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,
  )
  a     :			int					    =						logging.getLogger(__name__)
  def 		snake_case__  (		lowercase				):
     np.random.seed(args.seed				)
     torch.manual_seed(args.seed				)
     if args.n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed				)
  def 		snake_case__  (		):
     lowerCAmelCase_:       int         =  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"       ,							)
     lowerCAmelCase_:       str         =  parser.parse_args()
  
     lowerCAmelCase_:       Tuple         =  torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"				)
     lowerCAmelCase_:       str         =  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
     lowerCAmelCase_:       Any         =  AutoTokenizer.from_pretrained(args.model_name_or_path				)
     lowerCAmelCase_:       List[str]         =  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				)
     lowerCAmelCase_:       Dict         =  args.prompt if args.prompt else input("Model prompt >>> "				)
  
     lowerCAmelCase_:       Dict         =  tokenizer(lowercase       ,							return_tensors="pt"       ,							add_special_tokens=lowercase				)
     lowerCAmelCase_:       Union[str, Any]         =  {key: value.to(args.device				) for key, value in inputs.items()}
  
     lowerCAmelCase_:       Optional[Any]         =  model.generate(
         **lowercase       ,							max_length=args.length + len(inputs["input_ids"][0]				)       ,							penalty_alpha=args.penalty_alpha       ,							top_k=args.k       ,							)
  
     lowerCAmelCase_:       int         =  []
     for generated_sequence_idx, generated_sequence in enumerate(lowercase				):
        print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''				)
        lowerCAmelCase_:       Dict         =  generated_sequence.tolist()
  
        # Decode text
        lowerCAmelCase_:       Tuple         =  tokenizer.decode(lowercase       ,							clean_up_tokenization_spaces=lowercase       ,							add_special_tokens=lowercase				)
  
        # Remove all text after the stop token
        lowerCAmelCase_:       Optional[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
        lowerCAmelCase_:       Tuple         =  (
            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 ctypes import c_float, sizeof
  from enum import Enum
  from typing import TYPE_CHECKING, Optional, Union
  
  
  if TYPE_CHECKING:
   from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
  
  class 				_lowercase						(    UpperCAmelCase__ ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							Union[str, Any]        =						c_float
  
  
  
  
      @property
      def     _a						(						self   ):
         return sizeof(self.value   )
  
  
  
  
  
  def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase = 0				):
     # < 0 is possible if using a dynamic axis
     if dimension <= 0:
        lowerCAmelCase_:       int         =  fixed_dimension
  
     dimension -= num_token_to_add
     return dimension
  def 		snake_case__  (		lowercase       ,							lowercase				):
     return num_parameters * dtype.size
  
  
  
  
  
  
  
  def 		snake_case__  (		lowercase				):
     from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
  
     try:
        return AutoProcessor.from_pretrained(lowercase				)
     except (ValueError, OSError, KeyError):
        lowerCAmelCase_     ,  lowerCAmelCase_:       int         =  None, None
        try:
           lowerCAmelCase_:       Dict         =  AutoTokenizer.from_pretrained(lowercase				)
        except (OSError, KeyError):
           pass
        try:
           lowerCAmelCase_:       Optional[int]         =  AutoFeatureExtractor.from_pretrained(lowercase				)
        except (OSError, KeyError):
           pass
  
        if tokenizer is not None and feature_extractor is not None:
           raise ValueError(
               F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''				)
        elif tokenizer is None and feature_extractor is None:
           return None
        elif tokenizer is not None:
           return tokenizer
        else:
           return feature_extractor