
"""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


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     UpperCamelCase_    (						):
					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     UpperCamelCase_    (						):
					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**2_0}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()


"""simple docstring"""




from itertools import permutations

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_ = 5     ):
					if gon_side < 3 or gon_side > 5:
										raise ValueError('gon_side must be in the range [3, 5]'     )

					# Since it's 16, we know 10 is on the outer ring
					# Put the big numbers at the end so that they are never the first number
					SCREAMING_SNAKE_CASE           =      list(range(gon_side + 1,	0,	-1     )     )
					SCREAMING_SNAKE_CASE           =      list(range(gon_side + 2,	gon_side * 2 + 1     )     )

					for perm in permutations(small_numbers + big_numbers     ):
										SCREAMING_SNAKE_CASE           =      generate_gon_ring(SCREAMING_SNAKE_CASE_,	list(SCREAMING_SNAKE_CASE_     )     )
										if is_magic_gon(SCREAMING_SNAKE_CASE_     ):
															return int(''.join(str(SCREAMING_SNAKE_CASE_     ) for n in numbers     )     )

					SCREAMING_SNAKE_CASE           =      f'''Magic {gon_side}-gon ring is impossible'''
					raise ValueError(SCREAMING_SNAKE_CASE_     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      [0] * (gon_side * 3)
					SCREAMING_SNAKE_CASE           =      perm[0:3]
					perm.append(perm[1]     )

					SCREAMING_SNAKE_CASE           =      1 if gon_side < 5 else 2

					for i in range(1,	len(SCREAMING_SNAKE_CASE_     ) // 3 + magic_number     ):
										SCREAMING_SNAKE_CASE           =      perm[2 * i + 1]
										SCREAMING_SNAKE_CASE           =      result[3 * i - 1]
										SCREAMING_SNAKE_CASE           =      perm[2 * i + 2]

					return result

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					if len(SCREAMING_SNAKE_CASE_     ) % 3 != 0:
										raise ValueError('a gon ring should have a length that is a multiple of 3'     )

					if min(numbers[::3]     ) != numbers[0]:
										return False

					SCREAMING_SNAKE_CASE           =      sum(numbers[:3]     )

					return all(sum(numbers[i : i + 3]     ) == total for i in range(3,	len(SCREAMING_SNAKE_CASE_     ),	3     )     )


if __name__ == "__main__":
	print(solution())
