'''simple docstring'''

import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class    lowerCamelCase__  (   __snake_case     ):
   __UpperCAmelCase           =						42
   __UpperCAmelCase           =						None




def  _lowerCAmelCase				(				__a			,      __a=0.9_99			,      __a="cosine"			,      )  ->      List[Any]:
     '''simple docstring'''







     if alpha_transform_type == "cosine":

          def alpha_bar_fn(__a      ):
               return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2      ) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(__a      ):
               return math.exp(t * -12.0      )

     else:
          raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''      )

     _UpperCamelCase  :int       =[]
     for i in range(__a      ):
          _UpperCamelCase  :Tuple       =i / num_diffusion_timesteps
          _UpperCamelCase  :List[Any]       =(i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(__a      ) / alpha_bar_fn(__a      )			,      __a      )      )
     return torch.tensor(__a			,      dtype=torch.floataa      )




class    lowerCamelCase__  (   __snake_case       ,				__snake_case     ):
   __UpperCAmelCase           =						[e.name for e in KarrasDiffusionSchedulers]
   __UpperCAmelCase           =						1



   @register_to_config
   def __init__(    self					,      lowerCAmelCase__ = 1_000					,      lowerCAmelCase__ = 0.0001					,      lowerCAmelCase__ = 0.02					,      lowerCAmelCase__ = "linear"					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = False					,      lowerCAmelCase__ = "epsilon"					,      lowerCAmelCase__ = "linspace"					,      lowerCAmelCase__ = 0					,      )				->     Dict:
        """simple docstring"""





        if trained_betas is not None:
             _UpperCamelCase  :List[str]       =torch.tensor(lowerCAmelCase__					,      dtype=torch.floataa     )
        elif beta_schedule == "linear":
             _UpperCamelCase  :Dict       =torch.linspace(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      dtype=torch.floataa     )
        elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             _UpperCamelCase  :Any       =(
                 torch.linspace(beta_start**0.5					,      beta_end**0.5					,      lowerCAmelCase__					,      dtype=torch.floataa     ) ** 2
             )
        elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             _UpperCamelCase  :Union[str, Any]       =betas_for_alpha_bar(lowerCAmelCase__     )
        else:
             raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''     )

        _UpperCamelCase  :List[Any]       =1.0 - self.betas
        _UpperCamelCase  :Tuple       =torch.cumprod(self.alphas					,      dim=0     )

        _UpperCamelCase  :Optional[int]       =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
        _UpperCamelCase  :Tuple       =np.concatenate([sigmas[::-1], [0.0]]     ).astype(np.floataa     )
        _UpperCamelCase  :int       =torch.from_numpy(lowerCAmelCase__     )

        # setable values
        _UpperCamelCase  :int       =None
        _UpperCamelCase  :str       =use_karras_sigmas
        self.set_timesteps(lowerCAmelCase__					,      lowerCAmelCase__     )
        _UpperCamelCase  :Union[str, Any]       =[]
        _UpperCamelCase  :str       =False



   @property
   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





        if self.config.timestep_spacing in ["linspace", "trailing"]:
             return self.sigmas.max()

        return (self.sigmas.max() ** 2 + 1) ** 0.5



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     torch.FloatTensor:
        """simple docstring"""





        if isinstance(lowerCAmelCase__					,      torch.Tensor     ):
             _UpperCamelCase  :Union[str, Any]       =timestep.to(self.timesteps.device     )
        _UpperCamelCase  :str       =(self.timesteps == timestep).nonzero().item()
        _UpperCamelCase  :List[str]       =self.sigmas[step_index]
        _UpperCamelCase  :str       =sample / ((sigma**2 + 1) ** 0.5)
        _UpperCamelCase  :Tuple       =True
        return sample



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     List[Any]:
        """simple docstring"""





        def lms_derivative(lowerCAmelCase__     ):
             _UpperCamelCase  :Dict       =1.0
             for k in range(lowerCAmelCase__     ):
                  if current_order == k:
                       continue
                  prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
             return prod

        _UpperCamelCase  :Union[str, Any]       =integrate.quad(lowerCAmelCase__					,      self.sigmas[t]					,      self.sigmas[t + 1]					,      epsrel=1e-4     )[0]

        return integrated_coeff



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__ = None     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :str       =num_inference_steps

        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
        if self.config.timestep_spacing == "linspace":
             _UpperCamelCase  :str       =np.linspace(0					,      self.config.num_train_timesteps - 1					,      lowerCAmelCase__					,      dtype=lowerCAmelCase__     )[
                 ::-1
             ].copy()
        elif self.config.timestep_spacing == "leading":
             _UpperCamelCase  :Tuple       =self.config.num_train_timesteps // self.num_inference_steps
             # creates integer timesteps by multiplying by ratio
             # casting to int to avoid issues when num_inference_step is power of 3
             _UpperCamelCase  :Optional[int]       =(np.arange(0					,      lowerCAmelCase__     ) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase__     )
             timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
             _UpperCamelCase  :Tuple       =self.config.num_train_timesteps / self.num_inference_steps
             # creates integer timesteps by multiplying by ratio
             # casting to int to avoid issues when num_inference_step is power of 3
             _UpperCamelCase  :str       =(np.arange(self.config.num_train_timesteps					,      0					,      -step_ratio     )).round().copy().astype(lowerCAmelCase__     )
             timesteps -= 1
        else:
             raise ValueError(
                 f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''     )

        _UpperCamelCase  :Any       =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
        _UpperCamelCase  :Optional[Any]       =np.log(lowerCAmelCase__     )
        _UpperCamelCase  :Dict       =np.interp(lowerCAmelCase__					,      np.arange(0					,      len(lowerCAmelCase__     )     )					,      lowerCAmelCase__     )

        if self.use_karras_sigmas:
             _UpperCamelCase  :Optional[Any]       =self._convert_to_karras(in_sigmas=lowerCAmelCase__     )
             _UpperCamelCase  :Optional[int]       =np.array([self._sigma_to_t(lowerCAmelCase__					,      lowerCAmelCase__     ) for sigma in sigmas]     )

        _UpperCamelCase  :List[Any]       =np.concatenate([sigmas, [0.0]]     ).astype(np.floataa     )

        _UpperCamelCase  :Optional[Any]       =torch.from_numpy(lowerCAmelCase__     ).to(device=lowerCAmelCase__     )
        if str(lowerCAmelCase__     ).startswith("""mps"""     ):
             # mps does not support float64
             _UpperCamelCase  :str       =torch.from_numpy(lowerCAmelCase__     ).to(lowerCAmelCase__					,      dtype=torch.floataa     )
        else:
             _UpperCamelCase  :str       =torch.from_numpy(lowerCAmelCase__     ).to(device=lowerCAmelCase__     )

        _UpperCamelCase  :Union[str, Any]       =[]



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase  :Optional[int]       =np.log(lowerCAmelCase__     )

        # get distribution
        _UpperCamelCase  :Optional[int]       =log_sigma - log_sigmas[:, np.newaxis]

        # get sigmas range
        _UpperCamelCase  :Union[str, Any]       =np.cumsum((dists >= 0)					,      axis=0     ).argmax(axis=0     ).clip(max=log_sigmas.shape[0] - 2     )
        _UpperCamelCase  :List[str]       =low_idx + 1

        _UpperCamelCase  :str       =log_sigmas[low_idx]
        _UpperCamelCase  :Union[str, Any]       =log_sigmas[high_idx]

        # interpolate sigmas
        _UpperCamelCase  :int       =(low - log_sigma) / (low - high)
        _UpperCamelCase  :Union[str, Any]       =np.clip(lowerCAmelCase__					,      0					,      1     )

        # transform interpolation to time range
        _UpperCamelCase  :str       =(1 - w) * low_idx + w * high_idx
        _UpperCamelCase  :Optional[int]       =t.reshape(sigma.shape     )
        return t



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     torch.FloatTensor:
        """simple docstring"""





        _UpperCamelCase  :float       =in_sigmas[-1].item()
        _UpperCamelCase  :float       =in_sigmas[0].item()

        _UpperCamelCase  :Union[str, Any]       =7.0  # 7.0 is the value used in the paper
        _UpperCamelCase  :str       =np.linspace(0					,      1					,      self.num_inference_steps     )
        _UpperCamelCase  :Union[str, Any]       =sigma_min ** (1 / rho)
        _UpperCamelCase  :List[str]       =sigma_max ** (1 / rho)
        _UpperCamelCase  :List[str]       =(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
        return sigmas



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = 4					,      lowerCAmelCase__ = True					,      )				->     Union[LMSDiscreteSchedulerOutput, Tuple]:
        """simple docstring"""





        if not self.is_scale_input_called:
             warnings.warn(
                 """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
                 """See `StableDiffusionPipeline` for a usage example."""     )

        if isinstance(lowerCAmelCase__					,      torch.Tensor     ):
             _UpperCamelCase  :Tuple       =timestep.to(self.timesteps.device     )
        _UpperCamelCase  :int       =(self.timesteps == timestep).nonzero().item()
        _UpperCamelCase  :int       =self.sigmas[step_index]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
             _UpperCamelCase  :List[Any]       =sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
             # * c_out + input * c_skip
             _UpperCamelCase  :Union[str, Any]       =model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
        elif self.config.prediction_type == "sample":
             _UpperCamelCase  :List[str]       =model_output
        else:
             raise ValueError(
                 f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''     )

        # 2. Convert to an ODE derivative
        _UpperCamelCase  :List[str]       =(sample - pred_original_sample) / sigma
        self.derivatives.append(lowerCAmelCase__     )
        if len(self.derivatives     ) > order:
             self.derivatives.pop(0     )

        # 3. Compute linear multistep coefficients
        _UpperCamelCase  :Optional[Any]       =min(step_index + 1					,      lowerCAmelCase__     )
        _UpperCamelCase  :Any       =[self.get_lms_coefficient(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     ) for curr_order in range(lowerCAmelCase__     )]

        # 4. Compute previous sample based on the derivatives path
        _UpperCamelCase  :List[str]       =sample + sum(
            coeff * derivative for coeff, derivative in zip(lowerCAmelCase__					,      reversed(self.derivatives     )     )     )

        if not return_dict:
             return (prev_sample,)

        return LMSDiscreteSchedulerOutput(prev_sample=lowerCAmelCase__					,      pred_original_sample=lowerCAmelCase__     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      )				->     torch.FloatTensor:
        """simple docstring"""





        _UpperCamelCase  :str       =self.sigmas.to(device=original_samples.device					,      dtype=original_samples.dtype     )
        if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase__     ):
             # mps does not support float64
             _UpperCamelCase  :Tuple       =self.timesteps.to(original_samples.device					,      dtype=torch.floataa     )
             _UpperCamelCase  :Union[str, Any]       =timesteps.to(original_samples.device					,      dtype=torch.floataa     )
        else:
             _UpperCamelCase  :Optional[Any]       =self.timesteps.to(original_samples.device     )
             _UpperCamelCase  :Any       =timesteps.to(original_samples.device     )

        _UpperCamelCase  :Optional[int]       =[(schedule_timesteps == t).nonzero().item() for t in timesteps]

        _UpperCamelCase  :str       =sigmas[step_indices].flatten()
        while len(sigma.shape     ) < len(original_samples.shape     ):
             _UpperCamelCase  :List[str]       =sigma.unsqueeze(-1     )

        _UpperCamelCase  :List[Any]       =original_samples + noise * sigma
        return noisy_samples




   def __len__(    self     )				->     Optional[int]:
        """simple docstring"""





        return self.config.num_train_timesteps







import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_UpperCAmelCase					:						Union[str, Any]         =     16
_UpperCAmelCase					:						Dict         =     32






def  UpperCAmelCase__						(    lowerCamelCase	:			List[Any]			,lowerCamelCase	:			List[Any]    ):
   # Initialize accelerator
   if args.with_tracking:
      lowerCamelCase									=Accelerator(
          cpu=args.cpu			,mixed_precision=args.mixed_precision			,log_with="""all"""			,project_dir=args.project_dir    )
   else:
      lowerCamelCase									=Accelerator(cpu=args.cpu			,mixed_precision=args.mixed_precision    )

   if hasattr(args.checkpointing_steps			,"""isdigit"""    ):
      if args.checkpointing_steps == "epoch":
         lowerCamelCase									=args.checkpointing_steps
      elif args.checkpointing_steps.isdigit():
         lowerCamelCase									=int(args.checkpointing_steps    )
      else:
         raise ValueError(
             f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''    )
   else:
      lowerCamelCase									=None
   # 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"""]    )

   # We need to initialize the trackers we use, and also store our configuration
   if args.with_tracking:
      lowerCamelCase									=os.path.split(lowerCamelCase    )[-1].split("""."""    )[0]
      accelerator.init_trackers(lowerCamelCase			,lowerCamelCase    )

   lowerCamelCase									=AutoTokenizer.from_pretrained("""bert-base-cased"""    )
   lowerCamelCase									=load_dataset("""glue"""			,"""mrpc"""    )
   lowerCamelCase									=evaluate.load("""glue"""			,"""mrpc"""    )

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

   # Apply the method we just defined to all the examples in all the splits of the dataset
   # starting with the main process first:
   with accelerator.main_process_first():
      lowerCamelCase									=datasets.map(
          lowerCamelCase			,batched=lowerCamelCase			,remove_columns=["""idx""", """sentence1""", """sentence2"""]			,)

   # 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"""    )

   # If the batch size is too big we use gradient accumulation
   lowerCamelCase									=1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      lowerCamelCase									=batch_size // MAX_GPU_BATCH_SIZE
      lowerCamelCase									=MAX_GPU_BATCH_SIZE

   def collate_fn(lowerCamelCase	:			Any    ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      lowerCamelCase									=1_28 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         lowerCamelCase									=16
      elif accelerator.mixed_precision != "no":
         lowerCamelCase									=8
      else:
         lowerCamelCase									=None

      return tokenizer.pad(
          lowerCamelCase			,padding="""longest"""			,max_length=lowerCamelCase			,pad_to_multiple_of=lowerCamelCase			,return_tensors="""pt"""			,)

   # Instantiate dataloaders.
   lowerCamelCase									=DataLoader(
       tokenized_datasets["""train"""]			,shuffle=lowerCamelCase			,collate_fn=lowerCamelCase			,batch_size=lowerCamelCase    )
   lowerCamelCase									=DataLoader(
       tokenized_datasets["""validation"""]			,shuffle=lowerCamelCase			,collate_fn=lowerCamelCase			,batch_size=lowerCamelCase    )

   set_seed(lowerCamelCase    )

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   lowerCamelCase									=AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""			,return_dict=lowerCamelCase    )

   # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
   # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
   # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
   lowerCamelCase									=model.to(accelerator.device    )

   # Instantiate optimizer
   lowerCamelCase									=AdamW(params=model.parameters()			,lr=lowerCamelCase    )

   # Instantiate scheduler
   lowerCamelCase									=get_linear_schedule_with_warmup(
       optimizer=lowerCamelCase			,num_warmup_steps=1_00			,num_training_steps=(len(lowerCamelCase    ) * num_epochs) // gradient_accumulation_steps			,)

   # 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(
       lowerCamelCase			,lowerCamelCase			,lowerCamelCase			,lowerCamelCase			,lowerCamelCase    )

   # 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

   # Potentially load in the weights and states from a previous save
   if args.resume_from_checkpoint:
      if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
         accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''    )
         accelerator.load_state(args.resume_from_checkpoint    )
         lowerCamelCase									=os.path.basename(args.resume_from_checkpoint    )
      else:
         # Get the most recent checkpoint
         lowerCamelCase									=[f.name for f in os.scandir(os.getcwd()    ) if f.is_dir()]
         dirs.sort(key=os.path.getctime    )
         lowerCamelCase									=dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
      # Extract `epoch_{i}` or `step_{i}`
      lowerCamelCase									=os.path.splitext(lowerCamelCase    )[0]

      if "epoch" in training_difference:
         lowerCamelCase									=int(training_difference.replace("""epoch_"""			,""""""    )    ) + 1
         lowerCamelCase									=None
      else:
         lowerCamelCase									=int(training_difference.replace("""step_"""			,""""""    )    )
         lowerCamelCase									=resume_step // len(lowerCamelCase    )
         resume_step -= starting_epoch * len(lowerCamelCase    )

    # Now we train the model
   for epoch in range(lowerCamelCase			,lowerCamelCase    ):
      model.train()
      if args.with_tracking:
         lowerCamelCase									=0
      if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
         # We need to skip steps until we reach the resumed step
         lowerCamelCase									=accelerator.skip_first_batches(lowerCamelCase			,lowerCamelCase    )
         overall_step += resume_step
      else:
         # After the first iteration though, we need to go back to the original dataloader
         lowerCamelCase									=train_dataloader
      for step, batch in enumerate(lowerCamelCase    ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device    )
         lowerCamelCase									=model(**lowerCamelCase    )
         lowerCamelCase									=outputs.loss
         lowerCamelCase									=loss / gradient_accumulation_steps
         # We keep track of the loss at each epoch
         if args.with_tracking:
            total_loss += loss.detach().float()
         accelerator.backward(lowerCamelCase    )
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1

         if isinstance(lowerCamelCase			,lowerCamelCase    ):
            lowerCamelCase									=f'''step_{overall_step}'''
            if overall_step % checkpointing_steps == 0:
               if args.output_dir is not None:
                  lowerCamelCase									=os.path.join(args.output_dir			,lowerCamelCase    )
               accelerator.save_state(lowerCamelCase    )

      model.eval()
      for step, batch in enumerate(lowerCamelCase    ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device    )
         with torch.no_grad():
            lowerCamelCase									=model(**lowerCamelCase    )
         lowerCamelCase									=outputs.logits.argmax(dim=-1    )
         lowerCamelCase				,       lowerCamelCase									=accelerator.gather_for_metrics((predictions, batch["""labels"""])    )
         metric.add_batch(
             predictions=lowerCamelCase			,references=lowerCamelCase			,)

      lowerCamelCase									=metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f'''epoch {epoch}:'''			,lowerCamelCase    )
      if args.with_tracking:
         accelerator.log(
             {
                 """accuracy""": eval_metric["""accuracy"""],
                 """f1""": eval_metric["""f1"""],
                 """train_loss""": total_loss.item() / len(lowerCamelCase    ),
                 """epoch""": epoch,
             }			,step=lowerCamelCase			,)

      if checkpointing_steps == "epoch":
         lowerCamelCase									=f'''epoch_{epoch}'''
         if args.output_dir is not None:
            lowerCamelCase									=os.path.join(args.output_dir			,lowerCamelCase    )
         accelerator.save_state(lowerCamelCase    )

   if args.with_tracking:
      accelerator.end_training()






def  UpperCAmelCase__						(    ):
   lowerCamelCase									=argparse.ArgumentParser(description="""Simple example of training script."""    )
   parser.add_argument(
       """--mixed_precision"""			,type=lowerCamelCase			,default=lowerCamelCase			,choices=["""no""", """fp16""", """bf16""", """fp8"""]			,help="""Whether to use mixed precision. Choose"""
       """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
       """and an Nvidia Ampere GPU."""			,)
   parser.add_argument("""--cpu"""			,action="""store_true"""			,help="""If passed, will train on the CPU."""    )
   parser.add_argument(
       """--checkpointing_steps"""			,type=lowerCamelCase			,default=lowerCamelCase			,help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""			,)
   parser.add_argument(
       """--resume_from_checkpoint"""			,type=lowerCamelCase			,default=lowerCamelCase			,help="""If the training should continue from a checkpoint folder."""			,)
   parser.add_argument(
       """--with_tracking"""			,action="""store_true"""			,help="""Whether to load in all available experiment trackers from the environment and use them for logging."""			,)
   parser.add_argument(
       """--output_dir"""			,type=lowerCamelCase			,default="""."""			,help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""			,)
   parser.add_argument(
       """--project_dir"""			,type=lowerCamelCase			,default="""logs"""			,help="""Location on where to store experiment tracking logs` and relevent project information"""			,)
   lowerCamelCase									=parser.parse_args()
   lowerCamelCase									={"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
   training_function(lowerCamelCase			,lowerCamelCase    )


if __name__ == "__main__":
   main()