

import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler


lowerCAmelCase__								=		1_6
lowerCAmelCase__								=		3_2
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Accelerator							,       UpperCAmelCase_							:     int = 1_6							,       UpperCAmelCase_							:     str = "bert-base-cased"	)						->				Tuple:




	'''simple docstring'''




	_UpperCamelCase   :		Union[str, Any]			=						AutoTokenizer.from_pretrained(UpperCAmelCase_	)
	_UpperCamelCase   :		Optional[int]			=						load_dataset('glue'							,       'mrpc'	)

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

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

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

	def collate_fn(UpperCAmelCase_							:     List[Any]	):
		# 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(UpperCAmelCase_							,       padding='max_length'							,       max_length=1_2_8							,       return_tensors='pt'	)
		return tokenizer.pad(UpperCAmelCase_							,       padding='longest'							,       return_tensors='pt'	)

	# Instantiate dataloaders.
	_UpperCamelCase   :		List[str]			=						DataLoader(
	    tokenized_datasets['train']							,       shuffle=UpperCAmelCase_							,       collate_fn=UpperCAmelCase_							,       batch_size=UpperCAmelCase_	)
	_UpperCamelCase   :		Any			=						DataLoader(
	    tokenized_datasets['validation']							,       shuffle=UpperCAmelCase_							,       collate_fn=UpperCAmelCase_							,       batch_size=UpperCAmelCase_	)

	return train_dataloader, eval_dataloader
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Union[str, Any]							,       UpperCAmelCase_							:     str	)						->				str:




	'''simple docstring'''




	_UpperCamelCase   :		int			=						Accelerator()

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

	set_seed(UpperCAmelCase_	)
	_UpperCamelCase   :		Optional[Any]			=						get_dataloaders(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)

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

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

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

	# Instantiate scheduler
	if (
	    accelerator.state.deepspeed_plugin is None
	    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
	):
		_UpperCamelCase   :		Optional[int]			=						get_linear_schedule_with_warmup(
		    optimizer=UpperCAmelCase_							,       num_warmup_steps=0							,       num_training_steps=UpperCAmelCase_							,       )
	else:
		_UpperCamelCase   :		Any			=						DummyScheduler(UpperCAmelCase_							,       total_num_steps=UpperCAmelCase_							,       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.
	_UpperCamelCase   :		str			=						accelerator.prepare(
	    UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)

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

	# Now we train the model
	_UpperCamelCase   :		str			=						evaluate.load('glue'							,       'mrpc'	)
	_UpperCamelCase   :		Dict			=						0
	_UpperCamelCase   :		List[str]			=						{}
	for epoch in range(UpperCAmelCase_							,       UpperCAmelCase_	):
		model.train()
		for step, batch in enumerate(UpperCAmelCase_	):
			_UpperCamelCase   :		List[str]			=						model(**UpperCAmelCase_	)
			_UpperCamelCase   :		List[str]			=						outputs.loss
			_UpperCamelCase   :		Any			=						loss / gradient_accumulation_steps
			accelerator.backward(UpperCAmelCase_	)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1

		model.eval()
		_UpperCamelCase   :		Optional[int]			=						0
		for step, batch in enumerate(UpperCAmelCase_	):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device	)
			with torch.no_grad():
				_UpperCamelCase   :		List[str]			=						model(**UpperCAmelCase_	)
			_UpperCamelCase   :		Optional[Any]			=						outputs.logits.argmax(dim=-1	)
			# It is slightly faster to call this once, than multiple times
			_UpperCamelCase   :		str			=						accelerator.gather(
			    (predictions, batch['labels'])	)  # If we are in a multiprocess environment, the last batch has duplicates
			if accelerator.use_distributed:
				if step == len(UpperCAmelCase_	) - 1:
					_UpperCamelCase   :		Union[str, Any]			=						predictions[: len(eval_dataloader.dataset	) - samples_seen]
					_UpperCamelCase   :		Tuple			=						references[: len(eval_dataloader.dataset	) - samples_seen]
				else:
					samples_seen += references.shape[0]
			metric.add_batch(
			    predictions=UpperCAmelCase_							,       references=UpperCAmelCase_							,       )

		_UpperCamelCase   :		Any			=						metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F'''epoch {epoch}:'''							,       UpperCAmelCase_	)
		_UpperCamelCase   :		Any			=						eval_metric['accuracy']

		if best_performance < eval_metric["accuracy"]:
			_UpperCamelCase   :		Optional[Any]			=						eval_metric['accuracy']

	if args.performance_lower_bound is not None:
		assert (
		    args.performance_lower_bound <= best_performance
		), F'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

	accelerator.wait_for_everyone()
	if accelerator.is_main_process:
		with open(os.path.join(args.output_dir							,       'all_results.json'	)							,       'w'	) as f:
			json.dump(UpperCAmelCase_							,       UpperCAmelCase_	)
def 					lowerCamelCase_     (							)						->				Optional[int]:




	'''simple docstring'''




	_UpperCamelCase   :		int			=						argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'	)
	parser.add_argument(
	    '--model_name_or_path'							,       type=UpperCAmelCase_							,       default='bert-base-cased'							,       help='Path to pretrained model or model identifier from huggingface.co/models.'							,       required=UpperCAmelCase_							,       )
	parser.add_argument(
	    '--output_dir'							,       type=UpperCAmelCase_							,       default='.'							,       help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'							,       )
	parser.add_argument(
	    '--performance_lower_bound'							,       type=UpperCAmelCase_							,       default=UpperCAmelCase_							,       help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'							,       )
	parser.add_argument(
	    '--num_epochs'							,       type=UpperCAmelCase_							,       default=3							,       help='Number of train epochs.'							,       )
	_UpperCamelCase   :		Tuple			=						parser.parse_args()
	_UpperCamelCase   :		Optional[int]			=						{'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 4_2, 'batch_size': 1_6}
	training_function(UpperCAmelCase_							,       UpperCAmelCase_	)


if __name__ == "__main__":
							main()







import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


lowerCAmelCase__								=		False







class 	lowercase    (				unittest.TestCase							):


		"""simple docstring"""




		def 		A__					(				self	,       __snake_case=32):
			set_seed(0)
			_UpperCamelCase   :		int			=						UNetaDModel(sample_size=__snake_case	,       in_channels=3	,       out_channels=3)
			_UpperCamelCase   :		str			=						torch.optim.SGD(model.parameters()	,       lr=0.0_0_0_1)
			return model, optimizer






		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
			_UpperCamelCase   :		List[Any]			=						DDPMScheduler(
			    num_train_timesteps=10_00	,       beta_start=0.0_0_0_1	,       beta_end=0.0_2	,       beta_schedule='linear'	,       clip_sample=__snake_case	,       )
			_UpperCamelCase   :		List[Any]			=						DDIMScheduler(
			    num_train_timesteps=10_00	,       beta_start=0.0_0_0_1	,       beta_end=0.0_2	,       beta_schedule='linear'	,       clip_sample=__snake_case	,       )

			assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

			# shared batches for DDPM and DDIM
			set_seed(0)
			_UpperCamelCase   :		Optional[Any]			=						[torch.randn((4, 3, 32, 32)).clip(-1	,       1).to(__snake_case) for _ in range(4)]
			_UpperCamelCase   :		str			=						[torch.randn((4, 3, 32, 32)).to(__snake_case) for _ in range(4)]
			_UpperCamelCase   :		int			=						[torch.randint(0	,       10_00	,       (4,)).long().to(__snake_case) for _ in range(4)]

			# train with a DDPM scheduler
			_UpperCamelCase	,		_UpperCamelCase   :		List[Any]			=						self.get_model_optimizer(resolution=32)
			model.train().to(__snake_case)
			for i in range(4):
				optimizer.zero_grad()
				_UpperCamelCase   :		int			=						ddpm_scheduler.add_noise(clean_images[i]	,       noise[i]	,       timesteps[i])
				_UpperCamelCase   :		Any			=						model(__snake_case	,       timesteps[i]).sample
				_UpperCamelCase   :		str			=						torch.nn.functional.mse_loss(__snake_case	,       noise[i])
				loss.backward()
				optimizer.step()
			del model, optimizer

			# recreate the model and optimizer, and retry with DDIM
			_UpperCamelCase	,		_UpperCamelCase   :		Union[str, Any]			=						self.get_model_optimizer(resolution=32)
			model.train().to(__snake_case)
			for i in range(4):
				optimizer.zero_grad()
				_UpperCamelCase   :		Dict			=						ddim_scheduler.add_noise(clean_images[i]	,       noise[i]	,       timesteps[i])
				_UpperCamelCase   :		Dict			=						model(__snake_case	,       timesteps[i]).sample
				_UpperCamelCase   :		Tuple			=						torch.nn.functional.mse_loss(__snake_case	,       noise[i])
				loss.backward()
				optimizer.step()
			del model, optimizer

			self.assertTrue(torch.allclose(__snake_case	,       __snake_case	,       atol=1e-5))
			self.assertTrue(torch.allclose(__snake_case	,       __snake_case	,       atol=1e-5))




