



'''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  						=			{
    """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  						=			[
    """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(      ) ->							Optional[int]:
							lowerCAmelCase__  =   argparse.ArgumentParser(description="""Run and time generations on a big model using Accelerate."""						)
							parser.add_argument("""model_name"""					, type=snake_case_					, default=snake_case_					, help="""The name of the model to try."""						)
							parser.add_argument(
							    """--tokenizer_name"""					, type=snake_case_					, default=snake_case_					, help="""The name of the tokenizer (if different from the model."""						)
							parser.add_argument("""--is_causal"""					, type=snake_case_					, default=snake_case_					, help="""Whether or not the model is causal."""						)
							parser.add_argument(
							    """--model_revision"""					, type=snake_case_					, default=snake_case_					, help="""The revision to use for the model checkpoint."""						)
							parser.add_argument("""--torch_dtype"""					, type=snake_case_					, default=snake_case_					, 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 			_lowerCAmelCase(      ) ->							str:
							transformers.utils.logging.set_verbosity_error()
							lowerCAmelCase__  =   parse_args()

							if args.torch_dtype is None:
														lowerCAmelCase__  =   AutoConfig.from_pretrained(args.model_name						)
														lowerCAmelCase__  =   getattr(snake_case_					, """torch_dtype"""					, torch.floataa						)
							else:
														lowerCAmelCase__  =   getattr(snake_case_					, 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"""					, **snake_case_						)
							lowerCAmelCase__  =   end_measure(snake_case_						)
							log_measures(snake_case_					, """Model loading"""						)

							lowerCAmelCase__  =   compute_module_sizes(snake_case_						)
							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(snake_case_					, 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(snake_case_						) if outputs[: len(snake_case_						)] != tokens else len(snake_case_						) - len(snake_case_						)
														lowerCAmelCase__  =   after_generate - before_generate

														lowerCAmelCase__  =   tokenizer.decode(snake_case_					, skip_special_tokens=snake_case_						)
														texts_outs.append(snake_case_						)
														generation_times.append(snake_case_						)
														gen_tokens.append(snake_case_						)
														print(F'''Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n'''						)

							lowerCAmelCase__  =   end_measure(snake_case_						)
							log_measures(snake_case_					, """Model generation"""						)

							lowerCAmelCase__  =   [gen / tok for gen, tok in zip(snake_case_					, snake_case_						)]
							lowerCAmelCase__  =   sum(snake_case_						) / len(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'''						)
							lowerCAmelCase__  =   sum(generation_times_per_token[1:]						) / (len(snake_case_						) - 1)
							print(F'''Average time of generation per token (excluding the first): {avg_gen:.2f}s'''						)


if __name__ == "__main__":
		main()













"""simple docstring"""





UpperCamelCase					=     [chr(i) for i in range(32, 1_26)]
UpperCamelCase					=     list(range(len(alphabets)))
UpperCamelCase					=     list(range(len(alphabets)))
UpperCamelCase					=     list(range(len(alphabets)))
UpperCamelCase					=     list(reversed(range(len(alphabets))))
UpperCamelCase					=     []
UpperCamelCase					=     UpperCamelCase					=     UpperCamelCase					=     0






def 							A(    ):
       """simple docstring"""





       global gear_one_pos
       global gear_two_pos
       global gear_three_pos
       lowercase__:							int         =     gear_one[0]
       gear_one.append(snake_case_						)
       del gear_one[0]
       gear_one_pos += 1
       if gear_one_pos % int(len(snake_case_						)						) == 0:
              lowercase__:							List[str]         =     gear_two[0]
              gear_two.append(snake_case_						)
              del gear_two[0]
              gear_two_pos += 1
              if gear_two_pos % int(len(snake_case_						)						) == 0:
                     lowercase__:							Dict         =     gear_three[0]
                     gear_three.append(snake_case_						)
                     del gear_three[0]
                     gear_three_pos += 1






def 							A(    snake_case_						):
       """simple docstring"""





       lowercase__:							List[Any]         =     alphabets.index(snake_case_						)
       lowercase__:							Dict         =     gear_one[target]
       lowercase__:							Tuple         =     gear_two[target]
       lowercase__:							str         =     gear_three[target]
       lowercase__:							Any         =     reflector[target]
       lowercase__:							Any         =     gear_three.index(snake_case_						)
       lowercase__:							Tuple         =     gear_two.index(snake_case_						)
       lowercase__:							Union[str, Any]         =     gear_one.index(snake_case_						)
       code.append(alphabets[target]						)
       rotator()


if __name__ == "__main__":
  UpperCamelCase					=     list(input("""Type your message:\n"""))
  while True:
    try:
      UpperCamelCase					=     int(input("""Please set token:(must be only digits)\n"""))
      break
    except Exception as error:
      print(error)
  for _ in range(token):
    rotator()
  for j in decode:
    engine(j)
  print("""\n""" + """""".join(code))
  print(
      F"\nYour Token is {token} please write it down.\nIf you want to decode "
      """this message again you should input same digits as token!"""
  )






