code: 
  
  
  """simple docstring"""
  
  
  
  
  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
  
  
  __magic_name__	:   List[str]          =							{
      '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'},
  }
  
  __magic_name__	:   Any          =							[
      '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 			a_   (  ):
  			__lowerCamelCase        =			argparse.ArgumentParser(description="""Run and time generations on a big model using Accelerate."""    )
  			parser.add_argument("""model_name""", type=lowercase__, default=lowercase__, help="""The name of the model to try."""    )
  			parser.add_argument(
  			    """--tokenizer_name""", type=lowercase__, default=lowercase__, help="""The name of the tokenizer (if different from the model."""    )
  			parser.add_argument("""--is_causal""", type=lowercase__, default=lowercase__, help="""Whether or not the model is causal."""    )
  			parser.add_argument(
  			    """--model_revision""", type=lowercase__, default=lowercase__, help="""The revision to use for the model checkpoint."""    )
  			parser.add_argument("""--torch_dtype""", type=lowercase__, default=lowercase__, help="""The dtype for the model."""    )
  			parser.add_argument("""--disk_offload""", action="""store_true"""    )
  
  			__lowerCamelCase        =			parser.parse_args()
  
  			# Sanitize args
  			if args.model_name in DEFAULT_MODELS:
  						__lowerCamelCase        =			DEFAULT_MODELS[args.model_name]
  						__lowerCamelCase        =			defaults["""model"""]
  						if args.tokenizer_name is None:
  									__lowerCamelCase        =			defaults.get("""tokenizer""", args.model_name    )
  						if args.is_causal is None:
  									__lowerCamelCase        =			defaults["""is_causal"""]
  						if args.model_revision is None:
  									__lowerCamelCase        =			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:
  						__lowerCamelCase        =			args.model_name
  			if args.model_revision is None:
  						__lowerCamelCase        =			"""main"""
  
  			return args
  
  
  
  
  
  def 			a_   (  ):
  			transformers.utils.logging.set_verbosity_error()
  			__lowerCamelCase        =			parse_args()
  
  			if args.torch_dtype is None:
  						__lowerCamelCase        =			AutoConfig.from_pretrained(args.model_name    )
  						__lowerCamelCase        =			getattr(lowercase__, """torch_dtype""", torch.floataa    )
  			else:
  						__lowerCamelCase        =			getattr(lowercase__, args.torch_dtype    )
  			__lowerCamelCase        =			AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
  			__lowerCamelCase        =			{
  			    """torch_dtype""": torch_dtype,
  			    """revision""": args.model_revision,
  			}
  			if args.disk_offload:
  						__lowerCamelCase        =			"""tmp_offload"""
  						__lowerCamelCase        =			True
  
  			__lowerCamelCase        =			start_measure()
  			__lowerCamelCase        =			model_cls.from_pretrained(args.model_name, device_map="""auto""", **lowercase__    )
  			__lowerCamelCase        =			end_measure(lowercase__    )
  			log_measures(lowercase__, """Model loading"""    )
  
  			__lowerCamelCase        =			compute_module_sizes(lowercase__    )
  			__lowerCamelCase        =			{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]
  			__lowerCamelCase        =			"""\n""".join([f'- {device}: {size // 2**20}MiB' for device, size in device_size.items()]    )
  			print(f'\nTheoretical use:\n{message}'    )
  
  			__lowerCamelCase        =			AutoTokenizer.from_pretrained(args.tokenizer_name    )
  
  			__lowerCamelCase        =			start_measure()
  			__lowerCamelCase        =			[]
  			__lowerCamelCase        =			[]
  			__lowerCamelCase        =			[]
  			for prompt in PROMPTS:
  						__lowerCamelCase        =			tokenizer(lowercase__, return_tensors="""pt"""    ).to(0    )
  						__lowerCamelCase        =			inputs["""input_ids"""][0].tolist()
  						__lowerCamelCase        =			time.time()
  						__lowerCamelCase        =			model.generate(inputs["""input_ids"""]    )
  						__lowerCamelCase        =			time.time()
  						__lowerCamelCase        =			outputs[0].tolist()
  						__lowerCamelCase        =			len(lowercase__    ) if outputs[: len(lowercase__    )] != tokens else len(lowercase__    ) - len(lowercase__    )
  						__lowerCamelCase        =			after_generate - before_generate
  
  						__lowerCamelCase        =			tokenizer.decode(lowercase__, skip_special_tokens=lowercase__    )
  						texts_outs.append(lowercase__    )
  						generation_times.append(lowercase__    )
  						gen_tokens.append(lowercase__    )
  						print(f'Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n'    )
  
  			__lowerCamelCase        =			end_measure(lowercase__    )
  			log_measures(lowercase__, """Model generation"""    )
  
  			__lowerCamelCase        =			[gen / tok for gen, tok in zip(lowercase__, lowercase__    )]
  			__lowerCamelCase        =			sum(lowercase__    ) / len(lowercase__    )
  			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'    )
  			__lowerCamelCase        =			sum(generation_times_per_token[1:]    ) / (len(lowercase__    ) - 1)
  			print(f'Average time of generation per token (excluding the first): {avg_gen:.2f}s'    )
  
  
  if __name__ == "__main__":
  					main()
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import os
  
  
  
  
  
  def 			a_   (  ):
  			__lowerCamelCase        =			os.path.dirname(os.path.realpath(lowercase__    )    )
  			__lowerCamelCase        =			os.path.join(lowercase__, """triangle.txt"""    )
  
  			with open(lowercase__    ) as in_file:
  						__lowerCamelCase        =			[[int(lowercase__    ) for i in line.split()] for line in in_file]
  
  			while len(lowercase__    ) != 1:
  						__lowerCamelCase        =			triangle.pop()
  						__lowerCamelCase        =			triangle[-1]
  						for j in range(len(lowercase__    ) - 1    ):
  									curr_row[j] += max(last_row[j], last_row[j + 1]    )
  			return triangle[0][0]
  
  
  if __name__ == "__main__":
  					print(solution())
  
  
  
  
  