code: 
  
  
  import json
  import os
  from functools import lru_cache
  from typing import List, Optional, Tuple
  
  import regex as re
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  lowercase_								=    logging.get_logger(__name__)
  
  lowercase_								=    {
      'vocab_file': 'vocab.json',
      'merges_file': 'merges.txt',
  }
  
  lowercase_								=    {
      'vocab_file': {
          'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
          'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
          'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
          'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
          'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
          'roberta-large-openai-detector': (
              'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
          ),
      },
      'merges_file': {
          'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
          'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
          'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
          'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
          'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
          'roberta-large-openai-detector': (
              'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
          ),
      },
  }
  
  lowercase_								=    {
      'roberta-base': 5_12,
      'roberta-large': 5_12,
      'roberta-large-mnli': 5_12,
      'distilroberta-base': 5_12,
      'roberta-base-openai-detector': 5_12,
      'roberta-large-openai-detector': 5_12,
  }
  
  
  @lru_cache()
  def 							SCREAMING_SNAKE_CASE					(  )    ->  int:
  						_a     =			(
  						    list(range(ord('!'				)							,     ord('~'				) + 1				)				) + list(range(ord('¡'				)							,     ord('¬'				) + 1				)				) + list(range(ord('®'				)							,     ord('ÿ'				) + 1				)				)
  						)
  						_a     =			bs[:]
  						_a     =			0
  						for b in range(2**8				):
  												if b not in bs:
  																		bs.append(_UpperCAmelCase				)
  																		cs.append(2**8 + n				)
  																		n += 1
  						_a     =			[chr(_UpperCAmelCase				) for n in cs]
  						return dict(zip(_UpperCAmelCase							,     _UpperCAmelCase				)				)
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  int:
  						_a     =			set()
  						_a     =			word[0]
  						for char in word[1:]:
  												pairs.add((prev_char, char)				)
  												_a     =			char
  						return pairs
  
  
  
  
  
  
  class 					_UpperCamelCase (      lowerCamelCase__	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  							_A  				=  VOCAB_FILES_NAMES
  							_A  				=  PRETRAINED_VOCAB_FILES_MAP
  							_A  				=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  							_A  				=  ["input_ids", "attention_mask"]
  
  
  
  
  
  
  
  							def __init__(		self  :				str		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]		,				SCREAMING_SNAKE_CASE_  :				Any="replace"		,				SCREAMING_SNAKE_CASE_  :				Tuple="<s>"		,				SCREAMING_SNAKE_CASE_  :				List[Any]="</s>"		,				SCREAMING_SNAKE_CASE_  :				Any="</s>"		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]="<s>"		,				SCREAMING_SNAKE_CASE_  :				Tuple="<unk>"		,				SCREAMING_SNAKE_CASE_  :				str="<pad>"		,				SCREAMING_SNAKE_CASE_  :				List[str]="<mask>"		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]=False		,				**SCREAMING_SNAKE_CASE_  :				str		,				):
  													_a     =			AddedToken(SCREAMING_SNAKE_CASE_		,				lstrip=SCREAMING_SNAKE_CASE_		,				rstrip=SCREAMING_SNAKE_CASE_		) if isinstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		) else bos_token
  													_a     =			AddedToken(SCREAMING_SNAKE_CASE_		,				lstrip=SCREAMING_SNAKE_CASE_		,				rstrip=SCREAMING_SNAKE_CASE_		) if isinstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		) else eos_token
  													_a     =			AddedToken(SCREAMING_SNAKE_CASE_		,				lstrip=SCREAMING_SNAKE_CASE_		,				rstrip=SCREAMING_SNAKE_CASE_		) if isinstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		) else sep_token
  													_a     =			AddedToken(SCREAMING_SNAKE_CASE_		,				lstrip=SCREAMING_SNAKE_CASE_		,				rstrip=SCREAMING_SNAKE_CASE_		) if isinstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		) else cls_token
  													_a     =			AddedToken(SCREAMING_SNAKE_CASE_		,				lstrip=SCREAMING_SNAKE_CASE_		,				rstrip=SCREAMING_SNAKE_CASE_		) if isinstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		) else unk_token
  													_a     =			AddedToken(SCREAMING_SNAKE_CASE_		,				lstrip=SCREAMING_SNAKE_CASE_		,				rstrip=SCREAMING_SNAKE_CASE_		) if isinstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		) else pad_token
  
  													# Mask token behave like a normal word, i.e. include the space before it
  													_a     =			AddedToken(SCREAMING_SNAKE_CASE_		,				lstrip=SCREAMING_SNAKE_CASE_		,				rstrip=SCREAMING_SNAKE_CASE_		) if isinstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		) else mask_token
  
  													super().__init__(
  													    errors=SCREAMING_SNAKE_CASE_		,				bos_token=SCREAMING_SNAKE_CASE_		,				eos_token=SCREAMING_SNAKE_CASE_		,				unk_token=SCREAMING_SNAKE_CASE_		,				sep_token=SCREAMING_SNAKE_CASE_		,				cls_token=SCREAMING_SNAKE_CASE_		,				pad_token=SCREAMING_SNAKE_CASE_		,				mask_token=SCREAMING_SNAKE_CASE_		,				add_prefix_space=SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_		,				)
  
  													with open(SCREAMING_SNAKE_CASE_		,				encoding='utf-8'		) as vocab_handle:
  																			_a     =			json.load(SCREAMING_SNAKE_CASE_		)
  													_a     =			{v: k for k, v in self.encoder.items()}
  													_a     =			errors  # how to handle errors in decoding
  													_a     =			bytes_to_unicode()
  													_a     =			{v: k for k, v in self.byte_encoder.items()}
  													with open(SCREAMING_SNAKE_CASE_		,				encoding='utf-8'		) as merges_handle:
  																			_a     =			merges_handle.read().split('\n'		)[1:-1]
  													_a     =			[tuple(merge.split()		) for merge in bpe_merges]
  													_a     =			dict(zip(SCREAMING_SNAKE_CASE_		,				range(len(SCREAMING_SNAKE_CASE_		)		)		)		)
  													_a     =			{}
  													_a     =			add_prefix_space
  
  													# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
  													_a     =			re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'		)
  
  
  
  
  
  
  
  							@property
  							def 		_UpperCAmelCase	(		self  :				Optional[Any]		):
  													return len(self.encoder		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				str		):
  													return dict(self.encoder		,				**self.added_tokens_encoder		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				List[Any]		,				SCREAMING_SNAKE_CASE_  :				Dict		):
  													if token in self.cache:
  																			return self.cache[token]
  													_a     =			tuple(SCREAMING_SNAKE_CASE_		)
  													_a     =			get_pairs(SCREAMING_SNAKE_CASE_		)
  
  													if not pairs:
  																			return token
  
  													while True:
  																			_a     =			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
  																			_a ,	_a     =			bigram
  																			_a     =			[]
  																			_a     =			0
  																			while i < len(SCREAMING_SNAKE_CASE_		):
  																									try:
  																															_a     =			word.index(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		)
  																									except ValueError:
  																															new_word.extend(word[i:]		)
  																															break
  																									else:
  																															new_word.extend(word[i:j]		)
  																															_a     =			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
  																			_a     =			tuple(SCREAMING_SNAKE_CASE_		)
  																			_a     =			new_word
  																			if len(SCREAMING_SNAKE_CASE_		) == 1:
  																									break
  																			else:
  																									_a     =			get_pairs(SCREAMING_SNAKE_CASE_		)
  													_a     =			' '.join(SCREAMING_SNAKE_CASE_		)
  													_a     =			word
  													return word
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Optional[int]		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]		):
  													_a     =			[]
  													for token in re.findall(self.pat		,				SCREAMING_SNAKE_CASE_		):
  																			_a     =			''.join(
  																			    self.byte_encoder[b] for b in token.encode('utf-8'		)		)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
  																			bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE_		).split(' '		)		)
  													return bpe_tokens
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Optional[int]		,				SCREAMING_SNAKE_CASE_  :				Dict		):
  													return self.encoder.get(SCREAMING_SNAKE_CASE_		,				self.encoder.get(self.unk_token		)		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				str		,				SCREAMING_SNAKE_CASE_  :				str		):
  													return self.decoder.get(SCREAMING_SNAKE_CASE_		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				List[Any]		,				SCREAMING_SNAKE_CASE_  :				Optional[int]		):
  													_a     =			''.join(SCREAMING_SNAKE_CASE_		)
  													_a     =			bytearray([self.byte_decoder[c] for c in text]		).decode('utf-8'		,				errors=self.errors		)
  													return text
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Union[str, Any]		,				SCREAMING_SNAKE_CASE_  :				str		,				SCREAMING_SNAKE_CASE_  :				Optional[str] = None		):
  													if not os.path.isdir(SCREAMING_SNAKE_CASE_		):
  																			logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
  																			return
  													_a     =			os.path.join(
  													    SCREAMING_SNAKE_CASE_		,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)
  													_a     =			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'		)
  
  													_a     =			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!'		)
  																															_a     =			token_index
  																									writer.write(' '.join(SCREAMING_SNAKE_CASE_		) + '\n'		)
  																									index += 1
  
  													return vocab_file, merge_file
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				List[int]		,				SCREAMING_SNAKE_CASE_  :				Optional[List[int]] = None		):
  													if token_ids_a is None:
  																			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  													_a     =			[self.cls_token_id]
  													_a     =			[self.sep_token_id]
  													return cls + token_ids_a + sep + sep + token_ids_a + sep
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Optional[Any]		,				SCREAMING_SNAKE_CASE_  :				List[int]		,				SCREAMING_SNAKE_CASE_  :				Optional[List[int]] = None		,				SCREAMING_SNAKE_CASE_  :				bool = False		):
  													if already_has_special_tokens:
  																			return super().get_special_tokens_mask(
  																			    token_ids_a=SCREAMING_SNAKE_CASE_		,				token_ids_a=SCREAMING_SNAKE_CASE_		,				already_has_special_tokens=SCREAMING_SNAKE_CASE_		)
  
  													if token_ids_a is None:
  																			return [1] + ([0] * len(SCREAMING_SNAKE_CASE_		)) + [1]
  													return [1] + ([0] * len(SCREAMING_SNAKE_CASE_		)) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_		)) + [1]
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				str		,				SCREAMING_SNAKE_CASE_  :				List[int]		,				SCREAMING_SNAKE_CASE_  :				Optional[List[int]] = None		):
  													_a     =			[self.sep_token_id]
  													_a     =			[self.cls_token_id]
  
  													if token_ids_a is None:
  																			return len(cls + token_ids_a + sep		) * [0]
  													return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Tuple		,				SCREAMING_SNAKE_CASE_  :				str		,				SCREAMING_SNAKE_CASE_  :				Any=False		,				**SCREAMING_SNAKE_CASE_  :				List[Any]		):
  													_a     =			kwargs.pop('add_prefix_space'		,				self.add_prefix_space		)
  													if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE_		) > 0 and not text[0].isspace()):
  																			_a     =			' ' + text
  													return (text, kwargs)
  
  
  
style_context: 
  
  
  import numpy as np
  import PIL
  import torch
  import torchvision.transforms as T
  import torchvision.transforms.functional as TF
  from PIL import Image
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=256				)    ->  Dict:
  						_a     =			min(img.size				)
  
  						if s < target_image_size:
  												raise ValueError(f"""min dim for image {s} < {target_image_size}"""				)
  
  						_a     =			target_image_size / s
  						_a     =			(round(r * img.size[1]				), round(r * img.size[0]				))
  						_a     =			TF.resize(_UpperCAmelCase							,     _UpperCAmelCase							,     interpolation=PIL.Image.LANCZOS				)
  						_a     =			TF.center_crop(_UpperCAmelCase							,     output_size=2 * [target_image_size]				)
  						_a     =			torch.unsqueeze(T.ToTensor()(_UpperCAmelCase				)							,     0				)
  						return img
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  List[Any]:
  						_a     =			2.0 * x - 1.0
  						return x
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=True							,     _UpperCAmelCase="RGB"				)    ->  Optional[Any]:
  						_a     =			x.detach().cpu()
  						if process:
  												_a     =			post_process_tensor(_UpperCAmelCase				)
  						_a     =			x.numpy()
  						if process:
  												_a     =			(255 * x).astype(np.uinta				)
  						_a     =			Image.fromarray(_UpperCAmelCase				)
  						if not x.mode == mode:
  												_a     =			x.convert(_UpperCAmelCase				)
  						return x
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  Optional[Any]:
  						_a     =			torch.clamp(_UpperCAmelCase							,     -1.0							,     1.0				)
  						_a     =			(x + 1.0) / 2.0
  						_a     =			x.permute(1							,     2							,     0				)
  						return x
  
  
  
  
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  str:
  						_a     =			post_process_tensor(x.squeeze()				)
  						return x.permute(2							,     0							,     1				).unsqueeze(0				)
  
  
  