





"""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


UpperCamelCase_     ={
    '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'},
}

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

						_UpperCamelCase    :      Optional[int]						     =					parser.parse_args()

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

						return args





def  a_       (							):
						transformers.utils.logging.set_verbosity_error()
						_UpperCamelCase    :      Optional[int]						     =					parse_args()

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

						_UpperCamelCase    :      Dict						     =					start_measure()
						_UpperCamelCase    :      Tuple						     =					model_cls.from_pretrained(args.model_name ,		device_map='''auto''' ,		**lowercase__     )
						_UpperCamelCase    :      Optional[int]						     =					end_measure(lowercase__     )
						log_measures(lowercase__ ,		'''Model loading'''     )

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

						_UpperCamelCase    :      str						     =					AutoTokenizer.from_pretrained(args.tokenizer_name     )

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

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

						_UpperCamelCase    :      List[Any]						     =					end_measure(lowercase__     )
						log_measures(lowercase__ ,		'''Model generation'''     )

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


if __name__ == "__main__":
						main()










"""simple docstring"""




def  a_       (							_lowercase     ):
						if len(_lowercase     ) > 1:
												_UpperCamelCase    :      List[str]						     =					len(_lowercase     ) // 2  # Finds the middle of the array
												_UpperCamelCase    :      Dict						     =					arr[
												    :middle_length
												]  # Creates an array of the elements in the first half.
												_UpperCamelCase    :      Union[str, Any]						     =					arr[
												    middle_length:
												]  # Creates an array of the elements in the second half.
												_UpperCamelCase    :      str						     =					len(_lowercase     )
												_UpperCamelCase    :      List[str]						     =					len(_lowercase     )
												merge(_lowercase     )  # Starts sorting the left.
												merge(_lowercase     )  # Starts sorting the right
												_UpperCamelCase    :      Dict						     =					0  # Left Counter
												_UpperCamelCase    :      Union[str, Any]						     =					0  # Right Counter
												_UpperCamelCase    :      Optional[int]						     =					0  # Position Counter
												while (
												    left_index < left_size and right_index < right_size
												):  # Runs until the lowers size of the left and right are sorted.
																		if left_array[left_index] < right_array[right_index]:
																								_UpperCamelCase    :      Dict						     =					left_array[left_index]
																								left_index += 1
																		else:
																								_UpperCamelCase    :      Tuple						     =					right_array[right_index]
																								right_index += 1
																		index += 1
												while (
												    left_index < left_size
												):  # Adds the left over elements in the left half of the array
																		_UpperCamelCase    :      int						     =					left_array[left_index]
																		left_index += 1
																		index += 1
												while (
												    right_index < right_size
												):  # Adds the left over elements in the right half of the array
																		_UpperCamelCase    :      Any						     =					right_array[right_index]
																		right_index += 1
																		index += 1
						return arr


if __name__ == "__main__":
						import doctest

						doctest.testmod()



