code: 
  import argparse
  import inspect
  import logging
  from typing import Tuple
  
  import numpy as np
  import torch
  
  from transformers import (
      AutoTokenizer,
      BloomForCausalLM,
      BloomTokenizerFast,
      CTRLLMHeadModel,
      CTRLTokenizer,
      GenerationMixin,
      GPTaLMHeadModel,
      GPTaTokenizer,
      GPTJForCausalLM,
      LlamaForCausalLM,
      LlamaTokenizer,
      OpenAIGPTLMHeadModel,
      OpenAIGPTTokenizer,
      OPTForCausalLM,
      TransfoXLLMHeadModel,
      TransfoXLTokenizer,
      XLMTokenizer,
      XLMWithLMHeadModel,
      XLNetLMHeadModel,
      XLNetTokenizer,
  )
  from transformers.modeling_outputs import CausalLMOutputWithPast
  
  
  logging.basicConfig(
      format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",
      datefmt="""%m/%d/%Y %H:%M:%S""",
      level=logging.INFO,
  )
  UpperCamelCase_	:    List[Any]  					=       logging.getLogger(__name__)
  
  UpperCamelCase_	:    Union[str, Any]  					=       int(1_0000)  # Hardcoded max length to avoid infinite loop
  
  UpperCamelCase_	:    Union[str, Any]  					=       {
      """gpt2""": (GPTaLMHeadModel, GPTaTokenizer),
      """ctrl""": (CTRLLMHeadModel, CTRLTokenizer),
      """openai-gpt""": (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
      """xlnet""": (XLNetLMHeadModel, XLNetTokenizer),
      """transfo-xl""": (TransfoXLLMHeadModel, TransfoXLTokenizer),
      """xlm""": (XLMWithLMHeadModel, XLMTokenizer),
      """gptj""": (GPTJForCausalLM, AutoTokenizer),
      """bloom""": (BloomForCausalLM, BloomTokenizerFast),
      """llama""": (LlamaForCausalLM, LlamaTokenizer),
      """opt""": (OPTForCausalLM, GPTaTokenizer),
  }
  
  # Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
  # in https://github.com/rusiaaman/XLNet-gen#methodology
  # and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
  UpperCamelCase_	:    Any  					=       """In 1991, the remains of Russian Tsar Nicholas II and his family\n(except for Alexei and Maria) are discovered.\nThe voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the\nremainder of the story. 1883 Western Siberia,\na young Grigori Rasputin is asked by his father and a group of men to perform magic.\nRasputin has a vision and denounces one of the men as a horse thief. Although his\nfather initially slaps him for making such an accusation, Rasputin watches as the\nman is chased outside and beaten. Twenty years later, Rasputin sees a vision of\nthe Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,\nwith people, even a bishop, begging for his blessing. <eod> </s> <eos>"""
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      Optional[Any]     )      -> Tuple:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						np.random.seed(args.seed     )
  						torch.manual_seed(args.seed     )
  						if args.n_gpu > 0:
  												torch.cuda.manual_seed_all(args.seed     )
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      List[str]   , _UpperCAmelCase     :      Tuple   , _UpperCAmelCase     :      Optional[int]   , _UpperCAmelCase     :      Dict     )      -> Tuple:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						if args.temperature > 0.7:
  												logger.info("CTRL typically works better with lower temperatures (and lower top_k)."     )
  
  						_lowercase					:  int    			=      tokenizer.encode(UpperCamelCase__   , add_special_tokens=UpperCamelCase__     )
  						if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()     ):
  												logger.info("WARNING! You are not starting your generation from a control code so you won't get good results"     )
  						return prompt_text
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      Optional[Any]   , _UpperCAmelCase     :      List[str]   , _UpperCAmelCase     :      Optional[int]   , _UpperCAmelCase     :      List[str]     )      -> int:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  Union[str, Any]    			=      hasattr(model.config   , "use_lang_emb"     ) and model.config.use_lang_emb
  						if hasattr(model.config   , "lang2id"     ) and use_lang_emb:
  												_lowercase					:  Optional[Any]    			=      model.config.langaid.keys()
  												if args.xlm_language in available_languages:
  																		_lowercase					:  Tuple    			=      args.xlm_language
  												else:
  																		_lowercase					:  Optional[int]    			=      None
  																		while language not in available_languages:
  																								_lowercase					:  Tuple    			=      input("Using XLM. Select language in " + str(list(UpperCamelCase__     )     ) + " >>> "     )
  
  												_lowercase					:  List[Any]    			=      model.config.langaid[language]
  												# kwargs["language"] = tokenizer.lang2id[language]
  
  						# TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
  						# XLM masked-language modeling (MLM) models need masked token
  						# is_xlm_mlm = "mlm" in args.model_name_or_path
  						# if is_xlm_mlm:
  						#     kwargs["mask_token_id"] = tokenizer.mask_token_id
  
  						return prompt_text
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      Optional[Any]   , _UpperCAmelCase     :      Any   , _UpperCAmelCase     :      Dict   , _UpperCAmelCase     :      List[Any]     )      -> Dict:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  List[Any]    			=      args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
  						_lowercase					:  Optional[int]    			=      prefix + prompt_text
  						return prompt_text
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      Union[str, Any]   , _UpperCAmelCase     :      List[str]   , _UpperCAmelCase     :      List[str]   , _UpperCAmelCase     :      Tuple     )      -> Optional[int]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  Optional[Any]    			=      args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
  						_lowercase					:  Any    			=      prefix + prompt_text
  						return prompt_text
  
  
  UpperCamelCase_	:    List[Any]  					=       {
      """ctrl""": prepare_ctrl_input,
      """xlm""": prepare_xlm_input,
      """xlnet""": prepare_xlnet_input,
      """transfo-xl""": prepare_transfoxl_input,
  }
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      Dict   , _UpperCAmelCase     :      int     )      -> Union[str, Any]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						if length < 0 and max_sequence_length > 0:
  												_lowercase					:  Optional[int]    			=      max_sequence_length
  						elif 0 < max_sequence_length < length:
  												_lowercase					:  Tuple    			=      max_sequence_length  # No generation bigger than model size
  						elif length < 0:
  												_lowercase					:  Optional[Any]    			=      MAX_LENGTH  # avoid infinite loop
  						return length
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      Tuple     )      -> Dict:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  Dict    			=      None
  						if hasattr(UpperCamelCase__   , "hidden_size"     ):
  												_lowercase					:  str    			=      model_config.hidden_size
  						elif hasattr(UpperCamelCase__   , "n_embed"     ):
  												_lowercase					:  List[str]    			=      model_config.n_embed
  						elif hasattr(UpperCamelCase__   , "n_embd"     ):
  												_lowercase					:  Optional[Any]    			=      model_config.n_embd
  
  						_lowercase					:  Optional[Any]    			=      None
  						if hasattr(UpperCamelCase__   , "num_attention_heads"     ):
  												_lowercase					:  Tuple    			=      model_config.num_attention_heads
  						elif hasattr(UpperCamelCase__   , "n_head"     ):
  												_lowercase					:  Any    			=      model_config.n_head
  
  						if embedding_size is None or num_head is None or num_head == 0:
  												raise ValueError("Check the model config"     )
  
  						_lowercase					:  int    			=      int(embedding_size / num_head     )
  						if hasattr(UpperCamelCase__   , "n_layer"     ):
  												_lowercase					:  Tuple    			=      model_config.n_layer
  						elif hasattr(UpperCamelCase__   , "num_hidden_layers"     ):
  												_lowercase					:  str    			=      model_config.num_hidden_layers
  						else:
  												raise ValueError("Number of hidden layers couldn't be determined from the model config"     )
  
  						return num_layer, num_head, num_embedding_size_per_head
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      Optional[int]   , _UpperCAmelCase     :      Dict   , _UpperCAmelCase     :      Tuple     )      -> List[str]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  Optional[int]    			=      sparse_model_config(model.config     )
  						if model.config.model_type == "bloom":
  												_lowercase					:  Any    			=      tuple(
  												    (
  												        torch.empty(int(num_attention_heads * batch_size     )   , UpperCamelCase__   , UpperCamelCase__     )
  												        .to(model.dtype     )
  												        .to(model.device     ),
  												        torch.empty(int(num_attention_heads * batch_size     )   , UpperCamelCase__   , UpperCamelCase__     )
  												        .to(model.dtype     )
  												        .to(model.device     ),
  												    )
  												    for _ in range(UpperCamelCase__     )     )
  						else:
  												_lowercase					:  Union[str, Any]    			=      tuple(
  												    (
  												        torch.empty(UpperCamelCase__   , UpperCamelCase__   , UpperCamelCase__   , UpperCamelCase__     )
  												        .to(model.dtype     )
  												        .to(model.device     ),
  												        torch.empty(UpperCamelCase__   , UpperCamelCase__   , UpperCamelCase__   , UpperCamelCase__     )
  												        .to(model.dtype     )
  												        .to(model.device     ),
  												    )
  												    for _ in range(UpperCamelCase__     )     )
  						return past_key_values
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      Optional[Any]   , _UpperCAmelCase     :      List[str]   , _UpperCAmelCase     :      Any     )      -> List[str]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  List[str]    			=      len(UpperCamelCase__     )
  						_lowercase					:  Tuple    			=      tokenizer.batch_encode_plus(UpperCamelCase__   , return_tensors="pt"     )
  						_lowercase					:  Any    			=      dummy_input.to(model.device     )
  						if model.config.use_cache:
  												_lowercase					:  Tuple    			=      generate_past_key_values(UpperCamelCase__   , UpperCamelCase__   , 1     )
  						_lowercase					:  Any    			=      torch.cat(
  						    [
  						        torch.zeros(dummy_input["attention_mask"].shape[0]   , 1     )
  						        .to(dummy_input["attention_mask"].dtype     )
  						        .to(model.device     ),
  						        dummy_input["attention_mask"],
  						    ]   , -1   , )
  						return dummy_input
  
  
  
  
  class      __lowercase	(	__snake_case					):
  	_A   			=      ("_optimized", "_default")
  
  
  
  
  
  
  	def __init__(self							:   Dict			,  snake_case							:   int			,  snake_case							:   Any						)   -> Optional[Any]:
  							_lowercase					:  Optional[int]    			=      optimized
  							_lowercase					:  Optional[Any]    			=      default
  
  
  
  
  
  
  	def __call__(self							:   Union[str, Any]			,  *snake_case							:   Tuple			,  **snake_case							:   int						)   -> str:
  							if kwargs["past_key_values"] is None and self._default.config.use_cache:
  													_lowercase					:  Optional[Any]    			=      generate_past_key_values(self._default			,  kwargs["input_ids"].shape[0]			,  0						)
  							kwargs.pop("position_ids"			,  __lowerCamelCase						)
  							for k in list(kwargs.keys()						):
  													if kwargs[k] is None or isinstance(kwargs[k]			,  __lowerCamelCase						):
  																			kwargs.pop(__lowerCamelCase						)
  							_lowercase					:  List[Any]    			=      self._optimized(**__lowerCamelCase						)
  							_lowercase					:  Any    			=      outputs[0]
  							_lowercase					:  Any    			=      outputs[1]
  							_lowercase					:  Tuple    			=      CausalLMOutputWithPast(
  							    loss=__lowerCamelCase			,  logits=__lowerCamelCase			,  past_key_values=__lowerCamelCase			,  hidden_states=__lowerCamelCase			,  attentions=__lowerCamelCase			,  )
  							return fixed_output
  
  
  
  
  
  
  	def __getattr__(self							:   Any			,  snake_case							:   Dict						)   -> Union[str, Any]:
  							return getattr(self._default			,  __lowerCamelCase						)
  
  
  
  
  
  
  	def 			_a(self							:   Dict			,  snake_case							:   Optional[Any]			,  snake_case							:   Optional[int]=None			,  snake_case							:   Optional[Any]=None			,  snake_case							:   List[Any]=None			,  **snake_case							:   Tuple						)   -> int:
  							return self._default.prepare_inputs_for_generation(
  							    __lowerCamelCase			,  past_key_values=__lowerCamelCase			,  inputs_embeds=__lowerCamelCase			,  use_cache=__lowerCamelCase			,  **__lowerCamelCase						)
  
  
  
  
  
  
  	def 			_a(self							:   Optional[int]			,  snake_case							:   Union[str, Any]			,  snake_case							:   Dict						)   -> Tuple[Tuple[torch.Tensor]]:
  							return self._default._reorder_cache(__lowerCamelCase			,  __lowerCamelCase						)
  
  
  
  def 		UpperCamelCase    (					)      -> List[Any]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  Union[str, Any]    			=      argparse.ArgumentParser()
  						parser.add_argument(
  						    "--model_type"   , default=UpperCamelCase__   , type=UpperCamelCase__   , required=UpperCamelCase__   , help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()     )   , )
  						parser.add_argument(
  						    "--model_name_or_path"   , default=UpperCamelCase__   , type=UpperCamelCase__   , required=UpperCamelCase__   , help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(MODEL_CLASSES.keys()     )   , )
  
  						parser.add_argument("--prompt"   , type=UpperCamelCase__   , default=""     )
  						parser.add_argument("--length"   , type=UpperCamelCase__   , default=20     )
  						parser.add_argument("--stop_token"   , type=UpperCamelCase__   , default=UpperCamelCase__   , help="Token at which text generation is stopped"     )
  
  						parser.add_argument(
  						    "--temperature"   , type=UpperCamelCase__   , default=1.0   , help="temperature of 1.0 has no effect, lower tend toward greedy sampling"   , )
  						parser.add_argument(
  						    "--repetition_penalty"   , type=UpperCamelCase__   , default=1.0   , help="primarily useful for CTRL model; in that case, use 1.2"     )
  						parser.add_argument("--k"   , type=UpperCamelCase__   , default=0     )
  						parser.add_argument("--p"   , type=UpperCamelCase__   , default=0.9     )
  
  						parser.add_argument("--prefix"   , type=UpperCamelCase__   , default=""   , help="Text added prior to input."     )
  						parser.add_argument("--padding_text"   , type=UpperCamelCase__   , default=""   , help="Deprecated, the use of `--prefix` is preferred."     )
  						parser.add_argument("--xlm_language"   , type=UpperCamelCase__   , default=""   , help="Optional language when used with the XLM model."     )
  
  						parser.add_argument("--seed"   , type=UpperCamelCase__   , default=42   , help="random seed for initialization"     )
  						parser.add_argument("--no_cuda"   , action="store_true"   , help="Avoid using CUDA when available"     )
  						parser.add_argument("--num_return_sequences"   , type=UpperCamelCase__   , default=1   , help="The number of samples to generate."     )
  						parser.add_argument(
  						    "--fp16"   , action="store_true"   , help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"   , )
  						parser.add_argument("--jit"   , action="store_true"   , help="Whether or not to use jit trace to accelerate inference"     )
  						_lowercase					:  Union[str, Any]    			=      parser.parse_args()
  
  						_lowercase					:  List[str]    			=      torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"     )
  						_lowercase					:  Any    			=      0 if args.no_cuda else torch.cuda.device_count()
  
  						logger.warning(f"""device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}"""     )
  
  						set_seed(UpperCamelCase__     )
  
  						# Initialize the model and tokenizer
  						try:
  												_lowercase					:  Tuple    			=      args.model_type.lower()
  												_lowercase					:  str    			=      MODEL_CLASSES[args.model_type]
  						except KeyError:
  												raise KeyError("the model {} you specified is not supported. You are welcome to add it and open a PR :)"     )
  
  						_lowercase					:  Tuple    			=      tokenizer_class.from_pretrained(args.model_name_or_path     )
  						if tokenizer.pad_token is None:
  												_lowercase					:  str    			=      tokenizer.eos_token
  						_lowercase					:  Union[str, Any]    			=      model_class.from_pretrained(args.model_name_or_path     )
  						model.to(args.device     )
  
  						if args.fpaa:
  												model.half()
  						_lowercase					:  Dict    			=      getattr(model.config   , "max_position_embeddings"   , 0     )
  						_lowercase					:  List[Any]    			=      adjust_length_to_model(args.length   , max_sequence_length=UpperCamelCase__     )
  						logger.info(UpperCamelCase__     )
  
  						_lowercase					:  List[Any]    			=      args.prompt if args.prompt else input("Model prompt >>> "     )
  
  						# Different models need different input formatting and/or extra arguments
  						_lowercase					:  List[str]    			=      args.model_type in PREPROCESSING_FUNCTIONS.keys()
  						if requires_preprocessing:
  												_lowercase					:  List[Any]    			=      PREPROCESSING_FUNCTIONS.get(args.model_type     )
  												_lowercase					:  Tuple    			=      prepare_input(UpperCamelCase__   , UpperCamelCase__   , UpperCamelCase__   , UpperCamelCase__     )
  
  												if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
  																		_lowercase					:  Tuple    			=      {"add_space_before_punct_symbol": True}
  												else:
  																		_lowercase					:  Dict    			=      {}
  
  												_lowercase					:  List[Any]    			=      tokenizer.encode(
  												    UpperCamelCase__   , add_special_tokens=UpperCamelCase__   , return_tensors="pt"   , **UpperCamelCase__     )
  						else:
  												_lowercase					:  int    			=      args.prefix if args.prefix else args.padding_text
  												_lowercase					:  Any    			=      tokenizer.encode(prefix + prompt_text   , add_special_tokens=UpperCamelCase__   , return_tensors="pt"     )
  						_lowercase					:  str    			=      encoded_prompt.to(args.device     )
  
  						if encoded_prompt.size()[-1] == 0:
  												_lowercase					:  Union[str, Any]    			=      None
  						else:
  												_lowercase					:  Any    			=      encoded_prompt
  
  						if args.jit:
  												_lowercase					:  Union[str, Any]    			=      ["enable jit"]
  												_lowercase					:  List[Any]    			=      prepare_jit_inputs(UpperCamelCase__   , UpperCamelCase__   , UpperCamelCase__     )
  												torch._C._jit_set_texpr_fuser_enabled(UpperCamelCase__     )
  												_lowercase					:  Union[str, Any]    			=      False
  												if hasattr(UpperCamelCase__   , "forward"     ):
  																		_lowercase					:  List[Any]    			=      inspect.signature(model.forward     )
  												else:
  																		_lowercase					:  Union[str, Any]    			=      inspect.signature(model.__call__     )
  												_lowercase					:  str    			=      tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(UpperCamelCase__   , UpperCamelCase__     ) is not None     )
  												_lowercase					:  Any    			=      torch.jit.trace(UpperCamelCase__   , UpperCamelCase__   , strict=UpperCamelCase__     )
  												_lowercase					:  Dict    			=      torch.jit.freeze(traced_model.eval()     )
  												traced_model(*UpperCamelCase__     )
  												traced_model(*UpperCamelCase__     )
  
  												_lowercase					:  int    			=      _ModelFallbackWrapper(UpperCamelCase__   , UpperCamelCase__     )
  
  						_lowercase					:  Union[str, Any]    			=      model.generate(
  						    input_ids=UpperCamelCase__   , max_length=args.length + len(encoded_prompt[0]     )   , temperature=args.temperature   , top_k=args.k   , top_p=args.p   , repetition_penalty=args.repetition_penalty   , do_sample=UpperCamelCase__   , num_return_sequences=args.num_return_sequences   , )
  
  						# Remove the batch dimension when returning multiple sequences
  						if len(output_sequences.shape     ) > 2:
  												output_sequences.squeeze_()
  
  						_lowercase					:  List[str]    			=      []
  
  						for generated_sequence_idx, generated_sequence in enumerate(UpperCamelCase__     ):
  												print(f"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""     )
  												_lowercase					:  Optional[Any]    			=      generated_sequence.tolist()
  
  												# Decode text
  												_lowercase					:  List[Any]    			=      tokenizer.decode(UpperCamelCase__   , clean_up_tokenization_spaces=UpperCamelCase__     )
  
  												# Remove all text after the stop token
  												_lowercase					:  int    			=      text[: text.find(args.stop_token     ) if args.stop_token else None]
  
  												# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
  												_lowercase					:  int    			=      (
  												    prompt_text + text[len(tokenizer.decode(encoded_prompt[0]   , clean_up_tokenization_spaces=UpperCamelCase__     )     ) :]
  												)
  
  												generated_sequences.append(UpperCamelCase__     )
  												print(UpperCamelCase__     )
  
  						return generated_sequences
  
  
  if __name__ == "__main__":
  							main()
  
  
  
  
  
style_context: 
  from dataclasses import dataclass
  
  UpperCamelCase_	:    List[str]  					=       """0.1"""
  UpperCamelCase_	:    Optional[Any]  					=       """Lucia Harcekova"""
  
  
  
  
  @dataclass
  class      __lowercase	:
  	_A   			=      42
  	_A   			=      42
  	_A   			=      42
  
  
  
  
  
  
  	def __repr__(self							:   List[str]						)   -> str:
  							return F"""({self.offset}, {self.length}, {self.indicator})"""
  
  
  
  
  class      __lowercase	:
  
  
  
  
  
  
  	def __init__(self							:   Union[str, Any]			,  snake_case							:   int = 13			,  snake_case							:   int = 6						)   -> None:
  							_lowercase					:  int    			=      window_size
  							_lowercase					:  List[Any]    			=      lookahead_buffer_size
  							_lowercase					:  str    			=      self.window_size - self.lookahead_buffer_size
  
  
  
  
  
  
  	def 			_a(self							:   List[str]			,  snake_case							:   str						)   -> list[Token]:
  							_lowercase					:  List[Any]    			=      []
  							_lowercase					:  str    			=      ""
  
  							# while there are still characters in text to compress
  							while text:
  													# find the next encoding phrase
  													# - triplet with offset, length, indicator (the next encoding character)
  													_lowercase					:  Union[str, Any]    			=      self._find_encoding_token(snake_case			,  snake_case						)
  
  													# update the search buffer:
  													# - add new characters from text into it
  													# - check if size exceed the max search buffer size, if so, drop the
  													#   oldest elements
  													search_buffer += text[: token.length + 1]
  													if len(snake_case						) > self.search_buffer_size:
  																			_lowercase					:  Optional[int]    			=      search_buffer[-self.search_buffer_size :]
  
  													# update the text
  													_lowercase					:  str    			=      text[token.length + 1 :]
  
  													# append the token to output
  													output.append(snake_case						)
  
  							return output
  
  
  
  
  
  
  	def 			_a(self							:   Optional[Any]			,  snake_case							:   list[Token]						)   -> str:
  							_lowercase					:  Optional[int]    			=      ""
  
  							for token in tokens:
  													for _ in range(token.length						):
  																			output += output[-token.offset]
  													output += token.indicator
  
  							return output
  
  
  
  
  
  
  	def 			_a(self							:   Dict			,  snake_case							:   str			,  snake_case							:   str						)   -> Token:
  
  							if not text:
  													raise ValueError("We need some text to work with."						)
  
  							# Initialise result parameters to default values
  							_lowercase			,						_lowercase					:  Tuple    			=      0, 0
  
  							if not search_buffer:
  													return Token(snake_case			,  snake_case			,  text[length]						)
  
  							for i, character in enumerate(snake_case						):
  													_lowercase					:  Tuple    			=      len(snake_case						) - i
  													if character == text[0]:
  																			_lowercase					:  List[str]    			=      self._match_length_from_index(snake_case			,  snake_case			,  0			,  snake_case						)
  																			# if the found length is bigger than the current or if it's equal,
  																			# which means it's offset is smaller: update offset and length
  																			if found_length >= length:
  																									_lowercase			,						_lowercase					:  Optional[Any]    			=      found_offset, found_length
  
  							return Token(snake_case			,  snake_case			,  text[length]						)
  
  
  
  
  
  
  	def 			_a(self							:   Union[str, Any]			,  snake_case							:   str			,  snake_case							:   str			,  snake_case							:   int			,  snake_case							:   int						)   -> int:
  							if not text or text[text_index] != window[window_index]:
  													return 0
  							return 1 + self._match_length_from_index(
  							    snake_case			,  window + text[text_index]			,  text_index + 1			,  window_index + 1						)
  
  
  if __name__ == "__main__":
  							from doctest import testmod
  
  							testmod()
  							# Initialize compressor class
  							UpperCamelCase_	:    Optional[int]  					=       LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
  							# Example
  							UpperCamelCase_	:    Optional[int]  					=       """cabracadabrarrarrad"""
  							UpperCamelCase_	:    Tuple  					=       lzaa_compressor.compress(TEXT)
  							print(lzaa_compressor.compress("""ababcbababaa"""))
  							UpperCamelCase_	:    Union[str, Any]  					=       lzaa_compressor.decompress(compressed_text)
  							assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
  
  
  
  
  