



"""simple docstring"""
import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					_A		:			Dict						=     (IPNDMScheduler,)
					_A		:			Any						=     (("""num_inference_steps""", 5_0),)






					def    lowerCAmelCase_				(       self:							Union[str, Any]						,     **snake_case:							str						)   ->	Optional[int]:
											snake_case_ :Tuple						=			{"""num_train_timesteps""": 1_000}
											config.update(**snake_case						)
											return config






					def    lowerCAmelCase_				(       self:							List[str]						,     snake_case:							int=0						,     **snake_case:							str						)   ->	Optional[Any]:
											snake_case_ :List[Any]						=			dict(self.forward_default_kwargs						)
											snake_case_ :str						=			kwargs.pop("""num_inference_steps"""						,     snake_case						)
											snake_case_ :Optional[int]						=			self.dummy_sample
											snake_case_ :Tuple						=			0.1 * sample
											snake_case_ :str						=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

											for scheduler_class in self.scheduler_classes:
																	snake_case_ :int						=			self.get_scheduler_config(**snake_case						)
																	snake_case_ :str						=			scheduler_class(**snake_case						)
																	scheduler.set_timesteps(snake_case						)
																	# copy over dummy past residuals
																	snake_case_ :str						=			dummy_past_residuals[:]

																	if time_step is None:
																							snake_case_ :Optional[Any]						=			scheduler.timesteps[len(scheduler.timesteps						) // 2]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																							scheduler.save_config(snake_case						)
																							snake_case_ :List[str]						=			scheduler_class.from_pretrained(snake_case						)
																							new_scheduler.set_timesteps(snake_case						)
																							# copy over dummy past residuals
																							snake_case_ :str						=			dummy_past_residuals[:]

																	snake_case_ :int						=			scheduler.step(snake_case						,     snake_case						,     snake_case						,     **snake_case						).prev_sample
																	snake_case_ :Optional[int]						=			new_scheduler.step(snake_case						,     snake_case						,     snake_case						,     **snake_case						).prev_sample

																	assert torch.sum(torch.abs(output - new_output						)						) < 1E-5, "Scheduler outputs are not identical"

																	snake_case_ :Optional[int]						=			scheduler.step(snake_case						,     snake_case						,     snake_case						,     **snake_case						).prev_sample
																	snake_case_ :Tuple						=			new_scheduler.step(snake_case						,     snake_case						,     snake_case						,     **snake_case						).prev_sample

																	assert torch.sum(torch.abs(output - new_output						)						) < 1E-5, "Scheduler outputs are not identical"






					def    lowerCAmelCase_				(       self:							str						)   ->	List[str]:
											pass






					def    lowerCAmelCase_				(       self:							Union[str, Any]						,     snake_case:							str=0						,     **snake_case:							Dict						)   ->	Tuple:
											snake_case_ :List[str]						=			dict(self.forward_default_kwargs						)
											snake_case_ :Optional[int]						=			kwargs.pop("""num_inference_steps"""						,     snake_case						)
											snake_case_ :Dict						=			self.dummy_sample
											snake_case_ :Optional[Any]						=			0.1 * sample
											snake_case_ :Dict						=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

											for scheduler_class in self.scheduler_classes:
																	snake_case_ :Tuple						=			self.get_scheduler_config()
																	snake_case_ :List[str]						=			scheduler_class(**snake_case						)
																	scheduler.set_timesteps(snake_case						)

																	# copy over dummy past residuals (must be after setting timesteps)
																	snake_case_ :Any						=			dummy_past_residuals[:]

																	if time_step is None:
																							snake_case_ :Dict						=			scheduler.timesteps[len(scheduler.timesteps						) // 2]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																							scheduler.save_config(snake_case						)
																							snake_case_ :int						=			scheduler_class.from_pretrained(snake_case						)
																							# copy over dummy past residuals
																							new_scheduler.set_timesteps(snake_case						)

																							# copy over dummy past residual (must be after setting timesteps)
																							snake_case_ :Union[str, Any]						=			dummy_past_residuals[:]

																	snake_case_ :List[Any]						=			scheduler.step(snake_case						,     snake_case						,     snake_case						,     **snake_case						).prev_sample
																	snake_case_ :Union[str, Any]						=			new_scheduler.step(snake_case						,     snake_case						,     snake_case						,     **snake_case						).prev_sample

																	assert torch.sum(torch.abs(output - new_output						)						) < 1E-5, "Scheduler outputs are not identical"

																	snake_case_ :Tuple						=			scheduler.step(snake_case						,     snake_case						,     snake_case						,     **snake_case						).prev_sample
																	snake_case_ :List[str]						=			new_scheduler.step(snake_case						,     snake_case						,     snake_case						,     **snake_case						).prev_sample

																	assert torch.sum(torch.abs(output - new_output						)						) < 1E-5, "Scheduler outputs are not identical"






					def    lowerCAmelCase_				(       self:							Optional[int]						,     **snake_case:							Tuple						)   ->	str:
											snake_case_ :int						=			self.scheduler_classes[0]
											snake_case_ :List[Any]						=			self.get_scheduler_config(**snake_case						)
											snake_case_ :int						=			scheduler_class(**snake_case						)

											snake_case_ :str						=			10
											snake_case_ :Optional[Any]						=			self.dummy_model()
											snake_case_ :int						=			self.dummy_sample_deter
											scheduler.set_timesteps(snake_case						)

											for i, t in enumerate(scheduler.timesteps						):
																	snake_case_ :Tuple						=			model(snake_case						,     snake_case						)
																	snake_case_ :Dict						=			scheduler.step(snake_case						,     snake_case						,     snake_case						).prev_sample

											for i, t in enumerate(scheduler.timesteps						):
																	snake_case_ :Optional[int]						=			model(snake_case						,     snake_case						)
																	snake_case_ :Any						=			scheduler.step(snake_case						,     snake_case						,     snake_case						).prev_sample

											return sample






					def    lowerCAmelCase_				(       self:							str						)   ->	List[str]:
											snake_case_ :str						=			dict(self.forward_default_kwargs						)

											snake_case_ :int						=			kwargs.pop("""num_inference_steps"""						,     snake_case						)

											for scheduler_class in self.scheduler_classes:
																	snake_case_ :List[str]						=			self.get_scheduler_config()
																	snake_case_ :Any						=			scheduler_class(**snake_case						)

																	snake_case_ :List[str]						=			self.dummy_sample
																	snake_case_ :Dict						=			0.1 * sample

																	if num_inference_steps is not None and hasattr(snake_case						,     """set_timesteps"""						):
																							scheduler.set_timesteps(snake_case						)
																	elif num_inference_steps is not None and not hasattr(snake_case						,     """set_timesteps"""						):
																							snake_case_ :str						=			num_inference_steps

																	# copy over dummy past residuals (must be done after set_timesteps)
																	snake_case_ :Union[str, Any]						=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
																	snake_case_ :Optional[int]						=			dummy_past_residuals[:]

																	snake_case_ :Optional[int]						=			scheduler.timesteps[5]
																	snake_case_ :str						=			scheduler.timesteps[6]

																	snake_case_ :int						=			scheduler.step(snake_case						,     snake_case						,     snake_case						,     **snake_case						).prev_sample
																	snake_case_ :List[str]						=			scheduler.step(snake_case						,     snake_case						,     snake_case						,     **snake_case						).prev_sample

																	self.assertEqual(output_a.shape						,     sample.shape						)
																	self.assertEqual(output_a.shape						,     output_a.shape						)

																	snake_case_ :str						=			scheduler.step(snake_case						,     snake_case						,     snake_case						,     **snake_case						).prev_sample
																	snake_case_ :Optional[int]						=			scheduler.step(snake_case						,     snake_case						,     snake_case						,     **snake_case						).prev_sample

																	self.assertEqual(output_a.shape						,     sample.shape						)
																	self.assertEqual(output_a.shape						,     output_a.shape						)






					def    lowerCAmelCase_				(       self:							Tuple						)   ->	Dict:
											for timesteps in [100, 1_000]:
																	self.check_over_configs(num_train_timesteps=snake_case						,     time_step=snake_case						)






					def    lowerCAmelCase_				(       self:							int						)   ->	Optional[int]:
											for t, num_inference_steps in zip([1, 5, 10]						,     [10, 50, 100]						):
																	self.check_over_forward(num_inference_steps=snake_case						,     time_step=snake_case						)






					def    lowerCAmelCase_				(       self:							Optional[Any]						)   ->	Optional[Any]:
											snake_case_ :List[Any]						=			self.full_loop()
											snake_case_ :str						=			torch.mean(torch.abs(snake_case						)						)

											assert abs(result_mean.item() - 2_540_529						) < 10






"""simple docstring"""






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


lowercase__     						=						16
lowercase__     						=						32







def      _snake_case			(				lowercase__							,    lowercase__ = 16							,    lowercase__ = "bert-base-cased"		):
				_lowerCamelCase     :			List[Any]    =    AutoTokenizer.from_pretrained(lowercase__		)
				_lowerCamelCase     :			Tuple    =    load_dataset('glue'							,    'mrpc'		)

				def tokenize_function(lowercase__		):
								# max_length=None => use the model max length (it's actually the default)
								_lowerCamelCase     :			Union[str, Any]    =    tokenizer(examples['sentence1']							,    examples['sentence2']							,    truncation=lowercase__							,    max_length=lowercase__		)
								return outputs

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

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

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

				# Instantiate dataloaders.
				_lowerCamelCase     :			List[str]    =    DataLoader(
				    tokenized_datasets['train']							,    shuffle=lowercase__							,    collate_fn=lowercase__							,    batch_size=lowercase__		)
				_lowerCamelCase     :			int    =    DataLoader(
				    tokenized_datasets['validation']							,    shuffle=lowercase__							,    collate_fn=lowercase__							,    batch_size=lowercase__		)

				return train_dataloader, eval_dataloader







def      _snake_case			(				lowercase__							,    lowercase__		):
				# Initialize accelerator
				_lowerCamelCase     :			Optional[int]    =    Accelerator()

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

				set_seed(lowercase__		)
				_lowerCamelCase, _lowerCamelCase     :			Optional[int]    =    get_dataloaders(lowercase__							,    lowercase__							,    lowercase__		)

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

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

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

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

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

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

												overall_step += 1

								model.eval()
								_lowerCamelCase     :			Union[str, Any]    =    0
								for step, batch in enumerate(lowercase__		):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device		)
												with torch.no_grad():
																_lowerCamelCase     :			Optional[int]    =    model(**lowercase__		)
												_lowerCamelCase     :			Dict    =    outputs.logits.argmax(dim=-1		)
												# It is slightly faster to call this once, than multiple times
												_lowerCamelCase, _lowerCamelCase     :			List[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(lowercase__		) - 1:
																				_lowerCamelCase     :			Optional[Any]    =    predictions[: len(eval_dataloader.dataset		) - samples_seen]
																				_lowerCamelCase     :			Dict    =    references[: len(eval_dataloader.dataset		) - samples_seen]
																else:
																				samples_seen += references.shape[0]
												metric.add_batch(
												    predictions=lowercase__							,    references=lowercase__							,    )

								_lowerCamelCase     :			List[Any]    =    metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f'''epoch {epoch}:'''							,    lowercase__		)
								_lowerCamelCase     :			Tuple    =    eval_metric['accuracy']

								if best_performance < eval_metric["accuracy"]:
												_lowerCamelCase     :			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(lowercase__							,    lowercase__		)







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


if __name__ == "__main__":
			main()