






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


_a       =    {
    '''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'''},
}

_a       =    [
    '''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       ( )->    Optional[Any]:





						"""simple docstring"""



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

						_UpperCAmelCase							   =     parser.parse_args()

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

						return args


def __A       ( )->    List[str]:





						"""simple docstring"""



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

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

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

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

						_UpperCAmelCase							   =     AutoTokenizer.from_pretrained(args.tokenizer_name      )

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

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

						_UpperCAmelCase							   =     end_measure(__lowerCAmelCase      )
						log_measures(__lowerCAmelCase					,						'Model generation'      )

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


if __name__ == "__main__":
		main()








from __future__ import annotations


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						)->    list[float | int]:





						"""simple docstring"""



						if not all((nth_term, start_term_a, common_ratio_r)      ):
												return []
						_UpperCAmelCase							   =     []
						_UpperCAmelCase							   =     1
						_UpperCAmelCase							   =     common_ratio_r
						for _ in range(int(__lowerCAmelCase      )      ):
												if not series:
																		series.append(__lowerCAmelCase      )
												else:
																		power += 1
																		series.append(float(start_term_a * multiple      )      )
																		_UpperCAmelCase							   =     pow(float(__lowerCAmelCase      )					,						__lowerCAmelCase      )
						return series


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		_a       =    float(input('''Enter the last number (n term) of the Geometric Series'''))
		_a       =    float(input('''Enter the starting term (a) of the Geometric Series'''))
		_a       =    float(
		    input('''Enter the common ratio between two terms (r) of the Geometric Series''')
		)
		print('''Formula of Geometric Series => a + ar + ar^2 ... +ar^n''')
		print(geometric_series(nth_term, start_term_a, common_ratio_r))
