code: 
  
  """simple docstring"""
  import os
  
  from apache_beam.io.filesystems import FileSystems
  from apache_beam.pipeline import Pipeline
  
  from .logging import get_logger
  
  
  _A						=     2 << 20  # 2mb
  _A						=     get_logger(__name__)
  
  class 			lowerCamelCase  (					lowerCAmelCase__      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       str						=						self._options.get_all_options().get("""runner"""  )
          return runner in [None, "DirectRunner", "PortableRunner"]
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase=False						)  ->   Dict:
        UpperCAmelCase__ :       Optional[int]						=						FileSystems
        if fs.exists(lowerCAmelCase						):
              if force_upload:
                    logger.info(F"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."""						)
              else:
                    logger.info(F"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."""						)
                    return
        with fs.create(lowerCAmelCase						) as remote_file:
              with open(lowerCAmelCase ,  """rb"""						) as local_file:
                    UpperCAmelCase__ :       int						=						local_file.read(lowerCAmelCase						)
                    while chunk:
                          remote_file.write(lowerCAmelCase						)
                          UpperCAmelCase__ :       str						=						local_file.read(lowerCAmelCase						)
  
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase=False						)  ->   Any:
        UpperCAmelCase__ :       List[str]						=						FileSystems
        if os.path.exists(lowerCAmelCase						):
              if force_download:
                    logger.info(F"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."""						)
              else:
                    logger.info(F"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False."""						)
                    return
        with fs.open(lowerCAmelCase						) as remote_file:
              with open(lowerCAmelCase ,  """wb"""						) as local_file:
                    UpperCAmelCase__ :       Dict						=						remote_file.read(lowerCAmelCase						)
                    while chunk:
                          local_file.write(lowerCAmelCase						)
                          UpperCAmelCase__ :       List[Any]						=						remote_file.read(lowerCAmelCase						)
  
  
style_context: 
  
  """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
  
  
  _A						=     {
      """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"""},
  }
  
  _A						=     [
      """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__	(	)  ->   Optional[Any]:
        UpperCAmelCase__ :       int						=						argparse.ArgumentParser(description="""Run and time generations on a big model using Accelerate."""						)
        parser.add_argument("""model_name""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""The name of the model to try."""						)
        parser.add_argument(
            """--tokenizer_name""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""The name of the tokenizer (if different from the model."""						)
        parser.add_argument("""--is_causal""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""Whether or not the model is causal."""						)
        parser.add_argument(
            """--model_revision""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""The revision to use for the model checkpoint."""						)
        parser.add_argument("""--torch_dtype""" ,  type=lowerCAmelCase ,  default=lowerCAmelCase ,  help="""The dtype for the model."""						)
        parser.add_argument("""--disk_offload""" ,  action="""store_true"""						)
  
        UpperCAmelCase__ :       Union[str, Any]						=						parser.parse_args()
  
        # Sanitize args
        if args.model_name in DEFAULT_MODELS:
              UpperCAmelCase__ :       Any						=						DEFAULT_MODELS[args.model_name]
              UpperCAmelCase__ :       Tuple						=						defaults["""model"""]
              if args.tokenizer_name is None:
                    UpperCAmelCase__ :       Optional[Any]						=						defaults.get("""tokenizer""" ,  args.model_name						)
              if args.is_causal is None:
                    UpperCAmelCase__ :       List[Any]						=						defaults["""is_causal"""]
              if args.model_revision is None:
                    UpperCAmelCase__ :       Optional[int]						=						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:
              UpperCAmelCase__ :       str						=						args.model_name
        if args.model_revision is None:
              UpperCAmelCase__ :       List[str]						=						"""main"""
  
        return args
  
  
  
  
  def 				a__	(	)  ->   List[Any]:
        transformers.utils.logging.set_verbosity_error()
        UpperCAmelCase__ :       Optional[int]						=						parse_args()
  
        if args.torch_dtype is None:
              UpperCAmelCase__ :       List[str]						=						AutoConfig.from_pretrained(args.model_name						)
              UpperCAmelCase__ :       Optional[Any]						=						getattr(lowerCAmelCase ,  """torch_dtype""" ,  torch.floataa						)
        else:
              UpperCAmelCase__ :       Union[str, Any]						=						getattr(lowerCAmelCase ,  args.torch_dtype						)
        UpperCAmelCase__ :       List[Any]						=						AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
        UpperCAmelCase__ :       List[str]						=						{
            """torch_dtype""": torch_dtype,
            """revision""": args.model_revision,
        }
        if args.disk_offload:
              UpperCAmelCase__ :       List[str]						=						"""tmp_offload"""
              UpperCAmelCase__ :       int						=						True
  
        UpperCAmelCase__ :       Any						=						start_measure()
        UpperCAmelCase__ :       Optional[int]						=						model_cls.from_pretrained(args.model_name ,  device_map="""auto""" ,  **lowerCAmelCase						)
        UpperCAmelCase__ :       Any						=						end_measure(lowerCAmelCase						)
        log_measures(lowerCAmelCase ,  """Model loading"""						)
  
        UpperCAmelCase__ :       List[str]						=						compute_module_sizes(lowerCAmelCase						)
        UpperCAmelCase__ :       Dict						=						{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]
        UpperCAmelCase__ :       Optional[Any]						=						"""\n""".join([F"""- {device}: {size // 2**20}MiB""" for device, size in device_size.items()]						)
        print(F"""\nTheoretical use:\n{message}"""						)
  
        UpperCAmelCase__ :       Tuple						=						AutoTokenizer.from_pretrained(args.tokenizer_name						)
  
        UpperCAmelCase__ :       List[str]						=						start_measure()
        UpperCAmelCase__ :       List[Any]						=						[]
        UpperCAmelCase__ :       Tuple						=						[]
        UpperCAmelCase__ :       Optional[Any]						=						[]
        for prompt in PROMPTS:
              UpperCAmelCase__ :       Any						=						tokenizer(lowerCAmelCase ,  return_tensors="""pt"""						).to(0						)
              UpperCAmelCase__ :       List[str]						=						inputs["""input_ids"""][0].tolist()
              UpperCAmelCase__ :       Dict						=						time.time()
              UpperCAmelCase__ :       List[str]						=						model.generate(inputs["""input_ids"""]						)
              UpperCAmelCase__ :       str						=						time.time()
              UpperCAmelCase__ :       str						=						outputs[0].tolist()
              UpperCAmelCase__ :       Optional[Any]						=						len(lowerCAmelCase						) if outputs[: len(lowerCAmelCase						)] != tokens else len(lowerCAmelCase						) - len(lowerCAmelCase						)
              UpperCAmelCase__ :       Tuple						=						after_generate - before_generate
  
              UpperCAmelCase__ :       Optional[Any]						=						tokenizer.decode(lowerCAmelCase ,  skip_special_tokens=lowerCAmelCase						)
              texts_outs.append(lowerCAmelCase						)
              generation_times.append(lowerCAmelCase						)
              gen_tokens.append(lowerCAmelCase						)
              print(F"""Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n"""						)
  
        UpperCAmelCase__ :       int						=						end_measure(lowerCAmelCase						)
        log_measures(lowerCAmelCase ,  """Model generation"""						)
  
        UpperCAmelCase__ :       Any						=						[gen / tok for gen, tok in zip(lowerCAmelCase ,  lowerCAmelCase						)]
        UpperCAmelCase__ :       List[Any]						=						sum(lowerCAmelCase						) / len(lowerCAmelCase						)
        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"""						)
        UpperCAmelCase__ :       Any						=						sum(generation_times_per_token[1:]						) / (len(lowerCAmelCase						) - 1)
        print(F"""Average time of generation per token (excluding the first): {avg_gen:.2f}s"""						)
  
  
  if __name__ == "__main__":
      main()
  
  