code: 
  
  
  
  
  
  
  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,
  )
  lowerCamelCase										=  logging.getLogger(__name__)
  def        UpperCAmelCase      (   _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        UpperCAmelCase      (   ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	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',      )
      _lowercase     =	parser.parse_args()
  
      _lowercase     =	torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'      )
      _lowercase     =	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
      _lowercase     =	AutoTokenizer.from_pretrained(args.model_name_or_path      )
      _lowercase     =	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      )
      _lowercase     =	args.prompt if args.prompt else input('Model prompt >>> '      )
  
      _lowercase     =	tokenizer(_lowercase,      return_tensors='pt',      add_special_tokens=_lowercase      )
      _lowercase     =	{key: value.to(args.device      ) for key, value in inputs.items()}
  
      _lowercase     =	model.generate(
          **_lowercase,      max_length=args.length + len(inputs['input_ids'][0]      ),      penalty_alpha=args.penalty_alpha,      top_k=args.k,      )
  
      _lowercase     =	[]
      for generated_sequence_idx, generated_sequence in enumerate(_lowercase      ):
          print(f'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''      )
          _lowercase     =	generated_sequence.tolist()
  
          # Decode text
          _lowercase     =	tokenizer.decode(_lowercase,      clean_up_tokenization_spaces=_lowercase,      add_special_tokens=_lowercase      )
  
          # Remove all text after the stop token
          _lowercase     =	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     =	(
              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: 
  
  
  
  
  
  
  from __future__ import annotations
  
  import sys
  
  class 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	Any   ,       snake_case__:	str   ,       snake_case__:	int	)  ->		Optional[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	letter
         _lowercase     =	freq
         _lowercase     =	{}
  
     def __repr__(    self:	Optional[int]	)  ->		str:
  
  
         """simple docstring"""
  
  
  
         return f'''{self.letter}:{self.freq}'''
  
  
  
  
  
  
  class 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	Optional[int]   ,       snake_case__:	int   ,       snake_case__:	Letter | TreeNode   ,       snake_case__:	Letter | TreeNode	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	freq
         _lowercase     =	left
         _lowercase     =	right
  
  
  
  
  
  
  def        UpperCAmelCase      (   _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	{}
      with open(_lowercase      ) as f:
          while True:
              _lowercase     =	f.read(1      )
              if not c:
                  break
              _lowercase     =	chars[c] + 1 if c in chars else 1
      return sorted((Letter(_lowercase,      _lowercase      ) for c, f in chars.items()),      key=lambda _lowercase      : x.freq      )
  def        UpperCAmelCase      (   _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	letters  # type: ignore
      while len(_lowercase      ) > 1:
          _lowercase     =	response.pop(0      )
          _lowercase     =	response.pop(0      )
          _lowercase     =	left.freq + right.freq
          _lowercase     =	TreeNode(_lowercase,      _lowercase,      _lowercase      )
          response.append(_lowercase      )
          response.sort(key=lambda _lowercase      : x.freq      )
      return response[0]
  def        UpperCAmelCase      (   _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      if isinstance(_lowercase,      _lowercase      ):
          _lowercase     =	bitstring
          return [root]
      _lowercase     =	root  # type: ignore
      _lowercase     =	[]
      letters += traverse_tree(treenode.left,      bitstring + '0'      )
      letters += traverse_tree(treenode.right,      bitstring + '1'      )
      return letters
  def        UpperCAmelCase      (   _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	parse_file(_lowercase      )
      _lowercase     =	build_tree(_lowercase      )
      _lowercase     =	{
          k: v for letter in traverse_tree(_lowercase,      ''      ) for k, v in letter.bitstring.items()
      }
      print(f'''Huffman Coding  of {file_path}: '''      )
      with open(_lowercase      ) as f:
          while True:
              _lowercase     =	f.read(1      )
              if not c:
                  break
              print(letters[c],      end=' '      )
      print()
  
  
  if __name__ == "__main__":
     # pass the file path to the huffman function
     huffman(sys.argv[1])
  