text: #!/usr/bin/env python3
  # coding=utf-8
  # Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
  # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """ Conditional text generation with the auto-regressive models of the library (GPT/GPT-2/CTRL/Transformer-XL/XLNet)
  """
  
  
  import argparse
  import logging
  
  import numpy as np
  import torch
  
  from transformers import (
      CTRLLMHeadModel,
      CTRLTokenizer,
      GPT2LMHeadModel,
      GPT2Tokenizer,
      OpenAIGPTLMHeadModel,
      OpenAIGPTTokenizer,
      TransfoXLLMHeadModel,
      TransfoXLTokenizer,
      XLMTokenizer,
      XLMWithLMHeadModel,
      XLNetLMHeadModel,
      XLNetTokenizer,
  )
  
  
  logging.basicConfig(
      format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
      datefmt="%m/%d/%Y %H:%M:%S",
      level=logging.INFO,
  )
  logger = logging.getLogger(__name__)
  
  MAX_LENGTH = int(10000)  # Hardcoded max length to avoid infinite loop
  
  MODEL_CLASSES = {
      "gpt2": (GPT2LMHeadModel, GPT2Tokenizer),
      "ctrl": (CTRLLMHeadModel, CTRLTokenizer),
      "openai-gpt": (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
      "xlnet": (XLNetLMHeadModel, XLNetTokenizer),
      "transfo-xl": (TransfoXLLMHeadModel, TransfoXLTokenizer),
      "xlm": (XLMWithLMHeadModel, XLMTokenizer),
  }
  
  # 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
  PREFIX = """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 set_seed(args):
      np.random.seed(args.seed)
      torch.manual_seed(args.seed)
      if args.n_gpu > 0:
          torch.cuda.manual_seed_all(args.seed)
  
  
  #
  # Functions to prepare models' input
  #
  
  
  def prepare_ctrl_input(args, _, tokenizer, prompt_text):
      if args.temperature > 0.7:
          logger.info("CTRL typically works better with lower temperatures (and lower top_k).")
  
      encoded_prompt = tokenizer.encode(prompt_text, add_special_tokens=False)
      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 prepare_xlm_input(args, model, tokenizer, prompt_text):
      # kwargs = {"language": None, "mask_token_id": None}
  
      # Set the language
      use_lang_emb = hasattr(model.config, "use_lang_emb") and model.config.use_lang_emb
      if hasattr(model.config, "lang2id") and use_lang_emb:
          available_languages = model.config.lang2id.keys()
          if args.xlm_language in available_languages:
              language = args.xlm_language
          else:
              language = None
              while language not in available_languages:
                  language = input("Using XLM. Select language in " + str(list(available_languages)) + " >>> ")
  
          model.config.lang_id = model.config.lang2id[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 prepare_xlnet_input(args, _, tokenizer, prompt_text):
      prefix = args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
      prompt_text = prefix + prompt_text
      return prompt_text
  
  
  def prepare_transfoxl_input(args, _, tokenizer, prompt_text):
      prefix = args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
      prompt_text = prefix + prompt_text
      return prompt_text
  
  
  PREPROCESSING_FUNCTIONS = {
      "ctrl": prepare_ctrl_input,
      "xlm": prepare_xlm_input,
      "xlnet": prepare_xlnet_input,
      "transfo-xl": prepare_transfoxl_input,
  }
  
  
  def adjust_length_to_model(length, max_sequence_length):
      if length < 0 and max_sequence_length > 0:
          length = max_sequence_length
      elif 0 < max_sequence_length < length:
          length = max_sequence_length  # No generation bigger than model size
      elif length < 0:
          length = MAX_LENGTH  # avoid infinite loop
      return length
  
  
  def main():
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--model_type",
          default=None,
          type=str,
          required=True,
          help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
      )
      parser.add_argument(
          "--model_name_or_path",
          default=None,
          type=str,
          required=True,
          help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
      )
  
      parser.add_argument("--prompt", type=str, default="")
      parser.add_argument("--length", type=int, default=20)
      parser.add_argument("--stop_token", type=str, default=None, help="Token at which text generation is stopped")
  
      parser.add_argument(
          "--temperature",
          type=float,
          default=1.0,
          help="temperature of 1.0 has no effect, lower tend toward greedy sampling",
      )
      parser.add_argument(
          "--repetition_penalty", type=float, default=1.0, help="primarily useful for CTRL model; in that case, use 1.2"
      )
      parser.add_argument("--k", type=int, default=0)
      parser.add_argument("--p", type=float, default=0.9)
  
      parser.add_argument("--prefix", type=str, default="", help="Text added prior to input.")
      parser.add_argument("--padding_text", type=str, default="", help="Deprecated, the use of `--prefix` is preferred.")
      parser.add_argument("--xlm_language", type=str, default="", help="Optional language when used with the XLM model.")
  
      parser.add_argument("--seed", type=int, 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=int, 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",
      )
      args = parser.parse_args()
  
      args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
      args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()
  
      logger.warning(
          "device: %s, n_gpu: %s, 16-bits training: %s",
          args.device,
          args.n_gpu,
          args.fp16,
      )
  
      set_seed(args)
  
      # Initialize the model and tokenizer
      try:
          args.model_type = args.model_type.lower()
          model_class, tokenizer_class = 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 :)")
  
      tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
  
      # model_class - from above - is the NN model. model_name_or_path are the hyperparameters and will be autodownloaded as handled here.
      model = model_class.from_pretrained(args.model_name_or_path)
      model.to(args.device)
  
      if args.fp16:
          model.half()
  
      args.length = adjust_length_to_model(args.length, max_sequence_length=model.config.max_position_embeddings)
      logger.info(args)
  
      prompt_text = args.prompt if args.prompt else input("Model prompt >>> ")
  
      # Different models need different input formatting and/or extra arguments
      requires_preprocessing = args.model_type in PREPROCESSING_FUNCTIONS.keys()
      if requires_preprocessing:
          prepare_input = PREPROCESSING_FUNCTIONS.get(args.model_type)
          preprocessed_prompt_text = prepare_input(args, model, tokenizer, prompt_text)
  
          if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
              tokenizer_kwargs = {"add_space_before_punct_symbol": True}
          else:
              tokenizer_kwargs = {}
  
          encoded_prompt = tokenizer.encode(
              preprocessed_prompt_text, add_special_tokens=False, return_tensors="pt", **tokenizer_kwargs
          )
      else:
          prefix = args.prefix if args.prefix else args.padding_text
          encoded_prompt = tokenizer.encode(prefix + prompt_text, add_special_tokens=False, return_tensors="pt")
      encoded_prompt = encoded_prompt.to(args.device)
  
      if encoded_prompt.size()[-1] == 0:
          input_ids = None
      else:
          input_ids = encoded_prompt
  
      output_sequences = model.generate(
          input_ids=input_ids,
          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=True,
          num_return_sequences=args.num_return_sequences,
      )
  
      # Remove the batch dimension when returning multiple sequences
      if len(output_sequences.shape) > 2:
          output_sequences.squeeze_()
  
      generated_sequences = []
  
      for generated_sequence_idx, generated_sequence in enumerate(output_sequences):
          print("=== GENERATED SEQUENCE {} ===".format(generated_sequence_idx + 1))
          generated_sequence = generated_sequence.tolist()
  
          # Decode text
          text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)
  
          # Remove all text after the stop token
          text = 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
          total_sequence = (
              prompt_text + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)) :]
          )
  
          generated_sequences.append(total_sequence)
          print(total_sequence)
  
      return generated_sequences
  
  
  if __name__ == "__main__":
      main()
  