


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 :	Dict												=     {
    '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 :	Tuple												=     [
    '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      (			)     ->						str:
		"""simple docstring"""




		lowercase__      =	argparse.ArgumentParser(description="""Run and time generations on a big model using Accelerate."""  )
		parser.add_argument("""model_name"""					,					type=__magic_name__					,					default=__magic_name__					,					help="""The name of the model to try."""  )
		parser.add_argument(
		    """--tokenizer_name"""					,					type=__magic_name__					,					default=__magic_name__					,					help="""The name of the tokenizer (if different from the model."""  )
		parser.add_argument("""--is_causal"""					,					type=__magic_name__					,					default=__magic_name__					,					help="""Whether or not the model is causal."""  )
		parser.add_argument(
		    """--model_revision"""					,					type=__magic_name__					,					default=__magic_name__					,					help="""The revision to use for the model checkpoint."""  )
		parser.add_argument("""--torch_dtype"""					,					type=__magic_name__					,					default=__magic_name__					,					help="""The dtype for the model."""  )
		parser.add_argument("""--disk_offload"""					,					action="""store_true"""  )

		lowercase__      =	parser.parse_args()

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

		return args






def 				UpperCamelCase      (			)     ->						Dict:
		"""simple docstring"""




		transformers.utils.logging.set_verbosity_error()
		lowercase__      =	parse_args()

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

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

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

		lowercase__      =	AutoTokenizer.from_pretrained(args.tokenizer_name  )

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

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

		lowercase__      =	end_measure(__magic_name__  )
		log_measures(__magic_name__					,					"""Model generation"""  )

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


if __name__ == "__main__":
	main()





import math






def 				UpperCamelCase      (			__magic_name__  :							int  )     ->						bool:
		"""simple docstring"""




		if 1 < number < 4:
				# 2 and 3 are primes
				return True
		elif number < 2 or number % 2 == 0 or number % 3 == 0:
				# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
				return False

		# All primes number are in format of 6k +/- 1
		for i in range(5					,					int(math.sqrt(__magic_name__  ) + 1  )					,					6  ):
				if number % i == 0 or number % (i + 2) == 0:
						return False
		return True






def 				UpperCamelCase      (			__magic_name__  :							int = 1000					,					__magic_name__  :							int = 1000  )     ->						int:
		"""simple docstring"""




		lowercase__      =	[0, 0, 0]  # length, a, b
		for a in range((a_limit * -1) + 1					,					__magic_name__  ):
				for b in range(2					,					__magic_name__  ):
						if is_prime(__magic_name__  ):
								lowercase__      =	0
								lowercase__      =	0
								while is_prime((n**2) + (a * n) + b  ):
										count += 1
										n += 1
								if count > longest[0]:
										lowercase__      =	[count, a, b]
		lowercase__      =	longest[1] * longest[2]
		return ans


if __name__ == "__main__":
	print(solution(1_0_0_0, 1_0_0_0))

