

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 ():
						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'''	)

						snake_case_       =     parser.parse_args()

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

						return args


def   __SCREAMING_SNAKE_CASE ():
						transformers.utils.logging.set_verbosity_error()
						snake_case_       =     parse_args()

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

						snake_case_       =     start_measure()
						snake_case_       =     model_cls.from_pretrained(args.model_name  ,		device_map='''auto'''  ,		**SCREAMING_SNAKE_CASE__	)
						snake_case_       =     end_measure(SCREAMING_SNAKE_CASE__	)
						log_measures(SCREAMING_SNAKE_CASE__  ,		'''Model loading'''	)

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

						snake_case_       =     AutoTokenizer.from_pretrained(args.tokenizer_name	)

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

												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'''	)

						snake_case_       =     end_measure(SCREAMING_SNAKE_CASE__	)
						log_measures(SCREAMING_SNAKE_CASE__  ,		'''Model generation'''	)

						snake_case_       =     [gen / tok for gen, tok in zip(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	)]
						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'''	)
						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()


from __future__ import annotations

from random import randrange


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
						if len(SCREAMING_SNAKE_CASE__	) < 2:
												return collection
						snake_case_       =     randrange(len(SCREAMING_SNAKE_CASE__	)	)  # Use random element as pivot
						snake_case_       =     collection[pivot_index]
						snake_case_       =     []  # All elements greater than pivot
						snake_case_       =     []  # All elements less than or equal to pivot

						for element in collection[:pivot_index]:
												(greater if element > pivot else lesser).append(SCREAMING_SNAKE_CASE__	)

						for element in collection[pivot_index + 1 :]:
												(greater if element > pivot else lesser).append(SCREAMING_SNAKE_CASE__	)

						return [*quick_sort(SCREAMING_SNAKE_CASE__	), pivot, *quick_sort(SCREAMING_SNAKE_CASE__	)]


if __name__ == "__main__":
					lowerCAmelCase_	     =     input('''Enter numbers separated by a comma:\n''').strip()
					lowerCAmelCase_	     =     [int(item) for item in user_input.split(''',''')]
					print(quick_sort(unsorted))