




"""simple docstring"""





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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase=0.999 ,     lowerCamelCase="cosine" ,     ):
						'''simple docstring'''


						if alpha_transform_type == "cosine":

												def alpha_bar_fn(lowerCamelCase						):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

						elif alpha_transform_type == "exp":

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

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

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

class  __lowercase    (     snake_case_  ,				snake_case_		):


				'''simple docstring'''


				_lowerCAmelCase		     =[e.name for e in KarrasDiffusionSchedulers]
				_lowerCAmelCase		     =2

				@register_to_config
				def __init__(				self				:       Optional[Any] ,SCREAMING_SNAKE_CASE_				:       int = 1_0_0_0 ,SCREAMING_SNAKE_CASE_				:       float = 0.00085 ,SCREAMING_SNAKE_CASE_				:       float = 0.012 ,SCREAMING_SNAKE_CASE_				:       str = "linear" ,SCREAMING_SNAKE_CASE_				:       Optional[Union[np.ndarray, List[float]]] = None ,SCREAMING_SNAKE_CASE_				:       str = "epsilon" ,SCREAMING_SNAKE_CASE_				:       str = "linspace" ,SCREAMING_SNAKE_CASE_				:       int = 0 ,):
										if trained_betas is not None:
																snake_case__    :   Union[str, Any]							     =   torch.tensor(SCREAMING_SNAKE_CASE_ ,dtype=torch.floataa )
										elif beta_schedule == "linear":
																snake_case__    :   List[str]							     =   torch.linspace(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,dtype=torch.floataa )
										elif beta_schedule == "scaled_linear":
																# this schedule is very specific to the latent diffusion model.
																snake_case__    :   Optional[Any]							     =   (
																    torch.linspace(beta_start**0.5 ,beta_end**0.5 ,SCREAMING_SNAKE_CASE_ ,dtype=torch.floataa ) ** 2
																)
										elif beta_schedule == "squaredcos_cap_v2":
																# Glide cosine schedule
																snake_case__    :   str							     =   betas_for_alpha_bar(SCREAMING_SNAKE_CASE_ )
										else:
																raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}''' )

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

										#  set all values
										self.set_timesteps(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )

				def  _lowerCamelCase   (				self				:       str ,SCREAMING_SNAKE_CASE_				:       List[str] ,SCREAMING_SNAKE_CASE_				:       List[Any]=None ):
										if schedule_timesteps is None:
																snake_case__    :   Union[str, Any]							     =   self.timesteps

										snake_case__    :   Optional[int]							     =   (schedule_timesteps == timestep).nonzero()

										# The sigma index that is taken for the **very** first `step`
										# is always the second index (or the last index if there is only 1)
										# This way we can ensure we don't accidentally skip a sigma in
										# case we start in the middle of the denoising schedule (e.g. for image-to-image)
										if len(self._index_counter ) == 0:
																snake_case__    :   List[str]							     =   1 if len(SCREAMING_SNAKE_CASE_ ) > 1 else 0
										else:
																snake_case__    :   Any							     =   timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_ ) else timestep
																snake_case__    :   int							     =   self._index_counter[timestep_int]

										return indices[pos].item()

				@property
				def  _lowerCamelCase   (				self				:       List[Any] ):
										# standard deviation of the initial noise distribution
										if self.config.timestep_spacing in ["linspace", "trailing"]:
																return self.sigmas.max()

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

				def  _lowerCamelCase   (				self				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       torch.FloatTensor ,SCREAMING_SNAKE_CASE_				:       Union[float, torch.FloatTensor] ,):
										snake_case__    :   Optional[int]							     =   self.index_for_timestep(SCREAMING_SNAKE_CASE_ )

										if self.state_in_first_order:
																snake_case__    :   Optional[Any]							     =   self.sigmas[step_index]
										else:
																snake_case__    :   str							     =   self.sigmas_interpol[step_index - 1]

										snake_case__    :   Dict							     =   sample / ((sigma**2 + 1) ** 0.5)
										return sample

				def  _lowerCamelCase   (				self				:       Dict ,SCREAMING_SNAKE_CASE_				:       int ,SCREAMING_SNAKE_CASE_				:       Union[str, torch.device] = None ,SCREAMING_SNAKE_CASE_				:       Optional[int] = None ,):
										snake_case__    :   str							     =   num_inference_steps

										snake_case__    :   Union[str, Any]							     =   num_train_timesteps or self.config.num_train_timesteps

										# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
										if self.config.timestep_spacing == "linspace":
																snake_case__    :   Optional[Any]							     =   np.linspace(0 ,num_train_timesteps - 1 ,SCREAMING_SNAKE_CASE_ ,dtype=SCREAMING_SNAKE_CASE_ )[::-1].copy()
										elif self.config.timestep_spacing == "leading":
																snake_case__    :   Optional[Any]							     =   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
																snake_case__    :   Dict							     =   (np.arange(0 ,SCREAMING_SNAKE_CASE_ ) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE_ )
																timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
																snake_case__    :   Union[str, Any]							     =   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
																snake_case__    :   List[Any]							     =   (np.arange(SCREAMING_SNAKE_CASE_ ,0 ,-step_ratio )).round().copy().astype(SCREAMING_SNAKE_CASE_ )
																timesteps -= 1
										else:
																raise ValueError(
																    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''' )

										snake_case__    :   List[str]							     =   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
										snake_case__    :   List[str]							     =   torch.from_numpy(np.log(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )

										snake_case__    :   List[str]							     =   np.interp(SCREAMING_SNAKE_CASE_ ,np.arange(0 ,len(SCREAMING_SNAKE_CASE_ ) ) ,SCREAMING_SNAKE_CASE_ )
										snake_case__    :   int							     =   np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
										snake_case__    :   Dict							     =   torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(device=SCREAMING_SNAKE_CASE_ )

										# compute up and down sigmas
										snake_case__    :   str							     =   sigmas.roll(-1 )
										snake_case__    :   str							     =   0.0
										snake_case__    :   List[Any]							     =   (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
										snake_case__    :   List[str]							     =   (sigmas_next**2 - sigmas_up**2) ** 0.5
										snake_case__    :   List[Any]							     =   0.0

										# compute interpolated sigmas
										snake_case__    :   Tuple							     =   sigmas.log().lerp(sigmas_down.log() ,0.5 ).exp()
										snake_case__    :   Optional[Any]							     =   0.0

										# set sigmas
										snake_case__    :   List[str]							     =   torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
										snake_case__    :   Optional[int]							     =   torch.cat(
										    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
										snake_case__    :   str							     =   torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2 ), sigmas_up[-1:]] )
										snake_case__    :   Optional[Any]							     =   torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2 ), sigmas_down[-1:]] )

										if str(SCREAMING_SNAKE_CASE_ ).startswith('''mps''' ):
																# mps does not support float64
																snake_case__    :   Tuple							     =   torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ ,dtype=torch.floataa )
										else:
																snake_case__    :   int							     =   torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )

										snake_case__    :   Tuple							     =   self.sigma_to_t(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ ,dtype=timesteps.dtype )
										snake_case__    :   int							     =   torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None]) ,dim=-1 ).flatten()

										snake_case__    :   Dict							     =   torch.cat([timesteps[:1], interleaved_timesteps] )

										snake_case__    :   Optional[int]							     =   None

										# for exp beta schedules, such as the one for `pipeline_shap_e.py`
										# we need an index counter
										snake_case__    :   Optional[int]							     =   defaultdict(SCREAMING_SNAKE_CASE_ )

				def  _lowerCamelCase   (				self				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       Optional[Any] ):
										# get log sigma
										snake_case__    :   Dict							     =   sigma.log()

										# get distribution
										snake_case__    :   Union[str, Any]							     =   log_sigma - self.log_sigmas[:, None]

										# get sigmas range
										snake_case__    :   Optional[Any]							     =   dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
										snake_case__    :   Any							     =   low_idx + 1

										snake_case__    :   Any							     =   self.log_sigmas[low_idx]
										snake_case__    :   str							     =   self.log_sigmas[high_idx]

										# interpolate sigmas
										snake_case__    :   int							     =   (low - log_sigma) / (low - high)
										snake_case__    :   List[Any]							     =   w.clamp(0 ,1 )

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

				@property
				def  _lowerCamelCase   (				self				:       List[Any] ):
										return self.sample is None

				def  _lowerCamelCase   (				self				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       Union[torch.FloatTensor, np.ndarray] ,SCREAMING_SNAKE_CASE_				:       Union[float, torch.FloatTensor] ,SCREAMING_SNAKE_CASE_				:       Union[torch.FloatTensor, np.ndarray] ,SCREAMING_SNAKE_CASE_				:       Optional[torch.Generator] = None ,SCREAMING_SNAKE_CASE_				:       bool = True ,):
										snake_case__    :   str							     =   self.index_for_timestep(SCREAMING_SNAKE_CASE_ )

										# advance index counter by 1
										snake_case__    :   Optional[int]							     =   timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_ ) else timestep
										self._index_counter[timestep_int] += 1

										if self.state_in_first_order:
																snake_case__    :   List[Any]							     =   self.sigmas[step_index]
																snake_case__    :   str							     =   self.sigmas_interpol[step_index]
																snake_case__    :   str							     =   self.sigmas_up[step_index]
																snake_case__    :   Optional[int]							     =   self.sigmas_down[step_index - 1]
										else:
																# 2nd order / KPDM2's method
																snake_case__    :   int							     =   self.sigmas[step_index - 1]
																snake_case__    :   Tuple							     =   self.sigmas_interpol[step_index - 1]
																snake_case__    :   Optional[Any]							     =   self.sigmas_up[step_index - 1]
																snake_case__    :   Dict							     =   self.sigmas_down[step_index - 1]

										# currently only gamma=0 is supported. This usually works best anyways.
										# We can support gamma in the future but then need to scale the timestep before
										# passing it to the model which requires a change in API
										snake_case__    :   Optional[int]							     =   0
										snake_case__    :   List[Any]							     =   sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

										snake_case__    :   Dict							     =   model_output.device
										snake_case__    :   Tuple							     =   randn_tensor(model_output.shape ,dtype=model_output.dtype ,device=SCREAMING_SNAKE_CASE_ ,generator=SCREAMING_SNAKE_CASE_ )

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										if self.config.prediction_type == "epsilon":
																snake_case__    :   List[str]							     =   sigma_hat if self.state_in_first_order else sigma_interpol
																snake_case__    :   Any							     =   sample - sigma_input * model_output
										elif self.config.prediction_type == "v_prediction":
																snake_case__    :   Tuple							     =   sigma_hat if self.state_in_first_order else sigma_interpol
																snake_case__    :   List[Any]							     =   model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																    sample / (sigma_input**2 + 1)
																)
										elif self.config.prediction_type == "sample":
																raise NotImplementedError('''prediction_type not implemented yet: sample''' )
										else:
																raise ValueError(
																    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`''' )

										if self.state_in_first_order:
																# 2. Convert to an ODE derivative for 1st order
																snake_case__    :   List[str]							     =   (sample - pred_original_sample) / sigma_hat
																# 3. delta timestep
																snake_case__    :   Tuple							     =   sigma_interpol - sigma_hat

																# store for 2nd order step
																snake_case__    :   int							     =   sample
																snake_case__    :   Tuple							     =   dt
																snake_case__    :   Tuple							     =   sample + derivative * dt
										else:
																# DPM-Solver-2
																# 2. Convert to an ODE derivative for 2nd order
																snake_case__    :   Tuple							     =   (sample - pred_original_sample) / sigma_interpol
																# 3. delta timestep
																snake_case__    :   Union[str, Any]							     =   sigma_down - sigma_hat

																snake_case__    :   int							     =   self.sample
																snake_case__    :   Tuple							     =   None

																snake_case__    :   Dict							     =   sample + derivative * dt
																snake_case__    :   Any							     =   prev_sample + noise * sigma_up

										if not return_dict:
																return (prev_sample,)

										return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_ )

				def  _lowerCamelCase   (				self				:       Any ,SCREAMING_SNAKE_CASE_				:       torch.FloatTensor ,SCREAMING_SNAKE_CASE_				:       torch.FloatTensor ,SCREAMING_SNAKE_CASE_				:       torch.FloatTensor ,):
										# Make sure sigmas and timesteps have the same device and dtype as original_samples
										snake_case__    :   Optional[int]							     =   self.sigmas.to(device=original_samples.device ,dtype=original_samples.dtype )
										if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE_ ):
																# mps does not support float64
																snake_case__    :   List[str]							     =   self.timesteps.to(original_samples.device ,dtype=torch.floataa )
																snake_case__    :   int							     =   timesteps.to(original_samples.device ,dtype=torch.floataa )
										else:
																snake_case__    :   Any							     =   self.timesteps.to(original_samples.device )
																snake_case__    :   List[str]							     =   timesteps.to(original_samples.device )

										snake_case__    :   List[Any]							     =   [self.index_for_timestep(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) for t in timesteps]

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

										snake_case__    :   List[str]							     =   original_samples + noise * sigma
										return noisy_samples





				def __len__(				self				:       int ):
										return self.config.num_train_timesteps










"""simple docstring"""





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,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

_lowercase		 =      16
_lowercase		 =      32

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase = 16						):
						'''simple docstring'''


						snake_case__    :   int							     =   AutoTokenizer.from_pretrained('''bert-base-cased'''						)
						snake_case__    :   Dict							     =   load_dataset('''glue''' ,     '''mrpc'''						)

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

						def collate_fn(lowerCamelCase						):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												snake_case__    :   Tuple							     =   128 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		snake_case__    :   Optional[Any]							     =   16
												elif accelerator.mixed_precision != "no":
																		snake_case__    :   Dict							     =   8
												else:
																		snake_case__    :   str							     =   None

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

						# Instantiate dataloaders.
						snake_case__    :   List[Any]							     =   DataLoader(
						    tokenized_datasets['''train'''] ,     shuffle=lowerCamelCase ,     collate_fn=lowerCamelCase ,     batch_size=lowerCamelCase						)
						snake_case__    :   Tuple							     =   DataLoader(
						    tokenized_datasets['''validation'''] ,     shuffle=lowerCamelCase ,     collate_fn=lowerCamelCase ,     batch_size=lowerCamelCase						)

						return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
							from accelerate.test_utils.training import mocked_dataloaders

							_lowercase		 =      mocked_dataloaders  # noqa: F811

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,     lowerCamelCase						) == "1":
												snake_case__    :   Any							     =   2
						# Initialize accelerator
						snake_case__    :   List[Any]							     =   Accelerator(cpu=args.cpu ,     mixed_precision=args.mixed_precision						)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						snake_case__    :   Any							     =   config['''lr''']
						snake_case__    :   int							     =   int(config['''num_epochs''']						)
						snake_case__    :   List[str]							     =   int(config['''seed''']						)
						snake_case__    :   Dict							     =   int(config['''batch_size''']						)

						# New Code #
						# Parse out whether we are saving every epoch or after a certain number of batches
						if hasattr(args.checkpointing_steps ,     '''isdigit'''						):
												if args.checkpointing_steps == "epoch":
																		snake_case__    :   Optional[Any]							     =   args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		snake_case__    :   Union[str, Any]							     =   int(args.checkpointing_steps						)
												else:
																		raise ValueError(
																		    f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''						)
						else:
												snake_case__    :   Union[str, Any]							     =   None

						set_seed(lowerCamelCase						)

						snake_case__     ,		snake_case__    :   int							     =   get_dataloaders(lowerCamelCase ,     lowerCamelCase						)
						snake_case__    :   Optional[Any]							     =   evaluate.load('''glue''' ,     '''mrpc'''						)

						# If the batch size is too big we use gradient accumulation
						snake_case__    :   Optional[int]							     =   1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												snake_case__    :   Any							     =   batch_size // MAX_GPU_BATCH_SIZE
												snake_case__    :   Union[str, Any]							     =   MAX_GPU_BATCH_SIZE

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						snake_case__    :   Tuple							     =   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).
						snake_case__    :   Dict							     =   model.to(accelerator.device						)

						# Instantiate optimizer
						snake_case__    :   Optional[int]							     =   AdamW(params=model.parameters() ,     lr=lowerCamelCase						)

						# Instantiate scheduler
						snake_case__    :   Optional[Any]							     =   get_linear_schedule_with_warmup(
						    optimizer=lowerCamelCase ,     num_warmup_steps=100 ,     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.
						snake_case__     ,		snake_case__     ,		snake_case__     ,		snake_case__     ,		snake_case__    :   Optional[Any]							     =   accelerator.prepare(
						    lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						)

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

						# We need to load the checkpoint back in before training here with `load_state`
						# The total number of epochs is adjusted based on where the state is being loaded from,
						# as we assume continuation of the same training script
						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						)
																		snake_case__    :   Optional[Any]							     =   os.path.basename(args.resume_from_checkpoint						)
												else:
																		# Get the most recent checkpoint
																		snake_case__    :   Any							     =   [f.name for f in os.scandir(os.getcwd()						) if f.is_dir()]
																		dirs.sort(key=os.path.getctime						)
																		snake_case__    :   str							     =   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												snake_case__    :   List[Any]							     =   os.path.splitext(lowerCamelCase						)[0]

												if "epoch" in training_difference:
																		snake_case__    :   Optional[Any]							     =   int(training_difference.replace('''epoch_''' ,     ''''''						)						) + 1
																		snake_case__    :   List[str]							     =   None
												else:
																		snake_case__    :   int							     =   int(training_difference.replace('''step_''' ,     ''''''						)						)
																		snake_case__    :   Union[str, Any]							     =   resume_step // len(lowerCamelCase						)
																		resume_step -= starting_epoch * len(lowerCamelCase						)

    # Now we train the model
						for epoch in range(lowerCamelCase ,     lowerCamelCase						):
												model.train()
												# New Code #
												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
																		snake_case__    :   int							     =   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
																		snake_case__    :   Tuple							     =   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						)
																		snake_case__    :   Tuple							     =   model(**lowerCamelCase						)
																		snake_case__    :   Optional[int]							     =   outputs.loss
																		snake_case__    :   Union[str, Any]							     =   loss / gradient_accumulation_steps
																		accelerator.backward(lowerCamelCase						)
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()
																		# New Code #
																		overall_step += 1

																		# New Code #
																		# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
																		# These are saved to folders named `step_{overall_step}`
																		# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
																		# If mixed precision was used, will also save a "scalar.bin" file
																		if isinstance(lowerCamelCase ,     lowerCamelCase						):
																								snake_case__    :   Tuple							     =   f'''step_{overall_step}'''
																								if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																				snake_case__    :   Optional[int]							     =   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` (the default).
																		batch.to(accelerator.device						)
																		with torch.no_grad():
																								snake_case__    :   List[str]							     =   model(**lowerCamelCase						)
																		snake_case__    :   Optional[int]							     =   outputs.logits.argmax(dim=-1						)
																		snake_case__     ,		snake_case__    :   Dict							     =   accelerator.gather_for_metrics((predictions, batch['''labels'''])						)
																		metric.add_batch(
																		    predictions=lowerCamelCase ,     references=lowerCamelCase ,     )

												snake_case__    :   str							     =   metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f'''epoch {epoch}:''' ,     lowerCamelCase						)

												# New Code #
												# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
												# These are saved to folders named `epoch_{epoch}`
												# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
												# If mixed precision was used, will also save a "scalar.bin" file
												if checkpointing_steps == "epoch":
																		snake_case__    :   str							     =   f'''epoch_{epoch}'''
																		if args.output_dir is not None:
																								snake_case__    :   Optional[int]							     =   os.path.join(args.output_dir ,     lowerCamelCase						)
																		accelerator.save_state(lowerCamelCase						)

def 						lowerCAmelCase							(	):
						'''simple docstring'''


						snake_case__    :   Tuple							     =   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(
						    '''--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(
						    '''--resume_from_checkpoint''' ,     type=lowerCamelCase ,     default=lowerCamelCase ,     help='''If the training should continue from a checkpoint folder.''' ,     )
						snake_case__    :   List[str]							     =   parser.parse_args()
						snake_case__    :   str							     =   {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(lowerCamelCase ,     lowerCamelCase						)


if __name__ == "__main__":
							main()


