




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   :						Tuple				=			16
__lowerCamelCase   :						Dict				=			32







def 		lowercase__   (		__A:	List[Any]				):





     '''simple docstring'''






     return int(x / 2**2_0				)


class    lowerCamelCase    :






   '''simple docstring'''





   def __enter__( self			:   Optional[int]  )						->   List[Any]:
        gc.collect()
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
        __magic_name__							:	int    							=							torch.cuda.memory_allocated()
        return self


   def __exit__( self			:   str       ,						*lowerCamelCase_			:   str  )						->   Any:
        gc.collect()
        torch.cuda.empty_cache()
        __magic_name__							:	Optional[int]    							=							torch.cuda.memory_allocated()
        __magic_name__							:	Optional[Any]    							=							torch.cuda.max_memory_allocated()
        __magic_name__							:	Union[str, Any]    							=							bamb(self.end - self.begin  )
        __magic_name__							:	Tuple    							=							bamb(self.peak - self.begin  )
        # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")







def 		lowercase__   (		__A:	Accelerator							,__A:	int = 1_6							,__A:	str = "bert-base-cased"							,__A:	int = 3_2_0							,__A:	int = 1_6_0							,):





     '''simple docstring'''






     __magic_name__							:	List[str]    							=							AutoTokenizer.from_pretrained(__A				)
     __magic_name__							:	str    							=							load_dataset(
         '''glue'''							,'''mrpc'''							,split={'''train''': F'''train[:{n_train}]''', '''validation''': F'''validation[:{n_val}]'''}				)

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

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

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

     def collate_fn(__A:	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(__A							,padding='''max_length'''							,max_length=1_2_8							,return_tensors='''pt'''				)
          return tokenizer.pad(__A							,padding='''longest'''							,return_tensors='''pt'''				)

     # Instantiate dataloaders.
     __magic_name__							:	str    							=							DataLoader(
         tokenized_datasets['''train''']							,shuffle=__A							,collate_fn=__A							,batch_size=__A				)
     __magic_name__							:	Tuple    							=							DataLoader(
         tokenized_datasets['''validation''']							,shuffle=__A							,collate_fn=__A							,batch_size=__A				)

     return train_dataloader, eval_dataloader







def 		lowercase__   (		__A:	Dict							,__A:	str				):





     '''simple docstring'''






     __magic_name__							:	List[str]    							=							Accelerator()

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

     set_seed(__A				)
     __magic_name__							:	Tuple    							=							get_dataloaders(__A							,__A							,__A							,args.n_train							,args.n_val				)

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

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

     if accelerator.state.deepspeed_plugin is not None:
          __magic_name__							:	Dict    							=							accelerator.state.deepspeed_plugin.deepspeed_config[
              '''gradient_accumulation_steps'''
          ]
     else:
          __magic_name__							:	List[Any]    							=							1
     __magic_name__							:	Union[str, Any]    							=							(len(__A				) * num_epochs) // gradient_accumulation_steps

     # Instantiate scheduler
     if (
         accelerator.state.deepspeed_plugin is None
         or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
     ):
          __magic_name__							:	str    							=							get_linear_schedule_with_warmup(
              optimizer=__A							,num_warmup_steps=0							,num_training_steps=__A							,)
     else:
          __magic_name__							:	int    							=							DummyScheduler(__A							,total_num_steps=__A							,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.
     __magic_name__							:	Dict    							=							accelerator.prepare(
         __A							,__A							,__A							,__A							,__A				)

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

     # Now we train the model
     __magic_name__							:	Optional[int]    							=							{}
     for epoch in range(__A							,__A				):
          with TorchTracemalloc() as tracemalloc:
               model.train()
               for step, batch in enumerate(__A				):
                    __magic_name__							:	Dict    							=							model(**__A				)
                    __magic_name__							:	int    							=							outputs.loss
                    __magic_name__							:	Any    							=							loss / gradient_accumulation_steps
                    accelerator.backward(__A				)
                    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				)				)				)
          __magic_name__							:	int    							=							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(__A							,__A				)







def 		lowercase__   (		):





     '''simple docstring'''






     __magic_name__							:	List[str]    							=							argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''				)
     parser.add_argument(
         '''--model_name_or_path'''							,type=__A							,default='''bert-base-cased'''							,help='''Path to pretrained model or model identifier from huggingface.co/models.'''							,required=__A							,)
     parser.add_argument(
         '''--output_dir'''							,type=__A							,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=__A							,default=__A							,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=__A							,default=3_2_0							,help='''Number of training examples to use.'''							,)
     parser.add_argument(
         '''--n_val'''							,type=__A							,default=1_6_0							,help='''Number of validation examples to use.'''							,)
     parser.add_argument(
         '''--num_epochs'''							,type=__A							,default=1							,help='''Number of train epochs.'''							,)
     __magic_name__							:	int    							=							parser.parse_args()
     __magic_name__							:	Union[str, Any]    							=							{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}
     training_function(__A							,__A				)


if __name__ == "__main__":
 main()









import sys
import turtle







def 		lowercase__   (		__A:	tuple[float, float]							,__A:	tuple[float, float]				):





					'''simple docstring'''






					return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2







def 		lowercase__   (		__A:	tuple[float, float]							,__A:	tuple[float, float]							,__A:	tuple[float, float]							,__A:	int							,):





					'''simple docstring'''






					my_pen.up()
					my_pen.goto(vertexa[0]							,vertexa[1]				)
					my_pen.down()
					my_pen.goto(vertexa[0]							,vertexa[1]				)
					my_pen.goto(vertexa[0]							,vertexa[1]				)
					my_pen.goto(vertexa[0]							,vertexa[1]				)

					if depth == 0:
										return

					triangle(__A							,get_mid(__A							,__A				)							,get_mid(__A							,__A				)							,depth - 1				)
					triangle(__A							,get_mid(__A							,__A				)							,get_mid(__A							,__A				)							,depth - 1				)
					triangle(__A							,get_mid(__A							,__A				)							,get_mid(__A							,__A				)							,depth - 1				)


if __name__ == "__main__":
	if len(sys.argv) != 2:
		raise ValueError(
		    '''Correct format for using this script: '''
		    '''python fractals.py <int:depth_for_fractal>'''
		)
	__lowerCamelCase   :						Any				=			turtle.Turtle()
	my_pen.ht()
	my_pen.speed(5)
	my_pen.pencolor('''red''')

	__lowerCamelCase   :						Optional[Any]				=			[(-1_75, -1_25), (0, 1_75), (1_75, -1_25)]  # vertices of triangle
	triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))



