



'''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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCamelCase	 =       16
lowerCamelCase	 =       32



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase = 16					):



							"""simple docstring"""



							__lowercase       	=AutoTokenizer.from_pretrained('bert-base-cased'					)
							__lowercase       	=load_dataset('glue'  ,   'mrpc'					)

							def tokenize_function(_lowerCAmelCase					):
														# max_length=None => use the model max length (it's actually the default)
														__lowercase       	=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():
														__lowercase       	=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
							__lowercase       	=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.
														__lowercase       	=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":
																					__lowercase       	=16
														elif accelerator.mixed_precision != "no":
																					__lowercase       	=8
														else:
																					__lowercase       	=None

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

							# Instantiate dataloaders.
							__lowercase       	=DataLoader(
							    tokenized_datasets['train']  ,   shuffle=_lowerCAmelCase  ,   collate_fn=_lowerCAmelCase  ,   batch_size=_lowerCAmelCase					)
							__lowercase       	=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

		lowerCamelCase	 =       mocked_dataloaders  # noqa: F811



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							if os.environ.get('TESTING_MOCKED_DATALOADERS'  ,   _lowerCAmelCase					) == "1":
														__lowercase       	=2
							# New Code #
							__lowercase       	=int(args.gradient_accumulation_steps					)
							__lowercase       	=int(args.local_sgd_steps					)
							# Initialize accelerator
							__lowercase       	=Accelerator(
							    cpu=args.cpu  ,   mixed_precision=args.mixed_precision  ,   gradient_accumulation_steps=_lowerCAmelCase					)
							if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
														raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'					)
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							__lowercase       	=config['lr']
							__lowercase       	=int(config['num_epochs']					)
							__lowercase       	=int(config['seed']					)
							__lowercase       	=int(config['batch_size']					)

							__lowercase       	=evaluate.load('glue'  ,   'mrpc'					)

							set_seed(_lowerCAmelCase					)
							__lowercase	,				__lowercase       	=get_dataloaders(_lowerCAmelCase  ,   _lowerCAmelCase					)
							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							__lowercase       	=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).
							__lowercase       	=model.to(accelerator.device					)

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

							# Instantiate scheduler
							__lowercase       	=get_linear_schedule_with_warmup(
							    optimizer=_lowerCAmelCase  ,   num_warmup_steps=100  ,   num_training_steps=(len(_lowerCAmelCase					) * num_epochs)  ,   )

							# 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.
							__lowercase	,				__lowercase	,				__lowercase	,				__lowercase	,				__lowercase       	=accelerator.prepare(
							    _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					)

							# Now we train the model
							for epoch in range(_lowerCAmelCase					):
														model.train()
														with LocalSGD(
														    accelerator=_lowerCAmelCase  ,   model=_lowerCAmelCase  ,   local_sgd_steps=_lowerCAmelCase  ,   enabled=local_sgd_steps is not None					) as local_sgd:
																					for step, batch in enumerate(_lowerCAmelCase					):
																												# We could avoid this line since we set the accelerator with `device_placement=True`.
																												batch.to(accelerator.device					)
																												# New code #
																												# We use the new `accumulate` context manager to perform gradient accumulation
																												# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
																												with accelerator.accumulate(_lowerCAmelCase					):
																																			__lowercase       	=model(**_lowerCAmelCase					)
																																			__lowercase       	=output.loss
																																			accelerator.backward(_lowerCAmelCase					)
																																			optimizer.step()
																																			lr_scheduler.step()
																																			optimizer.zero_grad()
																																			# LocalSGD-specific line
																																			local_sgd.step()

														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():
																												__lowercase       	=model(**_lowerCAmelCase					)
																					__lowercase       	=outputs.logits.argmax(dim=-1					)
																					__lowercase	,				__lowercase       	=accelerator.gather_for_metrics((predictions, batch['labels'])					)
																					metric.add_batch(
																					    predictions=_lowerCAmelCase  ,   references=_lowerCAmelCase  ,   )

														__lowercase       	=metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(f"""epoch {epoch}:"""  ,   _lowerCAmelCase					)



def   _A       (   ):



							"""simple docstring"""



							__lowercase       	=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.'  ,   )
							# New Code #
							parser.add_argument(
							    '--gradient_accumulation_steps'  ,   type=_lowerCAmelCase  ,   default=1  ,   help='The number of minibatches to be ran before gradients are accumulated.'  ,   )
							parser.add_argument(
							    '--local_sgd_steps'  ,   type=_lowerCAmelCase  ,   default=8  ,   help='Number of local SGD steps or None to disable local SGD'					)
							parser.add_argument('--cpu'  ,   action='store_true'  ,   help='If passed, will train on the CPU.'					)
							__lowercase       	=parser.parse_args()
							__lowercase       	={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
							training_function(_lowerCAmelCase  ,   _lowerCAmelCase					)


if __name__ == "__main__":
		main()








'''simple docstring'''



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

import numpy as np
import torch

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





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class _UpperCamelCase		(     A		):





				'''simple docstring'''





				lowerCAmelCase__					    =    42
				lowerCAmelCase__					    =    None



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase=0.9_99  ,   _lowerCAmelCase="cosine"  ,   ):



							"""simple docstring"""



							if alpha_transform_type == "cosine":

														def alpha_bar_fn(_lowerCAmelCase					):
																					return math.cos((t + 0.0_08) / 1.0_08 * 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}"""					)

							__lowercase       	=[]
							for i in range(_lowerCAmelCase					):
														__lowercase       	=i / num_diffusion_timesteps
														__lowercase       	=(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 _UpperCamelCase		(     A      ,       A		):





				'''simple docstring'''





				@register_to_config
				def __init__(							self  :  Any  ,       _lowerCAmelCase  :  int = 1_0_0_0  ,       _lowerCAmelCase  :  str = "fixed_small_log"  ,       _lowerCAmelCase  :  bool = True  ,       _lowerCAmelCase  :  Optional[float] = 1.0  ,       _lowerCAmelCase  :  str = "epsilon"  ,       _lowerCAmelCase  :  str = "squaredcos_cap_v2"  ,       ):







											'''simple docstring'''

											if beta_schedule != "squaredcos_cap_v2":
																		raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'')

											__lowercase       	=betas_for_alpha_bar(_lowerCAmelCase)

											__lowercase       	=1.0 - self.betas
											__lowercase       	=torch.cumprod(self.alphas  ,       dim=0)
											__lowercase       	=torch.tensor(1.0)

											# standard deviation of the initial noise distribution
											__lowercase       	=1.0

											# setable values
											__lowercase       	=None
											__lowercase       	=torch.from_numpy(np.arange(0  ,       _lowerCAmelCase)[::-1].copy())

											__lowercase       	=variance_type

				def 				__lowerCamelCase     (							self  :  int  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  Optional[int] = None):







											'''simple docstring'''

											return sample

				def 				__lowerCamelCase     (							self  :  Tuple  ,       _lowerCAmelCase  :  int  ,       _lowerCAmelCase  :  Union[str, torch.device] = None):







											'''simple docstring'''

											__lowercase       	=num_inference_steps
											__lowercase       	=(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
											__lowercase       	=(np.arange(0  ,       _lowerCAmelCase) * step_ratio).round()[::-1].copy().astype(np.intaa)
											__lowercase       	=torch.from_numpy(_lowerCAmelCase).to(_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  str  ,       _lowerCAmelCase  :  List[str]  ,       _lowerCAmelCase  :  Dict=None  ,       _lowerCAmelCase  :  Dict=None  ,       _lowerCAmelCase  :  int=None):







											'''simple docstring'''

											if prev_timestep is None:
																		__lowercase       	=t - 1

											__lowercase       	=self.alphas_cumprod[t]
											__lowercase       	=self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
											__lowercase       	=1 - alpha_prod_t
											__lowercase       	=1 - alpha_prod_t_prev

											if prev_timestep == t - 1:
																		__lowercase       	=self.betas[t]
											else:
																		__lowercase       	=1 - alpha_prod_t / alpha_prod_t_prev

											# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
											# and sample from it to get previous sample
											# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
											__lowercase       	=beta_prod_t_prev / beta_prod_t * beta

											if variance_type is None:
																		__lowercase       	=self.config.variance_type

											# hacks - were probably added for training stability
											if variance_type == "fixed_small_log":
																		__lowercase       	=torch.log(torch.clamp(_lowerCAmelCase  ,       min=1e-20))
																		__lowercase       	=torch.exp(0.5 * variance)
											elif variance_type == "learned_range":
																		# NOTE difference with DDPM scheduler
																		__lowercase       	=variance.log()
																		__lowercase       	=beta.log()

																		__lowercase       	=(predicted_variance + 1) / 2
																		__lowercase       	=frac * max_log + (1 - frac) * min_log

											return variance

				def 				__lowerCamelCase     (							self  :  Union[str, Any]  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  int  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  Optional[int] = None  ,       _lowerCAmelCase  :  Optional[int]=None  ,       _lowerCAmelCase  :  bool = True  ,       ):







											'''simple docstring'''

											__lowercase       	=timestep

											if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
																		__lowercase	,				__lowercase       	=torch.split(_lowerCAmelCase  ,       sample.shape[1]  ,       dim=1)
											else:
																		__lowercase       	=None

											# 1. compute alphas, betas
											if prev_timestep is None:
																		__lowercase       	=t - 1

											__lowercase       	=self.alphas_cumprod[t]
											__lowercase       	=self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
											__lowercase       	=1 - alpha_prod_t
											__lowercase       	=1 - alpha_prod_t_prev

											if prev_timestep == t - 1:
																		__lowercase       	=self.betas[t]
																		__lowercase       	=self.alphas[t]
											else:
																		__lowercase       	=1 - alpha_prod_t / alpha_prod_t_prev
																		__lowercase       	=1 - beta

											# 2. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
											if self.config.prediction_type == "epsilon":
																		__lowercase       	=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
											elif self.config.prediction_type == "sample":
																		__lowercase       	=model_output
											else:
																		raise ValueError(
																		    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
																		    ' for the UnCLIPScheduler.')

											# 3. Clip "predicted x_0"
											if self.config.clip_sample:
																		__lowercase       	=torch.clamp(
																		    _lowerCAmelCase  ,       -self.config.clip_sample_range  ,       self.config.clip_sample_range)

											# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
											# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
											__lowercase       	=(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
											__lowercase       	=alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

											# 5. Compute predicted previous sample µ_t
											# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
											__lowercase       	=pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

											# 6. Add noise
											__lowercase       	=0
											if t > 0:
																		__lowercase       	=randn_tensor(
																		    model_output.shape  ,       dtype=model_output.dtype  ,       generator=_lowerCAmelCase  ,       device=model_output.device)

																		__lowercase       	=self._get_variance(
																		    _lowerCAmelCase  ,       predicted_variance=_lowerCAmelCase  ,       prev_timestep=_lowerCAmelCase  ,       )

																		if self.variance_type == "fixed_small_log":
																									__lowercase       	=variance
																		elif self.variance_type == "learned_range":
																									__lowercase       	=(0.5 * variance).exp()
																		else:
																									raise ValueError(
																									    f"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
																									    ' for the UnCLIPScheduler.')

																		__lowercase       	=variance * variance_noise

											__lowercase       	=pred_prev_sample + variance

											if not return_dict:
																		return (pred_prev_sample,)

											return UnCLIPSchedulerOutput(prev_sample=_lowerCAmelCase  ,       pred_original_sample=_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  int  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  torch.IntTensor  ,       ):







											'''simple docstring'''

											__lowercase       	=self.alphas_cumprod.to(device=original_samples.device  ,       dtype=original_samples.dtype)
											__lowercase       	=timesteps.to(original_samples.device)

											__lowercase       	=alphas_cumprod[timesteps] ** 0.5
											__lowercase       	=sqrt_alpha_prod.flatten()
											while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
																		__lowercase       	=sqrt_alpha_prod.unsqueeze(-1)

											__lowercase       	=(1 - alphas_cumprod[timesteps]) ** 0.5
											__lowercase       	=sqrt_one_minus_alpha_prod.flatten()
											while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
																		__lowercase       	=sqrt_one_minus_alpha_prod.unsqueeze(-1)

											__lowercase       	=sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
											return noisy_samples



