
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_ = 13  )       ->       str:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			""""""
   for c in s:
      if "A" <= c <= "Z":
         out += chr(ord("""A"""  ) + (ord(SCREAMING_SNAKE_CASE_  ) - ord("""A"""  ) + n) % 26  )
      elif "a" <= c <= "z":
         out += chr(ord("""a"""  ) + (ord(SCREAMING_SNAKE_CASE_  ) - ord("""a"""  ) + n) % 26  )
      else:
         out += c
   return out
def 				lowerCAmelCase_		(			)       ->       None:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			input("""Enter message: """  )

   _SCREAMING_SNAKE_CASE       	=			dencrypt(SCREAMING_SNAKE_CASE_      , 13  )
   print("""Encryption:"""      , SCREAMING_SNAKE_CASE_  )

   _SCREAMING_SNAKE_CASE       	=			dencrypt(SCREAMING_SNAKE_CASE_      , 13  )
   print("""Decryption: """      , SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()








'''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


UpperCamelCase__ :						Tuple    							=					{
    "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"},
}

UpperCamelCase__ :						int    							=					[
    "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 				lowerCAmelCase_		(			)       ->       str:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			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"""  )

   _SCREAMING_SNAKE_CASE       	=			parser.parse_args()

   # Sanitize args
   if args.model_name in DEFAULT_MODELS:
      _SCREAMING_SNAKE_CASE       	=			DEFAULT_MODELS[args.model_name]
      _SCREAMING_SNAKE_CASE       	=			defaults["""model"""]
      if args.tokenizer_name is None:
         _SCREAMING_SNAKE_CASE       	=			defaults.get("""tokenizer"""      , args.model_name  )
      if args.is_causal is None:
         _SCREAMING_SNAKE_CASE       	=			defaults["""is_causal"""]
      if args.model_revision is None:
         _SCREAMING_SNAKE_CASE       	=			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:
      _SCREAMING_SNAKE_CASE       	=			args.model_name
   if args.model_revision is None:
      _SCREAMING_SNAKE_CASE       	=			"""main"""

   return args
def 				lowerCAmelCase_		(			)       ->       Optional[int]:




   """simple docstring"""
   transformers.utils.logging.set_verbosity_error()
   _SCREAMING_SNAKE_CASE       	=			parse_args()

   if args.torch_dtype is None:
      _SCREAMING_SNAKE_CASE       	=			AutoConfig.from_pretrained(args.model_name  )
      _SCREAMING_SNAKE_CASE       	=			getattr(SCREAMING_SNAKE_CASE_      , """torch_dtype"""      , torch.floataa  )
   else:
      _SCREAMING_SNAKE_CASE       	=			getattr(SCREAMING_SNAKE_CASE_      , args.torch_dtype  )
   _SCREAMING_SNAKE_CASE       	=			AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
   _SCREAMING_SNAKE_CASE       	=			{
       """torch_dtype""": torch_dtype,
       """revision""": args.model_revision,
   }
   if args.disk_offload:
      _SCREAMING_SNAKE_CASE       	=			"""tmp_offload"""
      _SCREAMING_SNAKE_CASE       	=			True

   _SCREAMING_SNAKE_CASE       	=			start_measure()
   _SCREAMING_SNAKE_CASE       	=			model_cls.from_pretrained(args.model_name      , device_map="""auto"""      , **SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE       	=			end_measure(SCREAMING_SNAKE_CASE_  )
   log_measures(SCREAMING_SNAKE_CASE_      , """Model loading"""  )

   _SCREAMING_SNAKE_CASE       	=			compute_module_sizes(SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE       	=			{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]
   _SCREAMING_SNAKE_CASE       	=			"""\n""".join([F"- {device}: {size // 2**20}MiB" for device, size in device_size.items()]  )
   print(F"\nTheoretical use:\n{message}"  )

   _SCREAMING_SNAKE_CASE       	=			AutoTokenizer.from_pretrained(args.tokenizer_name  )

   _SCREAMING_SNAKE_CASE       	=			start_measure()
   _SCREAMING_SNAKE_CASE       	=			[]
   _SCREAMING_SNAKE_CASE       	=			[]
   _SCREAMING_SNAKE_CASE       	=			[]
   for prompt in PROMPTS:
      _SCREAMING_SNAKE_CASE       	=			tokenizer(SCREAMING_SNAKE_CASE_      , return_tensors="""pt"""  ).to(0  )
      _SCREAMING_SNAKE_CASE       	=			inputs["""input_ids"""][0].tolist()
      _SCREAMING_SNAKE_CASE       	=			time.time()
      _SCREAMING_SNAKE_CASE       	=			model.generate(inputs["""input_ids"""]  )
      _SCREAMING_SNAKE_CASE       	=			time.time()
      _SCREAMING_SNAKE_CASE       	=			outputs[0].tolist()
      _SCREAMING_SNAKE_CASE       	=			len(SCREAMING_SNAKE_CASE_  ) if outputs[: len(SCREAMING_SNAKE_CASE_  )] != tokens else len(SCREAMING_SNAKE_CASE_  ) - len(SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE       	=			after_generate - before_generate

      _SCREAMING_SNAKE_CASE       	=			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"  )

   _SCREAMING_SNAKE_CASE       	=			end_measure(SCREAMING_SNAKE_CASE_  )
   log_measures(SCREAMING_SNAKE_CASE_      , """Model generation"""  )

   _SCREAMING_SNAKE_CASE       	=			[gen / tok for gen, tok in zip(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )]
   _SCREAMING_SNAKE_CASE       	=			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"  )
   _SCREAMING_SNAKE_CASE       	=			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()






