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


_lowerCamelCase   :				Optional[Any]     			=						16
_lowerCamelCase   :				Any     			=						32





def 		__lowerCamelCase							(UpperCAmelCase__       :							List[str]					):
     return int(x / 2**2_0					)


class   lowercase      :




       def __enter__(  self				:   Tuple )   ->			int:



            '''simple docstring'''

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





       def __exit__(  self				:   Optional[Any]		,							*_UpperCamelCase				:   Optional[Any] )   ->			str:



            '''simple docstring'''

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





def 		__lowerCamelCase							(UpperCAmelCase__       :							Accelerator ,	UpperCAmelCase__       :							int = 1_6 ,	UpperCAmelCase__       :							str = "bert-base-cased" ,	UpperCAmelCase__       :							int = 3_2_0 ,	UpperCAmelCase__       :							int = 1_6_0 ,	):
     SCREAMING_SNAKE_CASE           =    AutoTokenizer.from_pretrained(UpperCAmelCase__					)
     SCREAMING_SNAKE_CASE           =    load_dataset(
         "glue" ,	"mrpc" ,	split={"train": F"train[:{n_train}]", "validation": F"validation[:{n_val}]"}					)

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

     def collate_fn(UpperCAmelCase__       :							Optional[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           =    DataLoader(
         tokenized_datasets["train"] ,	shuffle=UpperCAmelCase__ ,	collate_fn=UpperCAmelCase__ ,	batch_size=UpperCAmelCase__					)
     SCREAMING_SNAKE_CASE           =    DataLoader(
         tokenized_datasets["validation"] ,	shuffle=UpperCAmelCase__ ,	collate_fn=UpperCAmelCase__ ,	batch_size=UpperCAmelCase__					)

     return train_dataloader, eval_dataloader





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							int					):
     # Initialize accelerator
     SCREAMING_SNAKE_CASE           =    Accelerator()

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

     set_seed(UpperCAmelCase__					)
     SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    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)
     SCREAMING_SNAKE_CASE           =    AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase__ ,	return_dict=UpperCAmelCase__					)

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

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

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

     # Now we train the model
     SCREAMING_SNAKE_CASE           =    {}
     for epoch in range(UpperCAmelCase__ ,	UpperCAmelCase__					):
          with TorchTracemalloc() as tracemalloc:
               model.train()
               for step, batch in enumerate(UpperCAmelCase__					):
                    SCREAMING_SNAKE_CASE           =    model(**UpperCAmelCase__					)
                    SCREAMING_SNAKE_CASE           =    outputs.loss
                    SCREAMING_SNAKE_CASE           =    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					)					)					)
          SCREAMING_SNAKE_CASE           =    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							():
     SCREAMING_SNAKE_CASE           =    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=3_2_0 ,	help="Number of training examples to use." ,	)
     parser.add_argument(
         "--n_val" ,	type=UpperCAmelCase__ ,	default=1_6_0 ,	help="Number of validation examples to use." ,	)
     parser.add_argument(
         "--num_epochs" ,	type=UpperCAmelCase__ ,	default=1 ,	help="Number of train epochs." ,	)
     SCREAMING_SNAKE_CASE           =    parser.parse_args()
     SCREAMING_SNAKE_CASE           =    {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 4_2, "batch_size": 1_6}
     training_function(UpperCAmelCase__ ,	UpperCAmelCase__					)


if __name__ == "__main__":
       main()





def 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							int					):
					while b:
										SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    b, a % b
					return a





def 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							int					):
					return a if b == 0 else euclidean_gcd_recursive(UpperCAmelCase__ ,	a % b					)





def 		__lowerCamelCase							():
					print(F"euclidean_gcd(3, 5) = {euclidean_gcd(3 ,	5					)}"					)
					print(F"euclidean_gcd(5, 3) = {euclidean_gcd(5 ,	3					)}"					)
					print(F"euclidean_gcd(1, 3) = {euclidean_gcd(1 ,	3					)}"					)
					print(F"euclidean_gcd(3, 6) = {euclidean_gcd(3 ,	6					)}"					)
					print(F"euclidean_gcd(6, 3) = {euclidean_gcd(6 ,	3					)}"					)

					print(F"euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 ,	5					)}"					)
					print(F"euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 ,	3					)}"					)
					print(F"euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 ,	3					)}"					)
					print(F"euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 ,	6					)}"					)
					print(F"euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 ,	3					)}"					)


if __name__ == "__main__":
							main()




