



'''simple docstring'''
import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
					import torch_xla.core.xla_model as xm






def A__					(  UpperCAmelCase_						,						UpperCAmelCase_							):







						if isinstance(UpperCAmelCase_						,						(list, tuple)							):
												return honor_type(UpperCAmelCase_						,						(move_to_device(UpperCAmelCase_						,						UpperCAmelCase_							) for t in state)							)
						elif isinstance(UpperCAmelCase_						,						UpperCAmelCase_							):
												return type(UpperCAmelCase_							)({k: move_to_device(UpperCAmelCase_						,						UpperCAmelCase_							) for k, v in state.items()}							)
						elif isinstance(UpperCAmelCase_						,						torch.Tensor							):
												return state.to(UpperCAmelCase_							)
						return state






class 							lowercase__			(							torch.optim.Optimizer     ):





	def __init__(				self      :       Tuple				,lowerCamelCase__      :       Any				,lowerCamelCase__      :       Dict=True				,lowerCamelCase__      :       int=None				):







							'''simple docstring'''
							_UpperCamelCase      :	int				       =     optimizer
							_UpperCamelCase      :	str				       =     scaler
							_UpperCamelCase      :	List[Any]				       =     AcceleratorState()
							_UpperCamelCase      :	Optional[int]				       =     GradientState()
							_UpperCamelCase      :	Any				       =     device_placement
							_UpperCamelCase      :	Union[str, Any]				       =     False
							_UpperCamelCase      :	Dict				       =     None

							# Handle device placement
							if device_placement:
													_UpperCamelCase      :	str				       =     self.optimizer.state_dict()
													if self.accelerator_state.distributed_type == DistributedType.TPU:
																			xm.send_cpu_data_to_device(lowerCamelCase__				,self.accelerator_state.device				)
													else:
																			_UpperCamelCase      :	Dict				       =     move_to_device(lowerCamelCase__				,self.accelerator_state.device				)
													self.optimizer.load_state_dict(lowerCamelCase__				)





	@property
	def 			UpperCamelCase_						(				self      :       Union[str, Any]				):







							'''simple docstring'''
							return self.optimizer.state





	@state.setter
	def 			UpperCamelCase_						(				self      :       Union[str, Any]				,lowerCamelCase__      :       Optional[Any]				):







							'''simple docstring'''
							_UpperCamelCase      :	List[Any]				       =     state





	@property
	def 			UpperCamelCase_						(				self      :       int				):







							'''simple docstring'''
							return self.optimizer.param_groups





	@param_groups.setter
	def 			UpperCamelCase_						(				self      :       Union[str, Any]				,lowerCamelCase__      :       Dict				):







							'''simple docstring'''
							_UpperCamelCase      :	str				       =     param_groups





	@property
	def 			UpperCamelCase_						(				self      :       Tuple				):







							'''simple docstring'''
							return self.optimizer.defaults





	@defaults.setter
	def 			UpperCamelCase_						(				self      :       Optional[int]				,lowerCamelCase__      :       Union[str, Any]				):







							'''simple docstring'''
							_UpperCamelCase      :	List[str]				       =     defaults





	def 			UpperCamelCase_						(				self      :       Tuple				,lowerCamelCase__      :       List[str]				):







							'''simple docstring'''
							self.optimizer.add_param_group(lowerCamelCase__				)





	def 			UpperCamelCase_						(				self      :       int				,lowerCamelCase__      :       str				):







							'''simple docstring'''







							if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
													xm.send_cpu_data_to_device(lowerCamelCase__				,self.accelerator_state.device				)
							self.optimizer.load_state_dict(lowerCamelCase__				)





	def 			UpperCamelCase_						(				self      :       Optional[Any]				):







							'''simple docstring'''
							return self.optimizer.state_dict()





	def 			UpperCamelCase_						(				self      :       Dict				,lowerCamelCase__      :       Dict=None				):







							'''simple docstring'''







							if self.gradient_state.sync_gradients:
													_UpperCamelCase      :	Optional[int]				       =     'set_to_none' in inspect.signature(self.optimizer.zero_grad				).parameters
													if accept_arg:
																			if set_to_none is None:
																									_UpperCamelCase      :	str				       =     False
																			self.optimizer.zero_grad(set_to_none=lowerCamelCase__				)
													else:
																			if set_to_none is not None:
																									raise ValueError('`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer.'				)
																			self.optimizer.zero_grad()





	def 			UpperCamelCase_						(				self      :       Union[str, Any]				,lowerCamelCase__      :       Optional[int]=None				):







							'''simple docstring'''







							if self.gradient_state.sync_gradients:
													if self.accelerator_state.distributed_type == DistributedType.TPU:
																			_UpperCamelCase      :	Union[str, Any]				       =     {'closure': closure} if closure is not None else {}
																			xm.optimizer_step(self.optimizer				,optimizer_args=lowerCamelCase__				)
													elif self.scaler is not None:
																			_UpperCamelCase      :	str				       =     False
																			if self._last_scale is None:
																									# `get_scale` is an async operation requiring full synchronization
																									# on CPU and GPUs before finishing. As a result, we store away
																									# the prior one to reduce the call overhead
																									_UpperCamelCase      :	Any				       =     self.scaler.get_scale()
																									_UpperCamelCase      :	Union[str, Any]				       =     True
																			self.scaler.step(self.optimizer				,lowerCamelCase__				)
																			self.scaler.update()
																			_UpperCamelCase      :	Optional[Any]				       =     self.scaler.get_scale()
																			if not new_scale:
																									# If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
																									_UpperCamelCase      :	Optional[Any]				       =     scale_after < self._last_scale
																			_UpperCamelCase      :	Union[str, Any]				       =     scale_after

													else:
																			self.optimizer.step(lowerCamelCase__				)





	def 			UpperCamelCase_						(				self      :       Union[str, Any]				,lowerCamelCase__      :       Dict				):







							'''simple docstring'''







							for param_group in self.optimizer.param_groups:
													_UpperCamelCase      :	Optional[int]				       =     [parameters_map.get(lowerCamelCase__				,lowerCamelCase__				) for p in param_group['params']]





	@property
	def 			UpperCamelCase_						(				self      :       Dict				):







							'''simple docstring'''
							warnings.warn(
							    'The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '
							    '`optimizer.step_was_skipped` instead.'				,lowerCamelCase__				,)
							return self._is_overflow





	@property
	def 			UpperCamelCase_						(				self      :       Optional[int]				):







							'''simple docstring'''
							return self._is_overflow





	def __getstate__(				self      :       Optional[Any]				):







							'''simple docstring'''
							return self.__dict__.copy()





	def __setstate__(				self      :       Optional[int]				,lowerCamelCase__      :       int				):







							'''simple docstring'''
							self.__dict__.update(lowerCamelCase__				)







'''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_				:			str						 =						{
    '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_				:			int						 =						[
    '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      :	Tuple				       =     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'							)

						_UpperCamelCase      :	Dict				       =     parser.parse_args()

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

						return args






def A__					(  ):
						transformers.utils.logging.set_verbosity_error()
						_UpperCamelCase      :	Optional[Any]				       =     parse_args()

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

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

						_UpperCamelCase      :	Optional[int]				       =     compute_module_sizes(UpperCAmelCase_							)
						_UpperCamelCase      :	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]
						_UpperCamelCase      :	Tuple				       =     '\n'.join([f'- {device}: {size // 2**2_0}MiB' for device, size in device_size.items()]							)
						print(f'\nTheoretical use:\n{message}'							)

						_UpperCamelCase      :	List[str]				       =     AutoTokenizer.from_pretrained(args.tokenizer_name							)

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

												_UpperCamelCase      :	Optional[int]				       =     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'							)

						_UpperCamelCase      :	Union[str, Any]				       =     end_measure(UpperCAmelCase_							)
						log_measures(UpperCAmelCase_						,						'Model generation'							)

						_UpperCamelCase      :	str				       =     [gen / tok for gen, tok in zip(UpperCAmelCase_						,						UpperCAmelCase_							)]
						_UpperCamelCase      :	List[Any]				       =     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'							)
						_UpperCamelCase      :	List[Any]				       =     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()


