





import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput





class 			A__   (							__UpperCAmelCase			,  __UpperCAmelCase     ):




   """simple docstring"""



   __A       :    int	  =  1

   @register_to_config
   def __init__( self ,  lowercase = 1000 ,  lowercase = None)      ->						Any:







          '''simple docstring'''

          self.set_timesteps(lowercase)

          # standard deviation of the initial noise distribution
          a__						:     int			=    1.0

          # For now we only support F-PNDM, i.e. the runge-kutta method
          # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
          # mainly at formula (9), (12), (13) and the Algorithm 2.
          a__						:     Tuple			=    4

          # running values
          a__						:     Dict			=    []

   def 							__lowercase		( self ,  lowercase ,  lowercase = None)      ->						Tuple:







          '''simple docstring'''

          a__						:     Tuple			=    num_inference_steps
          a__						:     Union[str, Any]			=    torch.linspace(1 ,  0 ,  num_inference_steps + 1)[:-1]
          a__						:     Optional[Any]			=    torch.cat([steps, torch.tensor([0.0])])

          if self.config.trained_betas is not None:
                 a__						:     Optional[Any]			=    torch.tensor(self.config.trained_betas ,  dtype=torch.floataa)
          else:
                 a__						:     Dict			=    torch.sin(steps * math.pi / 2) ** 2

          a__						:     Dict			=    (1.0 - self.betas**2) ** 0.5

          a__						:     Any			=    (torch.atana(self.betas ,  self.alphas) / math.pi * 2)[:-1]
          a__						:     Optional[Any]			=    timesteps.to(lowercase)

          a__						:     Dict			=    []

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase = True ,  )      ->						Union[SchedulerOutput, Tuple]:







          '''simple docstring'''

          if self.num_inference_steps is None:
                 raise ValueError(
                     'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler')

          a__						:     List[Any]			=    (self.timesteps == timestep).nonzero().item()
          a__						:     Optional[int]			=    timestep_index + 1

          a__						:     int			=    sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
          self.ets.append(lowercase)

          if len(self.ets) == 1:
                 a__						:     List[str]			=    self.ets[-1]
          elif len(self.ets) == 2:
                 a__						:     Tuple			=    (3 * self.ets[-1] - self.ets[-2]) / 2
          elif len(self.ets) == 3:
                 a__						:     Dict			=    (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
          else:
                 a__						:     Union[str, Any]			=    (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

          a__						:     int			=    self._get_prev_sample(lowercase ,  lowercase ,  lowercase ,  lowercase)

          if not return_dict:
                 return (prev_sample,)

          return SchedulerOutput(prev_sample=lowercase)

   def 							__lowercase		( self ,  lowercase ,  *lowercase ,  **lowercase)      ->						torch.FloatTensor:







          '''simple docstring'''

          return sample

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     Optional[int]			=    self.alphas[timestep_index]
          a__						:     Union[str, Any]			=    self.betas[timestep_index]

          a__						:     str			=    self.alphas[prev_timestep_index]
          a__						:     Tuple			=    self.betas[prev_timestep_index]

          a__						:     Dict			=    (sample - sigma * ets) / max(lowercase ,  1e-8)
          a__						:     Dict			=    next_alpha * pred + ets * next_sigma

          return prev_sample

   def __len__( self)      ->						int:







          '''simple docstring'''

          return self.config.num_train_timesteps









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


lowercase				:	Optional[int]      						=  1_6
lowercase				:	Optional[int]      						=  3_2



def A_     (		A__    )  ->		Tuple:
       return int(x / 2**20    )





class 			A__   :




   """simple docstring"""



   def __enter__( self)      ->						Dict:







          '''simple docstring'''

          gc.collect()
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
          a__						:     Union[str, Any]			=    torch.cuda.memory_allocated()
          return self

   def __exit__( self ,  *lowercase)      ->						List[Any]:







          '''simple docstring'''

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



def A_     (		A__		,				A__ = 16		,				A__ = "bert-base-cased"		,				A__ = 320		,				A__ = 160		,				)  ->		Dict:
       a__						:     int			=    AutoTokenizer.from_pretrained(A__    )
       a__						:     str			=    load_dataset(
           'glue'		,				'mrpc'		,				split={'train': F'train[:{n_train}]', 'validation': F'validation[:{n_val}]'}    )

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

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

       # Instantiate dataloaders.
       a__						:     Dict			=    DataLoader(
           tokenized_datasets['train']		,				shuffle=A__		,				collate_fn=A__		,				batch_size=A__    )
       a__						:     Optional[int]			=    DataLoader(
           tokenized_datasets['validation']		,				shuffle=A__		,				collate_fn=A__		,				batch_size=A__    )

       return train_dataloader, eval_dataloader



def A_     (		A__		,				A__    )  ->		Any:
       # Initialize accelerator
       a__						:     Optional[int]			=    Accelerator()

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

       set_seed(A__    )
       a__			,							a__						:     Any			=    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)
       a__						:     Optional[int]			=    AutoModelForSequenceClassification.from_pretrained(A__		,				return_dict=A__    )

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

       if accelerator.state.deepspeed_plugin is not None:
              a__						:     str			=    accelerator.state.deepspeed_plugin.deepspeed_config[
                  'gradient_accumulation_steps'
              ]
       else:
              a__						:     str			=    1
       a__						:     Optional[int]			=    (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
       ):
              a__						:     List[Any]			=    get_linear_schedule_with_warmup(
                  optimizer=A__		,				num_warmup_steps=0		,				num_training_steps=A__		,				)
       else:
              a__						:     Any			=    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.
       a__			,							a__			,							a__			,							a__			,							a__						:     Optional[int]			=    accelerator.prepare(
           A__		,				A__		,				A__		,				A__		,				A__    )

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

       # Now we train the model
       a__						:     List[str]			=    {}
       for epoch in range(A__		,				A__    ):
              with TorchTracemalloc() as tracemalloc:
                     model.train()
                     for step, batch in enumerate(A__    ):
                            a__						:     Optional[Any]			=    model(**A__    )
                            a__						:     List[Any]			=    outputs.loss
                            a__						:     Union[str, 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    )    )    )
              a__						:     List[str]			=    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 A_     (		)  ->		int:
       a__						:     Union[str, Any]			=    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=320		,				help='Number of training examples to use.'		,				)
       parser.add_argument(
           '--n_val'		,				type=A__		,				default=160		,				help='Number of validation examples to use.'		,				)
       parser.add_argument(
           '--num_epochs'		,				type=A__		,				default=1		,				help='Number of train epochs.'		,				)
       a__						:     Dict			=    parser.parse_args()
       a__						:     List[str]			=    {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
       training_function(A__		,				A__    )


if __name__ == "__main__":
    main()


