




'''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      :      Optional[int]   							=		{
    '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      :      List[Any]   							=		[
    '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 		snake_case_    ():





							'''simple docstring'''


							_a	     =				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'''      )

							_a	     =				parser.parse_args()

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

							return args
def 		snake_case_    ():





							'''simple docstring'''


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

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

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

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

							_a	     =				AutoTokenizer.from_pretrained(args.tokenizer_name      )

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

														_a	     =				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'      )

							_a	     =				end_measure(UpperCamelCase      )
							log_measures(UpperCamelCase ,						'''Model generation'''      )

							_a	     =				[gen / tok for gen, tok in zip(UpperCamelCase ,						UpperCamelCase      )]
							_a	     =				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'      )
							_a	     =				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'''


def 		snake_case_    (UpperCamelCase       :  List[str] ,						UpperCamelCase       :  List[Any] ,						UpperCamelCase       :  Union[str, Any]      ):





							'''simple docstring'''


							_a	     =				index
							_a	     =				2 * index + 1
							_a	     =				2 * index + 2
							if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
														_a	     =				left_index

							if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
														_a	     =				right_index

							if largest != index:
														_a     ,							_a	     =				unsorted[index], unsorted[largest]
														heapify(UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase      )
def 		snake_case_    (UpperCamelCase       :  Union[str, Any]      ):





							'''simple docstring'''


							_a	     =				len(UpperCamelCase      )
							for i in range(n // 2 - 1 ,						-1 ,						-1      ):
														heapify(UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase      )
							for i in range(n - 1 ,						0 ,						-1      ):
														_a     ,							_a	     =				unsorted[i], unsorted[0]
														heapify(UpperCamelCase ,						0 ,						UpperCamelCase      )
							return unsorted


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

