






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


__UpperCAmelCase     							=						16
__UpperCAmelCase     							=						32




def 					lowercase__				(					__snake_case   :							Accelerator					,	__snake_case   :							int = 16					,	__snake_case   :							str = "bert-base-cased"  ):


					'''simple docstring'''


					UpperCAmelCase_   :				List[Any]						=						AutoTokenizer.from_pretrained(__snake_case  )
					UpperCAmelCase_   :				Union[str, Any]						=						load_dataset('glue'					,	'mrpc'  )

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

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

					# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
					# transformers library
					UpperCAmelCase_   :				Tuple						=						tokenized_datasets.rename_column('label'					,	'labels'  )

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

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

					return train_dataloader, eval_dataloader




def 					lowercase__				(					__snake_case   :							int					,	__snake_case   :							str  ):


					'''simple docstring'''


					UpperCAmelCase_   :				List[str]						=						Accelerator()

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

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

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

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

					if accelerator.state.deepspeed_plugin is not None:
										UpperCAmelCase_   :				str						=						accelerator.state.deepspeed_plugin.deepspeed_config[
										    'gradient_accumulation_steps'
										]
					else:
										UpperCAmelCase_   :				Dict						=						1
					UpperCAmelCase_   :				Union[str, Any]						=						(len(__snake_case  ) * 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_   :				Tuple						=						get_linear_schedule_with_warmup(
										    optimizer=__snake_case					,	num_warmup_steps=0					,	num_training_steps=__snake_case					,	)
					else:
										UpperCAmelCase_   :				Union[str, Any]						=						DummyScheduler(__snake_case					,	total_num_steps=__snake_case					,	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_	,  UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				Union[str, Any]						=						accelerator.prepare(
					    __snake_case					,	__snake_case					,	__snake_case					,	__snake_case					,	__snake_case  )

					# We need to keep track of how many total steps we have iterated over
					UpperCAmelCase_   :				Any						=						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_   :				Optional[Any]						=						0
					UpperCAmelCase_   :				List[Any]						=						{}
					for epoch in range(__snake_case					,	__snake_case  ):
										model.train()
										for step, batch in enumerate(__snake_case  ):
															UpperCAmelCase_   :				Union[str, Any]						=						model(**__snake_case  )
															UpperCAmelCase_   :				int						=						outputs.loss
															UpperCAmelCase_   :				Any						=						loss / gradient_accumulation_steps
															accelerator.backward(__snake_case  )
															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(__snake_case  ):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device  )
															with torch.no_grad():
																				UpperCAmelCase_   :				Any						=						model(**__snake_case  )
															UpperCAmelCase_   :				Optional[Any]						=						outputs.logits.argmax(dim=-1  )
															# It is slightly faster to call this once, than multiple times
															UpperCAmelCase_	,  UpperCAmelCase_   :				Optional[Any]						=						accelerator.gather(
															    (predictions, batch['labels'])  )  # If we are in a multiprocess environment, the last batch has duplicates
															if accelerator.use_distributed:
																				if step == len(__snake_case  ) - 1:
																									UpperCAmelCase_   :				Tuple						=						predictions[: len(eval_dataloader.dataset  ) - samples_seen]
																									UpperCAmelCase_   :				List[Any]						=						references[: len(eval_dataloader.dataset  ) - samples_seen]
																				else:
																									samples_seen += references.shape[0]
															metric.add_batch(
															    predictions=__snake_case					,	references=__snake_case					,	)

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

										if best_performance < eval_metric["accuracy"]:
															UpperCAmelCase_   :				str						=						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(__snake_case					,	__snake_case  )




def 					lowercase__				(					):


					'''simple docstring'''


					UpperCAmelCase_   :				List[Any]						=						argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'  )
					parser.add_argument(
					    '--model_name_or_path'					,	type=__snake_case					,	default='bert-base-cased'					,	help='Path to pretrained model or model identifier from huggingface.co/models.'					,	required=__snake_case					,	)
					parser.add_argument(
					    '--output_dir'					,	type=__snake_case					,	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=__snake_case					,	default=__snake_case					,	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=__snake_case					,	default=3					,	help='Number of train epochs.'					,	)
					UpperCAmelCase_   :				Dict						=						parser.parse_args()
					UpperCAmelCase_   :				str						=						{'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
					training_function(__snake_case					,	__snake_case  )


if __name__ == "__main__":
		main()














import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Any							=  (DDPMParallelScheduler,)



		def  __UpperCAmelCase      (							self    ,		**_UpperCamelCase       )     ->							Any:
							UpperCAmelCase_   :				List[str]						=						{
							    'num_train_timesteps': 1_0_0_0,
							    'beta_start': 0.00_01,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							    'variance_type': 'fixed_small',
							    'clip_sample': True,
							}

							config.update(**_UpperCamelCase       )
							return config



		def  __UpperCAmelCase      (							self       )     ->							Any:
							for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
												self.check_over_configs(num_train_timesteps=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							int:
							for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]    ,		[0.0_02, 0.02, 0.2, 2]       ):
												self.check_over_configs(beta_start=_UpperCamelCase    ,		beta_end=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Any:
							for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Optional[Any]:
							for variance in ["fixed_small", "fixed_large", "other"]:
												self.check_over_configs(variance_type=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							for clip_sample in [True, False]:
												self.check_over_configs(clip_sample=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							self.check_over_configs(thresholding=_UpperCamelCase       )
							for threshold in [0.5, 1.0, 2.0]:
												for prediction_type in ["epsilon", "sample", "v_prediction"]:
																	self.check_over_configs(
																	    thresholding=_UpperCamelCase    ,		prediction_type=_UpperCamelCase    ,		sample_max_value=_UpperCamelCase    ,		)



		def  __UpperCAmelCase      (							self       )     ->							Any:
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
												self.check_over_configs(prediction_type=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							for t in [0, 5_0_0, 9_9_9]:
												self.check_over_forward(time_step=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				Dict						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				int						=						self.get_scheduler_config()
							UpperCAmelCase_   :				Union[str, Any]						=						scheduler_class(**_UpperCamelCase       )

							assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 0.0       )       ) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_8_7       ) - 0.0_09_79       )       ) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_9_9       ) - 0.02       )       ) < 1E-5



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				List[Any]						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				Any						=						self.get_scheduler_config()
							UpperCAmelCase_   :				Union[str, Any]						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[Any]						=						len(_UpperCamelCase       )

							UpperCAmelCase_   :				Any						=						self.dummy_model()
							UpperCAmelCase_   :				List[Any]						=						self.dummy_sample_deter
							UpperCAmelCase_   :				Union[str, Any]						=						self.dummy_sample_deter + 0.1
							UpperCAmelCase_   :				str						=						self.dummy_sample_deter - 0.1

							UpperCAmelCase_   :				Tuple						=						samplea.shape[0]
							UpperCAmelCase_   :				Tuple						=						torch.stack([samplea, samplea, samplea]    ,		dim=0       )
							UpperCAmelCase_   :				str						=						torch.arange(_UpperCamelCase       )[0:3, None].repeat(1    ,		_UpperCamelCase       )

							UpperCAmelCase_   :				Any						=						model(samples.flatten(0    ,		1       )    ,		timesteps.flatten(0    ,		1       )       )
							UpperCAmelCase_   :				Dict						=						scheduler.batch_step_no_noise(_UpperCamelCase    ,		timesteps.flatten(0    ,		1       )    ,		samples.flatten(0    ,		1       )       )

							UpperCAmelCase_   :				List[Any]						=						torch.sum(torch.abs(_UpperCamelCase       )       )
							UpperCAmelCase_   :				Any						=						torch.mean(torch.abs(_UpperCamelCase       )       )

							assert abs(result_sum.item() - 11_53.18_33       ) < 1E-2
							assert abs(result_mean.item() - 0.50_05       ) < 1E-3



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				List[str]						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				List[Any]						=						self.get_scheduler_config()
							UpperCAmelCase_   :				Any						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[int]						=						len(_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[int]						=						self.dummy_model()
							UpperCAmelCase_   :				Optional[int]						=						self.dummy_sample_deter
							UpperCAmelCase_   :				Union[str, Any]						=						torch.manual_seed(0       )

							for t in reversed(range(_UpperCamelCase       )       ):
												# 1. predict noise residual
												UpperCAmelCase_   :				Union[str, Any]						=						model(_UpperCamelCase    ,		_UpperCamelCase       )

												# 2. predict previous mean of sample x_t-1
												UpperCAmelCase_   :				str						=						scheduler.step(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		generator=_UpperCamelCase       ).prev_sample

												UpperCAmelCase_   :				str						=						pred_prev_sample

							UpperCAmelCase_   :				Optional[int]						=						torch.sum(torch.abs(_UpperCamelCase       )       )
							UpperCAmelCase_   :				Optional[int]						=						torch.mean(torch.abs(_UpperCamelCase       )       )

							assert abs(result_sum.item() - 2_58.96_06       ) < 1E-2
							assert abs(result_mean.item() - 0.33_72       ) < 1E-3



		def  __UpperCAmelCase      (							self       )     ->							int:
							UpperCAmelCase_   :				List[Any]						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				Optional[int]						=						self.get_scheduler_config(prediction_type='v_prediction'       )
							UpperCAmelCase_   :				Tuple						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Tuple						=						len(_UpperCamelCase       )

							UpperCAmelCase_   :				Tuple						=						self.dummy_model()
							UpperCAmelCase_   :				List[str]						=						self.dummy_sample_deter
							UpperCAmelCase_   :				Any						=						torch.manual_seed(0       )

							for t in reversed(range(_UpperCamelCase       )       ):
												# 1. predict noise residual
												UpperCAmelCase_   :				Optional[Any]						=						model(_UpperCamelCase    ,		_UpperCamelCase       )

												# 2. predict previous mean of sample x_t-1
												UpperCAmelCase_   :				Any						=						scheduler.step(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		generator=_UpperCamelCase       ).prev_sample

												UpperCAmelCase_   :				List[str]						=						pred_prev_sample

							UpperCAmelCase_   :				Optional[int]						=						torch.sum(torch.abs(_UpperCamelCase       )       )
							UpperCAmelCase_   :				Any						=						torch.mean(torch.abs(_UpperCamelCase       )       )

							assert abs(result_sum.item() - 2_02.02_96       ) < 1E-2
							assert abs(result_mean.item() - 0.26_31       ) < 1E-3



		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							UpperCAmelCase_   :				Any						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				Optional[Any]						=						self.get_scheduler_config()
							UpperCAmelCase_   :				str						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[int]						=						[1_0_0, 8_7, 5_0, 1, 0]

							scheduler.set_timesteps(timesteps=_UpperCamelCase       )

							UpperCAmelCase_   :				Tuple						=						scheduler.timesteps

							for i, timestep in enumerate(_UpperCamelCase       ):
												if i == len(_UpperCamelCase       ) - 1:
																	UpperCAmelCase_   :				List[str]						=						-1
												else:
																	UpperCAmelCase_   :				int						=						timesteps[i + 1]

												UpperCAmelCase_   :				int						=						scheduler.previous_timestep(_UpperCamelCase       )
												UpperCAmelCase_   :				Optional[Any]						=						prev_t.item()

												self.assertEqual(_UpperCamelCase    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							UpperCAmelCase_   :				str						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				Tuple						=						self.get_scheduler_config()
							UpperCAmelCase_   :				Union[str, Any]						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[int]						=						[1_0_0, 8_7, 5_0, 5_1, 0]

							with self.assertRaises(_UpperCamelCase    ,		msg='`custom_timesteps` must be in descending order.'       ):
												scheduler.set_timesteps(timesteps=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				Any						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				List[str]						=						self.get_scheduler_config()
							UpperCAmelCase_   :				List[str]						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Tuple						=						[1_0_0, 8_7, 5_0, 1, 0]
							UpperCAmelCase_   :				Optional[Any]						=						len(_UpperCamelCase       )

							with self.assertRaises(_UpperCamelCase    ,		msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'       ):
												scheduler.set_timesteps(num_inference_steps=_UpperCamelCase    ,		timesteps=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				List[str]						=						self.scheduler_classes[0]
							UpperCAmelCase_   :				Any						=						self.get_scheduler_config()
							UpperCAmelCase_   :				Optional[Any]						=						scheduler_class(**_UpperCamelCase       )

							UpperCAmelCase_   :				Dict						=						[scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    _UpperCamelCase    ,		msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'    ,		):
												scheduler.set_timesteps(timesteps=_UpperCamelCase       )






