code: 
  
  """simple docstring"""
  
  
  
  
  import argparse
  import logging
  
  import numpy as np
  import torch
  
  from transformers import AutoModelForCausalLM, AutoTokenizer
  
  
  logging.basicConfig(
      format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
      datefmt='%m/%d/%Y %H:%M:%S',
      level=logging.INFO,
  )
  __A  :					int												=	logging.getLogger(__name__)
  def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""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      __SCREAMING_SNAKE_CASE		(		):
  					"""simple docstring"""
  					A    =      argparse.ArgumentParser()
  					parser.add_argument(
  					    "--model_name_or_path"    ,							default=lowercase__    ,							type=lowercase__    ,							required=lowercase__    ,							)
  					parser.add_argument("--prompt"    ,							type=lowercase__    ,							default=""   )
  					parser.add_argument("--length"    ,							type=lowercase__    ,							default=20   )
  					parser.add_argument("--stop_token"    ,							type=lowercase__    ,							default=lowercase__    ,							help="Token at which text generation is stopped"   )
  					parser.add_argument(
  					    "--temperature"    ,							type=lowercase__    ,							default=1.0    ,							help="temperature of 1.0 has no effect, lower tend toward greedy sampling"    ,							)
  					parser.add_argument(
  					    "--repetition_penalty"    ,							type=lowercase__    ,							default=1.0    ,							help="primarily useful for CTRL model; in that case, use 1.2"   )
  					parser.add_argument("--k"    ,							type=lowercase__    ,							default=0   )
  					parser.add_argument("--penalty_alpha"    ,							type=lowercase__    ,							default=0.0   )
  					parser.add_argument("--p"    ,							type=lowercase__    ,							default=0.9   )
  
  					parser.add_argument("--prefix"    ,							type=lowercase__    ,							default=""    ,							help="Text added prior to input."   )
  					parser.add_argument("--padding_text"    ,							type=lowercase__    ,							default=""    ,							help="Deprecated, the use of `--prefix` is preferred."   )
  					parser.add_argument("--xlm_language"    ,							type=lowercase__    ,							default=""    ,							help="Optional language when used with the XLM model."   )
  
  					parser.add_argument("--seed"    ,							type=lowercase__    ,							default=42    ,							help="random seed for initialization"   )
  					parser.add_argument("--no_cuda"    ,							action="store_true"    ,							help="Avoid using CUDA when available"   )
  					parser.add_argument(
  					    "--fp16"    ,							action="store_true"    ,							help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"    ,							)
  					A    =      parser.parse_args()
  
  					A    =      torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"   )
  					A    =      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(lowercase__   )
  
  					# Initialize the model and tokenizer
  					A    =      AutoTokenizer.from_pretrained(args.model_name_or_path   )
  					A    =      AutoModelForCausalLM.from_pretrained(args.model_name_or_path   )
  
  					# tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
  					# model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
  					model.to(args.device   )
  
  					if args.fpaa:
  										model.half()
  
  					logger.info(lowercase__   )
  					A    =      args.prompt if args.prompt else input("Model prompt >>> "   )
  
  					A    =      tokenizer(lowercase__    ,							return_tensors="pt"    ,							add_special_tokens=lowercase__   )
  					A    =      {key: value.to(args.device   ) for key, value in inputs.items()}
  
  					A    =      model.generate(
  					    **lowercase__    ,							max_length=args.length + len(inputs["input_ids"][0]   )    ,							penalty_alpha=args.penalty_alpha    ,							top_k=args.k    ,							)
  
  					A    =      []
  					for generated_sequence_idx, generated_sequence in enumerate(lowercase__   ):
  										print(F"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""   )
  										A    =      generated_sequence.tolist()
  
  										# Decode text
  										A    =      tokenizer.decode(lowercase__    ,							clean_up_tokenization_spaces=lowercase__    ,							add_special_tokens=lowercase__   )
  
  										# Remove all text after the stop token
  										A    =      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
  										A    =      (
  										    prompt_text + text[len(tokenizer.decode(inputs["input_ids"][0]    ,							clean_up_tokenization_spaces=lowercase__   )   ) :]
  										)
  
  										generated_sequences.append(lowercase__   )
  										print(lowercase__   )
  
  					return generated_sequences
  
  
  if __name__ == "__main__":
  	main()
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  
  
  
  
  def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__   ):
  					"""simple docstring"""
  					A    =      1
  					while b > 0:
  										if b & 1:
  															res *= a
  
  										a *= a
  										b >>= 1
  
  					return res
  def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__   ):
  					"""simple docstring"""
  					A    =      1
  					while b > 0:
  										if b & 1:
  															A    =      ((res % c) * (a % c)) % c
  
  										a *= a
  										b >>= 1
  
  					return res
  
  
  
  
  
  