code: 
  
  import argparse
  import time
  
  import torch
  import transformers
  from measures_util import end_measure, log_measures, start_measure
  from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForSeqaSeqLM, AutoTokenizer
  
  from accelerate.utils import compute_module_sizes
  
  
  snake_case_    						=				{
      '''gpt-j-6b''': {'''is_causal''': True, '''model''': '''sgugger/sharded-gpt-j-6B''', '''tokenizer''': '''EleutherAI/gpt-j-6B'''},
      '''gpt-neox''': {'''is_causal''': True, '''model''': '''EleutherAI/gpt-neox-20b'''},
      '''opt''': {'''is_causal''': True, '''model''': '''facebook/opt-30b'''},
      '''T0pp''': {'''is_causal''': False, '''model''': '''bigscience/T0pp''', '''model_revision''': '''sharded'''},
  }
  
  snake_case_    						=				[
      '''Hello, my name is''',
      '''Are unicorns real? Unicorns are''',
      '''For the first time in several years,''',
      '''My name is Julien and I am''',
      '''The goal of life is''',
      '''Whenever I\'m sad, I like to''',
  ]
  
  def 			snake_case__    (					):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Tuple   = argparse.ArgumentParser(description='Run and time generations on a big model using Accelerate.'    )
       parser.add_argument('model_name'			,  type=SCREAMING_SNAKE_CASE_			,  default=SCREAMING_SNAKE_CASE_			,  help='The name of the model to try.'    )
       parser.add_argument(
           '--tokenizer_name'			,  type=SCREAMING_SNAKE_CASE_			,  default=SCREAMING_SNAKE_CASE_			,  help='The name of the tokenizer (if different from the model.'    )
       parser.add_argument('--is_causal'			,  type=SCREAMING_SNAKE_CASE_			,  default=SCREAMING_SNAKE_CASE_			,  help='Whether or not the model is causal.'    )
       parser.add_argument(
           '--model_revision'			,  type=SCREAMING_SNAKE_CASE_			,  default=SCREAMING_SNAKE_CASE_			,  help='The revision to use for the model checkpoint.'    )
       parser.add_argument('--torch_dtype'			,  type=SCREAMING_SNAKE_CASE_			,  default=SCREAMING_SNAKE_CASE_			,  help='The dtype for the model.'    )
       parser.add_argument('--disk_offload'			,  action='store_true'    )
  
       lowercase__					:     str   = parser.parse_args()
  
       # Sanitize args
       if args.model_name in DEFAULT_MODELS:
            lowercase__					:     Optional[Any]   = DEFAULT_MODELS[args.model_name]
            lowercase__					:     str   = defaults['model']
            if args.tokenizer_name is None:
                 lowercase__					:     Tuple   = defaults.get('tokenizer'			,  args.model_name    )
            if args.is_causal is None:
                 lowercase__					:     Dict   = defaults['is_causal']
            if args.model_revision is None:
                 lowercase__					:     Tuple   = defaults.get('model_revision'			,  'main'    )
  
       if args.is_causal is None:
            raise ValueError('Could not infer the default for `--is_causal`, pass either True or False for it.'    )
       if args.tokenizer_name is None:
            lowercase__					:     Optional[int]   = args.model_name
       if args.model_revision is None:
            lowercase__					:     List[Any]   = 'main'
  
       return args
  
  def 			snake_case__    (					):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       transformers.utils.logging.set_verbosity_error()
       lowercase__					:     Any   = parse_args()
  
       if args.torch_dtype is None:
            lowercase__					:     Dict   = AutoConfig.from_pretrained(args.model_name    )
            lowercase__					:     Any   = getattr(SCREAMING_SNAKE_CASE_			,  'torch_dtype'			,  torch.floataa    )
       else:
            lowercase__					:     Optional[Any]   = getattr(SCREAMING_SNAKE_CASE_			,  args.torch_dtype    )
       lowercase__					:     Optional[Any]   = AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
       lowercase__					:     List[Any]   = {
           'torch_dtype': torch_dtype,
           'revision': args.model_revision,
       }
       if args.disk_offload:
            lowercase__					:     List[Any]   = 'tmp_offload'
            lowercase__					:     str   = True
  
       lowercase__					:     Optional[int]   = start_measure()
       lowercase__					:     Optional[Any]   = model_cls.from_pretrained(args.model_name			,  device_map='auto'			,  **SCREAMING_SNAKE_CASE_    )
       lowercase__					:     str   = end_measure(SCREAMING_SNAKE_CASE_    )
       log_measures(SCREAMING_SNAKE_CASE_			,  'Model loading'    )
  
       lowercase__					:     List[str]   = compute_module_sizes(SCREAMING_SNAKE_CASE_    )
       lowercase__					:     Optional[int]   = {v: 0 for v in model.hf_device_map.values()}
       for module, device in model.hf_device_map.items():
            device_size[device] += module_sizes[module]
       lowercase__					:     List[Any]   = '\n'.join([f"""- {device}: {size // 2**20}MiB""" for device, size in device_size.items()]    )
       print(f"""\nTheoretical use:\n{message}"""    )
  
       lowercase__					:     List[str]   = AutoTokenizer.from_pretrained(args.tokenizer_name    )
  
       lowercase__					:     List[str]   = start_measure()
       lowercase__					:     int   = []
       lowercase__					:     List[str]   = []
       lowercase__					:     str   = []
       for prompt in PROMPTS:
            lowercase__					:     Optional[int]   = tokenizer(SCREAMING_SNAKE_CASE_			,  return_tensors='pt'    ).to(0    )
            lowercase__					:     Any   = inputs['input_ids'][0].tolist()
            lowercase__					:     Tuple   = time.time()
            lowercase__					:     Any   = model.generate(inputs['input_ids']    )
            lowercase__					:     List[str]   = time.time()
            lowercase__					:     int   = outputs[0].tolist()
            lowercase__					:     Any   = len(SCREAMING_SNAKE_CASE_    ) if outputs[: len(SCREAMING_SNAKE_CASE_    )] != tokens else len(SCREAMING_SNAKE_CASE_    ) - len(SCREAMING_SNAKE_CASE_    )
            lowercase__					:     List[str]   = after_generate - before_generate
  
            lowercase__					:     Dict   = tokenizer.decode(SCREAMING_SNAKE_CASE_			,  skip_special_tokens=SCREAMING_SNAKE_CASE_    )
            texts_outs.append(SCREAMING_SNAKE_CASE_    )
            generation_times.append(SCREAMING_SNAKE_CASE_    )
            gen_tokens.append(SCREAMING_SNAKE_CASE_    )
            print(f"""Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n"""    )
  
       lowercase__					:     Union[str, Any]   = end_measure(SCREAMING_SNAKE_CASE_    )
       log_measures(SCREAMING_SNAKE_CASE_			,  'Model generation'    )
  
       lowercase__					:     List[Any]   = [gen / tok for gen, tok in zip(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )]
       lowercase__					:     Tuple   = sum(SCREAMING_SNAKE_CASE_    ) / len(SCREAMING_SNAKE_CASE_    )
       print(f"""Average time of generation per token: {avg_gen:.2f}s"""    )
       print(f"""First generation (avg time per token): {generation_times_per_token[0]:.2f}s"""    )
       lowercase__					:     List[Any]   = sum(generation_times_per_token[1:]    ) / (len(SCREAMING_SNAKE_CASE_    ) - 1)
       print(f"""Average time of generation per token (excluding the first): {avg_gen:.2f}s"""    )
  
  
  if __name__ == "__main__":
         main()
  
  
style_context: 
  
  from __future__ import annotations
  
  
  class    SCREAMING_SNAKE_CASE__      :
  
  
  
  
  
      def __init__(   self		,     a = 0):
           lowercase__					:     int   = value
           lowercase__					:     Node | None   = None
           lowercase__					:     Node | None   = None
  
  
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Node | None			,  SCREAMING_SNAKE_CASE_	:					Node | None    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       if treea is None:
            return treea
       if treea is None:
            return treea
  
       lowercase__					:     Optional[Any]   = treea.value + treea.value
       lowercase__					:     Union[str, Any]   = merge_two_binary_trees(treea.left			,  treea.left    )
       lowercase__					:     Optional[Any]   = merge_two_binary_trees(treea.right			,  treea.right    )
       return treea
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Node | None    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       if root:
            print(root.value    )
            print_preorder(root.left    )
            print_preorder(root.right    )
  
  
  if __name__ == "__main__":
         snake_case_    						=				Node(1)
         snake_case_    						=				Node(2)
         snake_case_    						=				Node(3)
         snake_case_    						=				Node(4)
  
         snake_case_    						=				Node(2)
         snake_case_    						=				Node(4)
         snake_case_    						=				Node(6)
         snake_case_    						=				Node(9)
         snake_case_    						=				Node(5)
  
         print('''Tree1 is: ''')
         print_preorder(treea)
         print('''Tree2 is: ''')
         print_preorder(treea)
         snake_case_    						=				merge_two_binary_trees(treea, treea)
         print('''Merged Tree is: ''')
         print_preorder(merged_tree)
  
  