code: 
  
  
  
  
  
  '''simple docstring'''
  
  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,
  )
  lowercase_			       =				logging.getLogger(__name__)
  
  lowercase_			       =				int(10_000)  # Hardcoded max length to avoid infinite loop
  
  lowercase_			       =				{
      """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
  lowercase_			       =				"""In 1991, the remains of Russian Tsar Nicholas II and his family
  (except for Alexei and Maria) are discovered.
  The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
  remainder of the story. 1883 Western Siberia,
  a young Grigori Rasputin is asked by his father and a group of men to perform magic.
  Rasputin has a vision and denounces one of the men as a horse thief. Although his
  father initially slaps him for making such an accusation, Rasputin watches as the
  man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
  the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
  with people, even a bishop, begging for his blessing. <eod> </s> <eos>"""
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Optional[Any]					)		->Tuple:
      np.random.seed(args.seed					)
      torch.manual_seed(args.seed					)
      if args.n_gpu > 0:
          torch.cuda.manual_seed_all(args.seed					)
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Optional[Any]      , __lowerCamelCase				:					List[str]      , __lowerCamelCase				:					Tuple      , __lowerCamelCase				:					str					)		->List[str]:
      if args.temperature > 0.7:
          logger.info("""CTRL typically works better with lower temperatures (and lower top_k)."""					)
  
      _SCREAMING_SNAKE_CASE             =    tokenizer.encode(__lowerCamelCase      , add_special_tokens=__lowerCamelCase					)
      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 lowerCamelCase		(  __lowerCamelCase				:					int      , __lowerCamelCase				:					Union[str, Any]      , __lowerCamelCase				:					Optional[Any]      , __lowerCamelCase				:					str					)		->str:
      # kwargs = {"language": None, "mask_token_id": None}
  
      # Set the language
      _SCREAMING_SNAKE_CASE             =    hasattr(model.config      , """use_lang_emb"""					) and model.config.use_lang_emb
      if hasattr(model.config      , """lang2id"""					) and use_lang_emb:
          _SCREAMING_SNAKE_CASE             =    model.config.langaid.keys()
          if args.xlm_language in available_languages:
              _SCREAMING_SNAKE_CASE             =    args.xlm_language
          else:
              _SCREAMING_SNAKE_CASE             =    None
              while language not in available_languages:
                  _SCREAMING_SNAKE_CASE             =    input("""Using XLM. Select language in """ + str(list(__lowerCamelCase					)					) + """ >>> """					)
  
          _SCREAMING_SNAKE_CASE             =    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 lowerCamelCase		(  __lowerCamelCase				:					Optional[Any]      , __lowerCamelCase				:					Optional[int]      , __lowerCamelCase				:					Dict      , __lowerCamelCase				:					int					)		->Dict:
      _SCREAMING_SNAKE_CASE             =    args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
      _SCREAMING_SNAKE_CASE             =    prefix + prompt_text
      return prompt_text
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Union[str, Any]      , __lowerCamelCase				:					str      , __lowerCamelCase				:					Optional[Any]      , __lowerCamelCase				:					List[str]					)		->Tuple:
      _SCREAMING_SNAKE_CASE             =    args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
      _SCREAMING_SNAKE_CASE             =    prefix + prompt_text
      return prompt_text
  
  
  lowercase_			       =				{
      """ctrl""": prepare_ctrl_input,
      """xlm""": prepare_xlm_input,
      """xlnet""": prepare_xlnet_input,
      """transfo-xl""": prepare_transfoxl_input,
  }
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					str      , __lowerCamelCase				:					Optional[int]					)		->str:
      if length < 0 and max_sequence_length > 0:
          _SCREAMING_SNAKE_CASE             =    max_sequence_length
      elif 0 < max_sequence_length < length:
          _SCREAMING_SNAKE_CASE             =    max_sequence_length  # No generation bigger than model size
      elif length < 0:
          _SCREAMING_SNAKE_CASE             =    MAX_LENGTH  # avoid infinite loop
      return length
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					List[Any]					)		->str:
      _SCREAMING_SNAKE_CASE             =    None
      if hasattr(__lowerCamelCase      , """hidden_size"""					):
          _SCREAMING_SNAKE_CASE             =    model_config.hidden_size
      elif hasattr(__lowerCamelCase      , """n_embed"""					):
          _SCREAMING_SNAKE_CASE             =    model_config.n_embed
      elif hasattr(__lowerCamelCase      , """n_embd"""					):
          _SCREAMING_SNAKE_CASE             =    model_config.n_embd
  
      _SCREAMING_SNAKE_CASE             =    None
      if hasattr(__lowerCamelCase      , """num_attention_heads"""					):
          _SCREAMING_SNAKE_CASE             =    model_config.num_attention_heads
      elif hasattr(__lowerCamelCase      , """n_head"""					):
          _SCREAMING_SNAKE_CASE             =    model_config.n_head
  
      if embedding_size is None or num_head is None or num_head == 0:
          raise ValueError("""Check the model config"""					)
  
      _SCREAMING_SNAKE_CASE             =    int(embedding_size / num_head					)
      if hasattr(__lowerCamelCase      , """n_layer"""					):
          _SCREAMING_SNAKE_CASE             =    model_config.n_layer
      elif hasattr(__lowerCamelCase      , """num_hidden_layers"""					):
          _SCREAMING_SNAKE_CASE             =    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 lowerCamelCase		(  __lowerCamelCase				:					Tuple      , __lowerCamelCase				:					Dict      , __lowerCamelCase				:					Tuple					)		->Any:
      _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    sparse_model_config(model.config					)
      if model.config.model_type == "bloom":
          _SCREAMING_SNAKE_CASE             =    tuple(
              (
                  torch.empty(int(num_attention_heads * batch_size					)      , __lowerCamelCase      , __lowerCamelCase					)
                  .to(model.dtype					)
                  .to(model.device					),
                  torch.empty(int(num_attention_heads * batch_size					)      , __lowerCamelCase      , __lowerCamelCase					)
                  .to(model.dtype					)
                  .to(model.device					),
              )
              for _ in range(__lowerCamelCase					)					)
      else:
          _SCREAMING_SNAKE_CASE             =    tuple(
              (
                  torch.empty(__lowerCamelCase      , __lowerCamelCase      , __lowerCamelCase      , __lowerCamelCase					)
                  .to(model.dtype					)
                  .to(model.device					),
                  torch.empty(__lowerCamelCase      , __lowerCamelCase      , __lowerCamelCase      , __lowerCamelCase					)
                  .to(model.dtype					)
                  .to(model.device					),
              )
              for _ in range(__lowerCamelCase					)					)
      return past_key_values
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					List[str]      , __lowerCamelCase				:					Any      , __lowerCamelCase				:					str					)		->List[Any]:
      _SCREAMING_SNAKE_CASE             =    len(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE             =    tokenizer.batch_encode_plus(__lowerCamelCase      , return_tensors="""pt"""					)
      _SCREAMING_SNAKE_CASE             =    dummy_input.to(model.device					)
      if model.config.use_cache:
          _SCREAMING_SNAKE_CASE             =    generate_past_key_values(__lowerCamelCase      , __lowerCamelCase      , 1					)
      _SCREAMING_SNAKE_CASE             =    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       a_  (       snake_case_ ):
  
      '''simple docstring'''
      UpperCamelCase									=				('''_optimized''', '''_default''')
  
  
      def __init__(	self				,	A				,	A )	->     List[str]:
          _SCREAMING_SNAKE_CASE             =    optimized
          _SCREAMING_SNAKE_CASE             =    default
  
  
      def __call__(	self				,	*A				,	**A )	->     str:
          if kwargs["past_key_values"] is None and self._default.config.use_cache:
              _SCREAMING_SNAKE_CASE             =    generate_past_key_values(self._default				,	kwargs["""input_ids"""].shape[0]				,	0 )
          kwargs.pop("""position_ids"""				,	A )
          for k in list(kwargs.keys() ):
              if kwargs[k] is None or isinstance(kwargs[k]				,	A ):
                  kwargs.pop(A )
          _SCREAMING_SNAKE_CASE             =    self._optimized(**A )
          _SCREAMING_SNAKE_CASE             =    outputs[0]
          _SCREAMING_SNAKE_CASE             =    outputs[1]
          _SCREAMING_SNAKE_CASE             =    CausalLMOutputWithPast(
              loss=A				,	logits=A				,	past_key_values=A				,	hidden_states=A				,	attentions=A				,	)
          return fixed_output
  
  
      def __getattr__(	self				,	A )	->     Optional[Any]:
          return getattr(self._default				,	A )
  
  
      def     snake_case_(	self				,	A				,	A=None				,	A=None				,	A=None				,	**A )	->     str:
          return self._default.prepare_inputs_for_generation(
              A				,	past_key_values=A				,	inputs_embeds=A				,	use_cache=A				,	**A )
  
  
  
  
  
      def     snake_case_(	self				,	A				,	A )	->     Tuple[Tuple[torch.Tensor]]:
          return self._default._reorder_cache(A				,	A )
  
  
  
  
  
  def lowerCamelCase		(  )		->List[Any]:
      _SCREAMING_SNAKE_CASE             =    argparse.ArgumentParser()
      parser.add_argument(
          """--model_type"""      , default=__lowerCamelCase      , type=__lowerCamelCase      , required=__lowerCamelCase      , help="""Model type selected in the list: """ + """, """.join(MODEL_CLASSES.keys()					)      , )
      parser.add_argument(
          """--model_name_or_path"""      , default=__lowerCamelCase      , type=__lowerCamelCase      , required=__lowerCamelCase      , help="""Path to pre-trained model or shortcut name selected in the list: """ + """, """.join(MODEL_CLASSES.keys()					)      , )
  
      parser.add_argument("""--prompt"""      , type=__lowerCamelCase      , default=""""""					)
      parser.add_argument("""--length"""      , type=__lowerCamelCase      , default=20					)
      parser.add_argument("""--stop_token"""      , type=__lowerCamelCase      , default=__lowerCamelCase      , help="""Token at which text generation is stopped"""					)
  
      parser.add_argument(
          """--temperature"""      , type=__lowerCamelCase      , default=1.0      , help="""temperature of 1.0 has no effect, lower tend toward greedy sampling"""      , )
      parser.add_argument(
          """--repetition_penalty"""      , type=__lowerCamelCase      , default=1.0      , help="""primarily useful for CTRL model; in that case, use 1.2"""					)
      parser.add_argument("""--k"""      , type=__lowerCamelCase      , default=0					)
      parser.add_argument("""--p"""      , type=__lowerCamelCase      , default=0.9					)
  
      parser.add_argument("""--prefix"""      , type=__lowerCamelCase      , default=""""""      , help="""Text added prior to input."""					)
      parser.add_argument("""--padding_text"""      , type=__lowerCamelCase      , default=""""""      , help="""Deprecated, the use of `--prefix` is preferred."""					)
      parser.add_argument("""--xlm_language"""      , type=__lowerCamelCase      , default=""""""      , help="""Optional language when used with the XLM model."""					)
  
      parser.add_argument("""--seed"""      , type=__lowerCamelCase      , 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=__lowerCamelCase      , 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"""					)
      _SCREAMING_SNAKE_CASE             =    parser.parse_args()
  
      _SCREAMING_SNAKE_CASE             =    torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""					)
      _SCREAMING_SNAKE_CASE             =    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(__lowerCamelCase					)
  
      # Initialize the model and tokenizer
      try:
          _SCREAMING_SNAKE_CASE             =    args.model_type.lower()
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    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 :)"""					)
  
      _SCREAMING_SNAKE_CASE             =    tokenizer_class.from_pretrained(args.model_name_or_path					)
      if tokenizer.pad_token is None:
          _SCREAMING_SNAKE_CASE             =    tokenizer.eos_token
      _SCREAMING_SNAKE_CASE             =    model_class.from_pretrained(args.model_name_or_path					)
      model.to(args.device					)
  
      if args.fpaa:
          model.half()
      _SCREAMING_SNAKE_CASE             =    getattr(model.config      , """max_position_embeddings"""      , 0					)
      _SCREAMING_SNAKE_CASE             =    adjust_length_to_model(args.length      , max_sequence_length=__lowerCamelCase					)
      logger.info(__lowerCamelCase					)
  
      _SCREAMING_SNAKE_CASE             =    args.prompt if args.prompt else input("""Model prompt >>> """					)
  
      # Different models need different input formatting and/or extra arguments
      _SCREAMING_SNAKE_CASE             =    args.model_type in PREPROCESSING_FUNCTIONS.keys()
      if requires_preprocessing:
          _SCREAMING_SNAKE_CASE             =    PREPROCESSING_FUNCTIONS.get(args.model_type					)
          _SCREAMING_SNAKE_CASE             =    prepare_input(__lowerCamelCase      , __lowerCamelCase      , __lowerCamelCase      , __lowerCamelCase					)
  
          if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
              _SCREAMING_SNAKE_CASE             =    {"""add_space_before_punct_symbol""": True}
          else:
              _SCREAMING_SNAKE_CASE             =    {}
  
          _SCREAMING_SNAKE_CASE             =    tokenizer.encode(
              __lowerCamelCase      , add_special_tokens=__lowerCamelCase      , return_tensors="""pt"""      , **__lowerCamelCase					)
      else:
          _SCREAMING_SNAKE_CASE             =    args.prefix if args.prefix else args.padding_text
          _SCREAMING_SNAKE_CASE             =    tokenizer.encode(prefix + prompt_text      , add_special_tokens=__lowerCamelCase      , return_tensors="""pt"""					)
      _SCREAMING_SNAKE_CASE             =    encoded_prompt.to(args.device					)
  
      if encoded_prompt.size()[-1] == 0:
          _SCREAMING_SNAKE_CASE             =    None
      else:
          _SCREAMING_SNAKE_CASE             =    encoded_prompt
  
      if args.jit:
          _SCREAMING_SNAKE_CASE             =    ["""enable jit"""]
          _SCREAMING_SNAKE_CASE             =    prepare_jit_inputs(__lowerCamelCase      , __lowerCamelCase      , __lowerCamelCase					)
          torch._C._jit_set_texpr_fuser_enabled(__lowerCamelCase					)
          _SCREAMING_SNAKE_CASE             =    False
          if hasattr(__lowerCamelCase      , """forward"""					):
              _SCREAMING_SNAKE_CASE             =    inspect.signature(model.forward					)
          else:
              _SCREAMING_SNAKE_CASE             =    inspect.signature(model.__call__					)
          _SCREAMING_SNAKE_CASE             =    tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(__lowerCamelCase      , __lowerCamelCase					) is not None					)
          _SCREAMING_SNAKE_CASE             =    torch.jit.trace(__lowerCamelCase      , __lowerCamelCase      , strict=__lowerCamelCase					)
          _SCREAMING_SNAKE_CASE             =    torch.jit.freeze(traced_model.eval()					)
          traced_model(*__lowerCamelCase					)
          traced_model(*__lowerCamelCase					)
  
          _SCREAMING_SNAKE_CASE             =    _ModelFallbackWrapper(__lowerCamelCase      , __lowerCamelCase					)
  
      _SCREAMING_SNAKE_CASE             =    model.generate(
          input_ids=__lowerCamelCase      , 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=__lowerCamelCase      , num_return_sequences=args.num_return_sequences      , )
  
      # Remove the batch dimension when returning multiple sequences
      if len(output_sequences.shape					) > 2:
          output_sequences.squeeze_()
  
      _SCREAMING_SNAKE_CASE             =    []
  
      for generated_sequence_idx, generated_sequence in enumerate(__lowerCamelCase					):
          print(F'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='					)
          _SCREAMING_SNAKE_CASE             =    generated_sequence.tolist()
  
          # Decode text
          _SCREAMING_SNAKE_CASE             =    tokenizer.decode(__lowerCamelCase      , clean_up_tokenization_spaces=__lowerCamelCase					)
  
          # Remove all text after the stop token
          _SCREAMING_SNAKE_CASE             =    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
          _SCREAMING_SNAKE_CASE             =    (
              prompt_text + text[len(tokenizer.decode(encoded_prompt[0]      , clean_up_tokenization_spaces=__lowerCamelCase					)					) :]
          )
  
          generated_sequences.append(__lowerCamelCase					)
          print(__lowerCamelCase					)
  
      return generated_sequences
  
  
  if __name__ == "__main__":
        main()
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import safs
  
  
  class       a_  (       safs.SaFileSystem ):
  
      '''simple docstring'''
  
  
  
  