code: 
  
  
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
  	from .tokenization_xglm import XGLMTokenizer
  else:
  	SCREAMING_SNAKE_CASE : Any          =    None
  
  
  SCREAMING_SNAKE_CASE : Optional[int]          =    logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE : Tuple          =    {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
  
  SCREAMING_SNAKE_CASE : int          =    {
      "vocab_file": {
          "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
      },
      "tokenizer_file": {
          "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json",
      },
  }
  
  SCREAMING_SNAKE_CASE : Tuple          =    {
      "facebook/xglm-564M": 20_48,
  }
  
  
  
  
  
  class 					snake_case__  ( __A       ):
  	UpperCAmelCase   :							Optional[int]         = VOCAB_FILES_NAMES
  	UpperCAmelCase   :							Union[str, Any]         = PRETRAINED_VOCAB_FILES_MAP
  	UpperCAmelCase   :							str         = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  	UpperCAmelCase   :							str         = ["""input_ids""", """attention_mask"""]
  	UpperCAmelCase   :							List[Any]         = XGLMTokenizer
  
  
  
  
  
  	def __init__(  self				,    UpperCamelCase_=None				,    UpperCamelCase_=None				,    UpperCamelCase_="<s>"				,    UpperCamelCase_="</s>"				,    UpperCamelCase_="</s>"				,    UpperCamelCase_="<s>"				,    UpperCamelCase_="<unk>"				,    UpperCamelCase_="<pad>"				,    **UpperCamelCase_				,    )		->			Optional[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   List[Any]			=	7
  								a_       :   Dict			=	[f"""<madeupword{i}>""" for i in range(self.num_madeup_words							)]
  
  								a_       :   Optional[Any]			=	kwargs.get("""additional_special_tokens"""				,    []							)
  								kwargs["additional_special_tokens"] += [
  								    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
  								]
  
  								super().__init__(
  								    UpperCamelCase_				,    tokenizer_file=UpperCamelCase_				,    bos_token=UpperCamelCase_				,    eos_token=UpperCamelCase_				,    sep_token=UpperCamelCase_				,    cls_token=UpperCamelCase_				,    unk_token=UpperCamelCase_				,    pad_token=UpperCamelCase_				,    **UpperCamelCase_				,    )
  
  								a_       :   Tuple			=	vocab_file
  								a_       :   str			=	False if not self.vocab_file else True
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_ = None							)		->			List[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								if token_ids_a is None:
  															return [self.sep_token_id] + token_ids_a
  								a_       :   Any			=	[self.sep_token_id]
  								return sep + token_ids_a + sep + sep + token_ids_a
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_ = None							)		->			List[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Optional[int]			=	[self.sep_token_id]
  
  								if token_ids_a is None:
  															return len(sep + token_ids_a							) * [0]
  								return len(sep + token_ids_a + sep + sep + token_ids_a							) * [0]
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_ = None							)		->			Tuple[str]:
  								"""simple docstring"""
  
  
  
  
  
  
  								if not self.can_save_slow_tokenizer:
  															raise ValueError(
  															    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
  															    """tokenizer."""							)
  
  								if not os.path.isdir(UpperCamelCase_							):
  															logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""							)
  															return
  								a_       :   Tuple			=	os.path.join(
  								    UpperCamelCase_				,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
  
  								if os.path.abspath(self.vocab_file							) != os.path.abspath(UpperCamelCase_							):
  															copyfile(self.vocab_file				,    UpperCamelCase_							)
  
  								return (out_vocab_file,)
  
  
  
style_context: 
  
  
  from ...utils import is_transformers_available
  from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline
  
  
  if is_transformers_available():
  	from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline
  
  
  