

'''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


lowerCAmelCase__      				=			{
    "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"},
}

lowerCAmelCase__      				=			[
    "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     SCREAMING_SNAKE_CASE(	)     ->     List[Any]:
	UpperCAmelCase_		:   str													=       argparse.ArgumentParser(description='Run and time generations on a big model using Accelerate.'	)
	parser.add_argument('model_name' ,type=UpperCamelCase ,default=UpperCamelCase ,help='The name of the model to try.'	)
	parser.add_argument(
	    '--tokenizer_name' ,type=UpperCamelCase ,default=UpperCamelCase ,help='The name of the tokenizer (if different from the model.'	)
	parser.add_argument('--is_causal' ,type=UpperCamelCase ,default=UpperCamelCase ,help='Whether or not the model is causal.'	)
	parser.add_argument(
	    '--model_revision' ,type=UpperCamelCase ,default=UpperCamelCase ,help='The revision to use for the model checkpoint.'	)
	parser.add_argument('--torch_dtype' ,type=UpperCamelCase ,default=UpperCamelCase ,help='The dtype for the model.'	)
	parser.add_argument('--disk_offload' ,action='store_true'	)

	UpperCAmelCase_		:   List[str]													=       parser.parse_args()

	# Sanitize args
	if args.model_name in DEFAULT_MODELS:
		UpperCAmelCase_		:   Optional[int]													=       DEFAULT_MODELS[args.model_name]
		UpperCAmelCase_		:   str													=       defaults['model']
		if args.tokenizer_name is None:
			UpperCAmelCase_		:   List[str]													=       defaults.get('tokenizer' ,args.model_name	)
		if args.is_causal is None:
			UpperCAmelCase_		:   Union[str, Any]													=       defaults['is_causal']
		if args.model_revision is None:
			UpperCAmelCase_		:   List[str]													=       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_		:   List[Any]													=       args.model_name
	if args.model_revision is None:
		UpperCAmelCase_		:   Optional[int]													=       'main'

	return args







def     SCREAMING_SNAKE_CASE(	)     ->     Any:
	transformers.utils.logging.set_verbosity_error()
	UpperCAmelCase_		:   List[str]													=       parse_args()

	if args.torch_dtype is None:
		UpperCAmelCase_		:   str													=       AutoConfig.from_pretrained(args.model_name	)
		UpperCAmelCase_		:   Union[str, Any]													=       getattr(UpperCamelCase ,'torch_dtype' ,torch.floataa	)
	else:
		UpperCAmelCase_		:   Dict													=       getattr(UpperCamelCase ,args.torch_dtype	)
	UpperCAmelCase_		:   List[str]													=       AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
	UpperCAmelCase_		:   Union[str, Any]													=       {
	    'torch_dtype': torch_dtype,
	    'revision': args.model_revision,
	}
	if args.disk_offload:
		UpperCAmelCase_		:   List[str]													=       'tmp_offload'
		UpperCAmelCase_		:   Dict													=       True

	UpperCAmelCase_		:   str													=       start_measure()
	UpperCAmelCase_		:   Any													=       model_cls.from_pretrained(args.model_name ,device_map='auto' ,**UpperCamelCase	)
	UpperCAmelCase_		:   List[Any]													=       end_measure(UpperCamelCase	)
	log_measures(UpperCamelCase ,'Model loading'	)

	UpperCAmelCase_		:   str													=       compute_module_sizes(UpperCamelCase	)
	UpperCAmelCase_		:   List[str]													=       {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_		:   List[Any]													=       '\n'.join([f"""- {device}: {size // 2**2_0}MiB""" for device, size in device_size.items()]	)
	print(f"""\nTheoretical use:\n{message}"""	)

	UpperCAmelCase_		:   Optional[Any]													=       AutoTokenizer.from_pretrained(args.tokenizer_name	)

	UpperCAmelCase_		:   List[Any]													=       start_measure()
	UpperCAmelCase_		:   str													=       []
	UpperCAmelCase_		:   str													=       []
	UpperCAmelCase_		:   Union[str, Any]													=       []
	for prompt in PROMPTS:
		UpperCAmelCase_		:   str													=       tokenizer(UpperCamelCase ,return_tensors='pt'	).to(0	)
		UpperCAmelCase_		:   str													=       inputs['input_ids'][0].tolist()
		UpperCAmelCase_		:   Union[str, Any]													=       time.time()
		UpperCAmelCase_		:   Optional[Any]													=       model.generate(inputs['input_ids']	)
		UpperCAmelCase_		:   Dict													=       time.time()
		UpperCAmelCase_		:   int													=       outputs[0].tolist()
		UpperCAmelCase_		:   List[Any]													=       len(UpperCamelCase	) if outputs[: len(UpperCamelCase	)] != tokens else len(UpperCamelCase	) - len(UpperCamelCase	)
		UpperCAmelCase_		:   Optional[int]													=       after_generate - before_generate

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

	UpperCAmelCase_		:   List[str]													=       end_measure(UpperCamelCase	)
	log_measures(UpperCamelCase ,'Model generation'	)

	UpperCAmelCase_		:   Union[str, Any]													=       [gen / tok for gen, tok in zip(UpperCamelCase ,UpperCamelCase	)]
	UpperCAmelCase_		:   List[Any]													=       sum(UpperCamelCase	) / len(UpperCamelCase	)
	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_		:   Optional[int]													=       sum(generation_times_per_token[1:]	) / (len(UpperCamelCase	) - 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 combinations, product







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)     ->     bool:
	if xa == ya == 0 or xa == ya == 0:
		return False
	UpperCAmelCase_		:   int													=       xa * xa + ya * ya
	UpperCAmelCase_		:   List[str]													=       xa * xa + ya * ya
	UpperCAmelCase_		:   Optional[Any]													=       (xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
	return (
	    a_square + b_square == c_square
	    or a_square + c_square == b_square
	    or b_square + c_square == a_square
	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase = 5_0	)     ->     int:
	return sum(
	    1
	    for pta, pta in combinations(product(range(limit + 1	) ,repeat=2	) ,2	)
	    if is_right(*UpperCamelCase ,*UpperCamelCase	)	)


if __name__ == "__main__":
						print(F'{solution() = }')






