






'''simple docstring'''
import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


a_					= 1_6
a_					= 3_2





def  _a(  UpperCamelCase__				:						Accelerator,	UpperCamelCase__				:						int = 1_6,	UpperCamelCase__				:						str = "bert-base-cased"							):




						'''simple docstring'''






						SCREAMING_SNAKE_CASE__     :				Any									=AutoTokenizer.from_pretrained(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=load_dataset('''glue''',	'''mrpc'''							)

						def tokenize_function(UpperCamelCase__				:						Union[str, Any]							):
												# max_length=None => use the model max length (it's actually the default)
												SCREAMING_SNAKE_CASE__     :				int									=tokenizer(examples['''sentence1'''],	examples['''sentence2'''],	truncation=UpperCamelCase__,	max_length=UpperCamelCase__							)
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						SCREAMING_SNAKE_CASE__     :				str									=datasets.map(
						    UpperCamelCase__,	batched=UpperCamelCase__,	remove_columns=['''idx''', '''sentence1''', '''sentence2'''],	load_from_cache_file=UpperCamelCase__							)

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						SCREAMING_SNAKE_CASE__     :				Tuple									=tokenized_datasets.rename_column('''label''',	'''labels'''							)

						def collate_fn(UpperCamelCase__				:						List[Any]							):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												if accelerator.distributed_type == DistributedType.TPU:
																		return tokenizer.pad(UpperCamelCase__,	padding='''max_length''',	max_length=1_2_8,	return_tensors='''pt'''							)
												return tokenizer.pad(UpperCamelCase__,	padding='''longest''',	return_tensors='''pt'''							)

						# Instantiate dataloaders.
						SCREAMING_SNAKE_CASE__     :				Any									=DataLoader(
						    tokenized_datasets['''train'''],	shuffle=UpperCamelCase__,	collate_fn=UpperCamelCase__,	batch_size=UpperCamelCase__							)
						SCREAMING_SNAKE_CASE__     :				Optional[Any]									=DataLoader(
						    tokenized_datasets['''validation'''],	shuffle=UpperCamelCase__,	collate_fn=UpperCamelCase__,	batch_size=UpperCamelCase__							)

						return train_dataloader, eval_dataloader





def  _a(  UpperCamelCase__				:						Tuple,	UpperCamelCase__				:						Optional[Any]							):




						'''simple docstring'''






						SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=Accelerator()

						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						SCREAMING_SNAKE_CASE__     :				Any									=config['''lr''']
						SCREAMING_SNAKE_CASE__     :				int									=int(config['''num_epochs''']							)
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=int(config['''seed''']							)
						SCREAMING_SNAKE_CASE__     :				str									=int(config['''batch_size''']							)
						SCREAMING_SNAKE_CASE__     :				List[Any]									=args.model_name_or_path

						set_seed(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE__     :				Tuple									=get_dataloaders(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__							)

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						SCREAMING_SNAKE_CASE__     :				Any									=AutoModelForSequenceClassification.from_pretrained(UpperCamelCase__,	return_dict=UpperCamelCase__							)

						# Instantiate optimizer
						SCREAMING_SNAKE_CASE__     :				List[Any]									=(
						    AdamW
						    if accelerator.state.deepspeed_plugin is None
						    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
						    else DummyOptim
						)
						SCREAMING_SNAKE_CASE__     :				int									=optimizer_cls(params=model.parameters(),	lr=UpperCamelCase__							)

						if accelerator.state.deepspeed_plugin is not None:
												SCREAMING_SNAKE_CASE__     :				Tuple									=accelerator.state.deepspeed_plugin.deepspeed_config[
												    '''gradient_accumulation_steps'''
												]
						else:
												SCREAMING_SNAKE_CASE__     :				Optional[int]									=1
						SCREAMING_SNAKE_CASE__     :				Any									=(len(UpperCamelCase__							) * num_epochs) // gradient_accumulation_steps

						# Instantiate scheduler
						if (
						    accelerator.state.deepspeed_plugin is None
						    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
						):
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=get_linear_schedule_with_warmup(
												    optimizer=UpperCamelCase__,	num_warmup_steps=0,	num_training_steps=UpperCamelCase__,	)
						else:
												SCREAMING_SNAKE_CASE__     :				List[str]									=DummyScheduler(UpperCamelCase__,	total_num_steps=UpperCamelCase__,	warmup_num_steps=0							)

						# Prepare everything
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						SCREAMING_SNAKE_CASE__     :				List[Any]									=accelerator.prepare(
						    UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__							)

						# We need to keep track of how many total steps we have iterated over
						SCREAMING_SNAKE_CASE__     :				int									=0
						# We also need to keep track of the stating epoch so files are named properly
						SCREAMING_SNAKE_CASE__     :				Optional[Any]									=0

						# Now we train the model
						SCREAMING_SNAKE_CASE__     :				List[Any]									=evaluate.load('''glue''',	'''mrpc'''							)
						SCREAMING_SNAKE_CASE__     :				Optional[int]									=0
						SCREAMING_SNAKE_CASE__     :				Dict									={}
						for epoch in range(UpperCamelCase__,	UpperCamelCase__							):
												model.train()
												for step, batch in enumerate(UpperCamelCase__							):
																		SCREAMING_SNAKE_CASE__     :				List[str]									=model(**UpperCamelCase__							)
																		SCREAMING_SNAKE_CASE__     :				Optional[Any]									=outputs.loss
																		SCREAMING_SNAKE_CASE__     :				Any									=loss / gradient_accumulation_steps
																		accelerator.backward(UpperCamelCase__							)
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1

												model.eval()
												SCREAMING_SNAKE_CASE__     :				Any									=0
												for step, batch in enumerate(UpperCamelCase__							):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device							)
																		with torch.no_grad():
																								SCREAMING_SNAKE_CASE__     :				Tuple									=model(**UpperCamelCase__							)
																		SCREAMING_SNAKE_CASE__     :				Optional[int]									=outputs.logits.argmax(dim=-1							)
																		# It is slightly faster to call this once, than multiple times
																		SCREAMING_SNAKE_CASE__     :				str									=accelerator.gather(
																		    (predictions, batch['''labels'''])							)  # If we are in a multiprocess environment, the last batch has duplicates
																		if accelerator.use_distributed:
																								if step == len(UpperCamelCase__							) - 1:
																														SCREAMING_SNAKE_CASE__     :				Any									=predictions[: len(eval_dataloader.dataset							) - samples_seen]
																														SCREAMING_SNAKE_CASE__     :				Optional[Any]									=references[: len(eval_dataloader.dataset							) - samples_seen]
																								else:
																														samples_seen += references.shape[0]
																		metric.add_batch(
																		    predictions=UpperCamelCase__,	references=UpperCamelCase__,	)

												SCREAMING_SNAKE_CASE__     :				List[str]									=metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"epoch {epoch}:",	UpperCamelCase__							)
												SCREAMING_SNAKE_CASE__     :				List[Any]									=eval_metric['''accuracy''']

												if best_performance < eval_metric["accuracy"]:
																		SCREAMING_SNAKE_CASE__     :				Dict									=eval_metric['''accuracy''']

						if args.performance_lower_bound is not None:
												assert (
												    args.performance_lower_bound <= best_performance
												), f"Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"

						accelerator.wait_for_everyone()
						if accelerator.is_main_process:
												with open(os.path.join(args.output_dir,	'''all_results.json'''							),	'''w'''							) as f:
																		json.dump(UpperCamelCase__,	UpperCamelCase__							)





def  _a(  ):




						'''simple docstring'''






						SCREAMING_SNAKE_CASE__     :				int									=argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''							)
						parser.add_argument(
						    '''--model_name_or_path''',	type=UpperCamelCase__,	default='''bert-base-cased''',	help='''Path to pretrained model or model identifier from huggingface.co/models.''',	required=UpperCamelCase__,	)
						parser.add_argument(
						    '''--output_dir''',	type=UpperCamelCase__,	default='''.''',	help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''',	)
						parser.add_argument(
						    '''--performance_lower_bound''',	type=UpperCamelCase__,	default=UpperCamelCase__,	help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.''',	)
						parser.add_argument(
						    '''--num_epochs''',	type=UpperCamelCase__,	default=3,	help='''Number of train epochs.''',	)
						SCREAMING_SNAKE_CASE__     :				Any									=parser.parse_args()
						SCREAMING_SNAKE_CASE__     :				str									={'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}
						training_function(UpperCamelCase__,	UpperCamelCase__							)


if __name__ == "__main__":
		main()







'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_					= logging.get_logger(__name__)

a_					= {
    'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}






class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
 snake_case_  							=							"""gpt_neox"""




 def __init__(      self	:							List[Any]					,      __lowercase	:							Union[str, Any]=5_04_32					,      __lowercase	:							int=61_44					,      __lowercase	:							Tuple=44					,      __lowercase	:							List[str]=64					,      __lowercase	:							str=2_45_76					,      __lowercase	:							Dict="gelu"					,      __lowercase	:							Tuple=0.25					,      __lowercase	:							Tuple=1_00_00					,      __lowercase	:							Tuple=0.0					,      __lowercase	:							str=0.0					,      __lowercase	:							List[Any]=0.1					,      __lowercase	:							Dict=20_48					,      __lowercase	:							Any=0.02					,      __lowercase	:							Dict=1e-5					,      __lowercase	:							List[Any]=True					,      __lowercase	:							str=0					,      __lowercase	:							Optional[Any]=2					,      __lowercase	:							Tuple=False					,      __lowercase	:							List[Any]=True					,      __lowercase	:							Optional[Any]=None					,      **__lowercase	:							Any					,      )	->						Dict:
       super().__init__(bos_token_id=__lowercase					,      eos_token_id=__lowercase					,      **__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=vocab_size
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=max_position_embeddings
       SCREAMING_SNAKE_CASE__     :				Any									=hidden_size
       SCREAMING_SNAKE_CASE__     :				str									=num_hidden_layers
       SCREAMING_SNAKE_CASE__     :				Any									=num_attention_heads
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=intermediate_size
       SCREAMING_SNAKE_CASE__     :				Dict									=hidden_act
       SCREAMING_SNAKE_CASE__     :				str									=rotary_pct
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=rotary_emb_base
       SCREAMING_SNAKE_CASE__     :				List[Any]									=attention_dropout
       SCREAMING_SNAKE_CASE__     :				List[Any]									=hidden_dropout
       SCREAMING_SNAKE_CASE__     :				str									=classifier_dropout
       SCREAMING_SNAKE_CASE__     :				Any									=initializer_range
       SCREAMING_SNAKE_CASE__     :				Dict									=layer_norm_eps
       SCREAMING_SNAKE_CASE__     :				Any									=use_cache
       SCREAMING_SNAKE_CASE__     :				Tuple									=tie_word_embeddings
       SCREAMING_SNAKE_CASE__     :				Tuple									=use_parallel_residual
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=rope_scaling
       self._rope_scaling_validation()

       if self.hidden_size % self.num_attention_heads != 0:
             raise ValueError(
                 '''The hidden size is not divisble by the number of attention heads! Make sure to update them!'''      )




 def      __magic_name__	(      self	:							Optional[Any]      )	->						Optional[Any]:
       if self.rope_scaling is None:
             return

       if not isinstance(self.rope_scaling					,      __lowercase      ) or len(self.rope_scaling      ) != 2:
             raise ValueError(
                 '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
                 F"got {self.rope_scaling}"      )
       SCREAMING_SNAKE_CASE__     :				int									=self.rope_scaling.get('''type'''					,      __lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.rope_scaling.get('''factor'''					,      __lowercase      )
       if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
             raise ValueError(
                 F"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"      )
       if rope_scaling_factor is None or not isinstance(__lowercase					,      __lowercase      ) or rope_scaling_factor <= 1.0:
             raise ValueError(F"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"      )