



'''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]:






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

							_lowercase							:		Union[str, Any]				=   parser.parse_args()

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

							return args





def 				_SCREAMING_SNAKE_CASE(					)		->Dict:






							'''simple docstring'''
							transformers.utils.logging.set_verbosity_error()
							_lowercase							:		List[Any]				=   parse_args()

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

							_lowercase							:		Union[str, Any]				=   start_measure()
							_lowercase							:		Any				=   model_cls.from_pretrained(args.model_name ,       device_map='''auto''' ,       **snake_case_  )
							_lowercase							:		Optional[Any]				=   end_measure(snake_case_  )
							log_measures(snake_case_ ,       '''Model loading'''  )

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

							_lowercase							:		Tuple				=   AutoTokenizer.from_pretrained(args.tokenizer_name  )

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

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

							_lowercase							:		Union[str, Any]				=   end_measure(snake_case_  )
							log_measures(snake_case_ ,       '''Model generation'''  )

							_lowercase							:		Any				=   [gen / tok for gen, tok in zip(snake_case_ ,       snake_case_  )]
							_lowercase							:		Optional[int]				=   sum(snake_case_  ) / len(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"  )
							_lowercase							:		List[str]				=   sum(generation_times_per_token[1:]  ) / (len(snake_case_  ) - 1)
							print(F"Average time of generation per token (excluding the first): {avg_gen:.2f}s"  )


if __name__ == "__main__":
						main()








'''simple docstring'''






def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     List[str]  )		->Dict:






							'''simple docstring'''
							_lowercase							:		Optional[Any]				=   [7_01, 3_01, 1_32, 57, 23, 10, 4, 1]
							for gap in gaps:
														for i in range(snake_case_ ,       len(snake_case_  )  ):
																					_lowercase							:		int				=   collection[i]
																					_lowercase							:		Any				=   i
																					while j >= gap and collection[j - gap] > insert_value:
																												_lowercase							:		Optional[int]				=   collection[j - gap]
																												j -= gap
																					if j != i:
																												_lowercase							:		str				=   insert_value
							return collection


if __name__ == "__main__":
						from doctest import testmod

						testmod()
						lowerCamelCase__	  =		input('Enter numbers separated by a comma:\n').strip()
						lowerCamelCase__	  =		[int(item) for item in user_input.split(',')]
						print(shell_sort(unsorted))



