




import argparse
import gc
import json
import os

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        =    16
__A        =    32







def 			lowerCamelCase_		(       UpperCamelCase__				:      Any      )     ->       List[str]:





				"""simple docstring"""


				return int(x / 2**20      )







class     __lowerCAmelCase :

			"""simple docstring"""







			def __enter__(      self							)    ->      int:



							'''simple docstring'''
							gc.collect()
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
							__lowerCamelCase		       =    torch.cuda.memory_allocated()
							return self






			def __exit__(      self			,						*lowerCamelCase__							)    ->      Dict:



							'''simple docstring'''
							gc.collect()
							torch.cuda.empty_cache()
							__lowerCamelCase		       =    torch.cuda.memory_allocated()
							__lowerCamelCase		       =    torch.cuda.max_memory_allocated()
							__lowerCamelCase		       =    bamb(self.end - self.begin							)
							__lowerCamelCase		       =    bamb(self.peak - self.begin							)
							# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")







def 			lowerCamelCase_		(       UpperCamelCase__				:      Accelerator ,     UpperCamelCase__				:      int = 16 ,     UpperCamelCase__				:      str = "bert-base-cased" ,     UpperCamelCase__				:      int = 320 ,     UpperCamelCase__				:      int = 160 ,     )     ->       List[Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    AutoTokenizer.from_pretrained(UpperCamelCase__      )
				__lowerCamelCase		       =    load_dataset(
				    'glue' ,     'mrpc' ,     split={'train': F"""train[:{n_train}]""", 'validation': F"""validation[:{n_val}]"""}      )

				def tokenize_function(UpperCamelCase__				:      Optional[int]      ):
								# max_length=None => use the model max length (it's actually the default)
								__lowerCamelCase		       =    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
				__lowerCamelCase		       =    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
				__lowerCamelCase		       =    tokenized_datasets.rename_column('label' ,     'labels'      )

				def collate_fn(UpperCamelCase__				:      Dict      ):
								# 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=128 ,     return_tensors='pt'      )
								return tokenizer.pad(UpperCamelCase__ ,     padding='longest' ,     return_tensors='pt'      )

				# Instantiate dataloaders.
				__lowerCamelCase		       =    DataLoader(
				    tokenized_datasets['train'] ,     shuffle=UpperCamelCase__ ,     collate_fn=UpperCamelCase__ ,     batch_size=UpperCamelCase__      )
				__lowerCamelCase		       =    DataLoader(
				    tokenized_datasets['validation'] ,     shuffle=UpperCamelCase__ ,     collate_fn=UpperCamelCase__ ,     batch_size=UpperCamelCase__      )

				return train_dataloader, eval_dataloader







def 			lowerCamelCase_		(       UpperCamelCase__				:      int ,     UpperCamelCase__				:      Optional[Any]      )     ->       Union[str, Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    Accelerator()

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

				set_seed(UpperCamelCase__      )
				__lowerCamelCase       ,						__lowerCamelCase		       =    get_dataloaders(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     args.n_train ,     args.n_val      )

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

				# Instantiate optimizer
				__lowerCamelCase		       =    (
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				__lowerCamelCase		       =    optimizer_cls(params=model.parameters() ,     lr=UpperCamelCase__      )

				if accelerator.state.deepspeed_plugin is not None:
								__lowerCamelCase		       =    accelerator.state.deepspeed_plugin.deepspeed_config[
								    'gradient_accumulation_steps'
								]
				else:
								__lowerCamelCase		       =    1
				__lowerCamelCase		       =    (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
				):
								__lowerCamelCase		       =    get_linear_schedule_with_warmup(
								    optimizer=UpperCamelCase__ ,     num_warmup_steps=0 ,     num_training_steps=UpperCamelCase__ ,     )
				else:
								__lowerCamelCase		       =    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.
				__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    accelerator.prepare(
				    UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )

				# We need to keep track of how many total steps we have iterated over
				__lowerCamelCase		       =    0
				# We also need to keep track of the stating epoch so files are named properly
				__lowerCamelCase		       =    0

				# Now we train the model
				__lowerCamelCase		       =    {}
				for epoch in range(UpperCamelCase__ ,     UpperCamelCase__      ):
								with TorchTracemalloc() as tracemalloc:
												model.train()
												for step, batch in enumerate(UpperCamelCase__      ):
																__lowerCamelCase		       =    model(**UpperCamelCase__      )
																__lowerCamelCase		       =    outputs.loss
																__lowerCamelCase		       =    loss / gradient_accumulation_steps
																accelerator.backward(UpperCamelCase__      )
																if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

																overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
								accelerator.print('Memory before entering the train : {}'.format(bamb(tracemalloc.begin      )      )      )
								accelerator.print('Memory consumed at the end of the train (end-begin): {}'.format(tracemalloc.used      )      )
								accelerator.print('Peak Memory consumed during the train (max-begin): {}'.format(tracemalloc.peaked      )      )
								accelerator.print(
								    'Total Peak Memory consumed during the train (max): {}'.format(
								        tracemalloc.peaked + bamb(tracemalloc.begin      )      )      )
								__lowerCamelCase		       =    tracemalloc.peaked + bamb(tracemalloc.begin      )
								if args.peak_memory_upper_bound is not None:
												assert (
												    train_total_peak_memory[F"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
												), "Peak memory usage exceeded the upper bound"

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







def 			lowerCamelCase_		(       )     ->       List[str]:





				"""simple docstring"""


				__lowerCamelCase		       =    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(
				    '--peak_memory_upper_bound' ,     type=UpperCamelCase__ ,     default=UpperCamelCase__ ,     help='The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.' ,     )
				parser.add_argument(
				    '--n_train' ,     type=UpperCamelCase__ ,     default=320 ,     help='Number of training examples to use.' ,     )
				parser.add_argument(
				    '--n_val' ,     type=UpperCamelCase__ ,     default=160 ,     help='Number of validation examples to use.' ,     )
				parser.add_argument(
				    '--num_epochs' ,     type=UpperCamelCase__ ,     default=1 ,     help='Number of train epochs.' ,     )
				__lowerCamelCase		       =    parser.parse_args()
				__lowerCamelCase		       =    {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
				training_function(UpperCamelCase__ ,     UpperCamelCase__      )


if __name__ == "__main__":
					main()








import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


__A        =    logging.get_logger(__name__)

__A        =    {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__A        =    {
    "tokenizer_file": {
        "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json",
    },
}

__A        =    {
    "gpt-neox-20b": 20_48,
}







class     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			snake_case_   		=       VOCAB_FILES_NAMES
			snake_case_   		=       PRETRAINED_VOCAB_FILES_MAP
			snake_case_   		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case_   		=       ['''input_ids''', '''attention_mask''']


			def __init__(      self			,						lowerCamelCase__=None			,						lowerCamelCase__=None			,						lowerCamelCase__=None			,						lowerCamelCase__="<|endoftext|>"			,						lowerCamelCase__="<|endoftext|>"			,						lowerCamelCase__="<|endoftext|>"			,						lowerCamelCase__=False			,						**lowerCamelCase__			,						)    ->      int:



							'''simple docstring'''
							super().__init__(
							    lowerCamelCase__			,						lowerCamelCase__			,						tokenizer_file=lowerCamelCase__			,						unk_token=lowerCamelCase__			,						bos_token=lowerCamelCase__			,						eos_token=lowerCamelCase__			,						add_prefix_space=lowerCamelCase__			,						**lowerCamelCase__			,						)

							__lowerCamelCase		       =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
							if pre_tok_state.get('add_prefix_space'			,						lowerCamelCase__							) != add_prefix_space:
											__lowerCamelCase		       =    getattr(lowerCamelCase__			,						pre_tok_state.pop('type'							)							)
											__lowerCamelCase		       =    add_prefix_space
											__lowerCamelCase		       =    pre_tok_class(**lowerCamelCase__							)

							__lowerCamelCase		       =    add_prefix_space


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__ = None							)    ->      Tuple[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    self._tokenizer.model.save(lowerCamelCase__			,						name=lowerCamelCase__							)
							return tuple(lowerCamelCase__							)






			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      List[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    []
							for is_user, text in conversation.iter_texts():
											input_ids.extend(self.encode(lowerCamelCase__			,						add_special_tokens=lowerCamelCase__							) + [self.eos_token_id]							)

							if len(lowerCamelCase__							) > self.model_max_length:
											__lowerCamelCase		       =    input_ids[-self.model_max_length :]
							return input_ids


