cp <= 0x2b_73f)  #
						    or (cp >= 0x2b_740 and cp <= 0x2b_81f)  #
						    or (cp >= 0x2b_820 and cp <= 0x2c_eaf)  #
						    or (cp >= 0xf_900 and cp <= 0xf_aff)
						    or (cp >= 0x2f_800 and cp <= 0x2f_a1f)  #
						):  #
									return True

						return False







			def       _A							(  self   ,							_SCREAMING_SNAKE_CASE  ):


						'''simple docstring'''

						__lowerCamelCase								=				[]
						for char in text:
									__lowerCamelCase								=				ord(_SCREAMING_SNAKE_CASE  )
									if cp == 0 or cp == 0xf_ffd 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 			A__						(  lowercase_    :  int	)     ->       Dict:
			__lowerCamelCase								=				set()
			__lowerCamelCase								=				word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)	)
						__lowerCamelCase								=				char
			return pairs



def 			A__						(  lowercase_    :  Union[str, Any]	)     ->       Union[str, Any]:
			__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     _SCREAMING_SNAKE_CASE       ( UpperCAmelCase							):



			'''simple docstring'''



			__UpperCAmelCase  :       List[str]						=						VOCAB_FILES_NAMES
			__UpperCAmelCase  :       int						=						PRETRAINED_VOCAB_FILES_MAP
			__UpperCAmelCase  :       int						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__UpperCAmelCase  :       Union[str, Any]						=						["input_ids", "attention_mask"]







			def __init__(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE="<unk>"   ,							**_SCREAMING_SNAKE_CASE  ):


						'''simple docstring'''

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

						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=_SCREAMING_SNAKE_CASE  )
									__lowerCamelCase								=				None

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







			@property
			def       _A							(  self  ):


						'''simple docstring'''

						return True







			@property
			def       _A							(  self  ):


						'''simple docstring'''

						return len(self.encoder  )







			def       _A							(  self  ):


						'''simple docstring'''

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







			def       _A							(  self   ,							_SCREAMING_SNAKE_CASE  ):


						'''simple docstring'''

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

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

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







			def       _A							(  self   ,							_SCREAMING_SNAKE_CASE  ):


						'''simple docstring'''

						__lowerCamelCase								=				[]
						if self.fix_text is None:
									# Using BERT's BasicTokenizer
									__lowerCamelCase								=				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)
									__lowerCamelCase								=				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   ,							_SCREAMING_SNAKE_CASE  ):


						'''simple docstring'''

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







			def       _A							(  self   ,							_SCREAMING_SNAKE_CASE  ):


						'''simple docstring'''

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







			def       _A							(  self   ,							_SCREAMING_SNAKE_CASE  ):


						'''simple docstring'''

						__lowerCamelCase								=				"".join(_SCREAMING_SNAKE_CASE  ).replace('</w>'   ,							' '  ).strip()
						return out_string







			def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE = None  ):


						'''simple docstring'''

						if not os.path.isdir(_SCREAMING_SNAKE_CASE  ):
									logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
									return
						__lowerCamelCase								=				os.path.join(
						    _SCREAMING_SNAKE_CASE   ,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )
						__lowerCamelCase								=				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'  )

						__lowerCamelCase								=				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 _SCREAMING_SNAKE_CASE  : 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(_SCREAMING_SNAKE_CASE  ) + '\n'  )
												index += 1

						return vocab_file, merge_file







"""simple docstring"""







import sys

from transformers import AutoTokenizer


_SCREAMING_SNAKE_CASE  :		List[str]        =      sys.argv[1]
_SCREAMING_SNAKE_CASE  :		List[str]        =      sys.argv[2]
_SCREAMING_SNAKE_CASE  :		Tuple        =      int(sys.argv[3])

_SCREAMING_SNAKE_CASE  :		List[str]        =      0

_SCREAMING_SNAKE_CASE  :		Optional[int]        =      AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, '''rt''') as f_p:
     for line in f_p:
          _SCREAMING_SNAKE_CASE  :		List[Any]        =      line.rstrip()

          if not line:
               print(line)
               _SCREAMING_SNAKE_CASE  :		Union[str, Any]        =      0
               continue

          _SCREAMING_SNAKE_CASE  :		Dict        =      line.split()[0]

          _SCREAMING_SNAKE_CASE  :		Tuple        =      len(tokenizer.tokenize(token))

          # Token contains strange control characters like \x96 or \x95
          # Just filter out the complete line
          if current_subwords_len == 0:
               continue

          if (subword_len_counter + current_subwords_len) > max_len:
               print('''''')
               print(line)
               _SCREAMING_SNAKE_CASE  :		List[Any]        =      current_subwords_len
               continue

          subword_len_counter += current_subwords_len

          print(line)






