




'''simple docstring'''


import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 how to perform Cross Validation,
# 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
#
########################################################################


_snake_case : List[str]  				=						16
_snake_case : int  				=						32
def 						snake_case_       (UpperCamelCase			:    Accelerator					,		UpperCamelCase			:    DatasetDict					,		UpperCamelCase			:    List[int]					,		UpperCamelCase			:    List[int]					,		UpperCamelCase			:    int = 16 ):





							'''simple docstring'''


							_a      	=							AutoTokenizer.from_pretrained('''bert-base-cased''' )
							_a      	=							DatasetDict(
							    {
							        '''train''': dataset['''train'''].select(UpperCamelCase ),
							        '''validation''': dataset['''train'''].select(UpperCamelCase ),
							        '''test''': dataset['''validation'''],
							    } )

							def tokenize_function(UpperCamelCase			:    List[str] ):
														# max_length=None => use the model max length (it's actually the default)
														_a      	=							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
							# starting with the main process first:
							with accelerator.main_process_first():
														_a      	=							datasets.map(
														    UpperCamelCase					,		batched=UpperCamelCase					,		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
							_a      	=							tokenized_datasets.rename_column('''label'''					,		'''labels''' )

							def collate_fn(UpperCamelCase			:    str ):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														_a      	=							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":
																					_a      	=							16
														elif accelerator.mixed_precision != "no":
																					_a      	=							8
														else:
																					_a      	=							None

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

							# Instantiate dataloaders.
							_a      	=							DataLoader(
							    tokenized_datasets['''train''']					,		shuffle=UpperCamelCase					,		collate_fn=UpperCamelCase					,		batch_size=UpperCamelCase )
							_a      	=							DataLoader(
							    tokenized_datasets['''validation''']					,		shuffle=UpperCamelCase					,		collate_fn=UpperCamelCase					,		batch_size=UpperCamelCase )

							_a      	=							DataLoader(
							    tokenized_datasets['''test''']					,		shuffle=UpperCamelCase					,		collate_fn=UpperCamelCase					,		batch_size=UpperCamelCase )

							return train_dataloader, eval_dataloader, test_dataloader
def 						snake_case_       (UpperCamelCase			:    Any					,		UpperCamelCase			:    List[Any] ):





							'''simple docstring'''


							_a      	=							[]
							# Download the dataset
							_a      	=							load_dataset('''glue'''					,		'''mrpc''' )
							# Create our splits
							_a      	=							StratifiedKFold(n_splits=int(args.num_folds ) )
							# Initialize accelerator
							_a      	=							Accelerator(cpu=args.cpu					,		mixed_precision=args.mixed_precision )
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							_a      	=							config['''lr''']
							_a      	=							int(config['''num_epochs'''] )
							_a      	=							int(config['''seed'''] )
							_a      	=							int(config['''batch_size'''] )

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

							# If the batch size is too big we use gradient accumulation
							_a      	=							1
							if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
														_a      	=							batch_size // MAX_GPU_BATCH_SIZE
														_a      	=							MAX_GPU_BATCH_SIZE

							set_seed(UpperCamelCase )

							# New Code #
							# Create our folds:
							_a      	=							kfold.split(np.zeros(datasets['''train'''].num_rows )					,		datasets['''train''']['''label'''] )
							_a      	=							[]
							# Iterate over them
							for i, (train_idxs, valid_idxs) in enumerate(UpperCamelCase ):
														_a	,				_a	,				_a      	=							get_fold_dataloaders(
														    UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase					,		)
														# Instantiate the model (we build the model here so that the seed also control new weights initialization)
														_a      	=							AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''					,		return_dict=UpperCamelCase )

														# 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).
														_a      	=							model.to(accelerator.device )

														# Instantiate optimizer
														_a      	=							AdamW(params=model.parameters()					,		lr=UpperCamelCase )

														# Instantiate scheduler
														_a      	=							get_linear_schedule_with_warmup(
														    optimizer=UpperCamelCase					,		num_warmup_steps=100					,		num_training_steps=(len(UpperCamelCase ) * 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.
														_a	,				_a	,				_a	,				_a	,				_a      	=							accelerator.prepare(
														    UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase )

														# Now we train the model
														for epoch in range(UpperCamelCase ):
																					model.train()
																					for step, batch in enumerate(UpperCamelCase ):
																												# We could avoid this line since we set the accelerator with `device_placement=True`.
																												batch.to(accelerator.device )
																												_a      	=							model(**UpperCamelCase )
																												_a      	=							outputs.loss
																												_a      	=							loss / gradient_accumulation_steps
																												accelerator.backward(UpperCamelCase )
																												if step % gradient_accumulation_steps == 0:
																																			optimizer.step()
																																			lr_scheduler.step()
																																			optimizer.zero_grad()

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

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

														# New Code #
														# We also run predictions on the test set at the very end
														_a      	=							[]
														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():
																												_a      	=							model(**UpperCamelCase )
																					_a      	=							outputs.logits
																					_a	,				_a      	=							accelerator.gather_for_metrics((predictions, batch['''labels''']) )
																					fold_predictions.append(predictions.cpu() )
																					if i == 0:
																												# We need all of the test predictions
																												test_references.append(references.cpu() )
        # Use accelerator.print to print only on the main process.
														test_predictions.append(torch.cat(UpperCamelCase					,		dim=0 ) )
														# We now need to release all our memory and get rid of the current model, optimizer, etc
														accelerator.free_memory()
							# New Code #
							# Finally we check the accuracy of our folded results:
							_a      	=							torch.cat(UpperCamelCase					,		dim=0 )
							_a      	=							torch.stack(UpperCamelCase					,		dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
							_a      	=							metric.compute(predictions=UpperCamelCase					,		references=UpperCamelCase )
							accelerator.print('''Average test metrics from all folds:'''					,		UpperCamelCase )
def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							argparse.ArgumentParser(description='''Simple example of training script.''' )
							parser.add_argument(
							    '''--mixed_precision'''					,		type=UpperCamelCase					,		default=UpperCamelCase					,		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.''' )
							# New Code #
							parser.add_argument('''--num_folds'''					,		type=UpperCamelCase					,		default=3					,		help='''The number of splits to perform across the dataset''' )
							_a      	=							parser.parse_args()
							_a      	=							{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
							training_function(UpperCamelCase					,		UpperCamelCase )


if __name__ == "__main__":
				main()







'''simple docstring'''


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class      A       (						_a       ):
							lowercase_							     =							(DDPMParallelScheduler,)







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     **lowerCAmelCase_			:    Optional[int]   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							{
														    '''num_train_timesteps''': 10_00,
														    '''beta_start''': 0.0_0_0_1,
														    '''beta_end''': 0.0_2,
														    '''beta_schedule''': '''linear''',
														    '''variance_type''': '''fixed_small''',
														    '''clip_sample''': True,
														}

														config.update(**lowerCAmelCase_   )
														return config







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Any:


														"""simple docstring"""


														for timesteps in [1, 5, 1_00, 10_00]:
																					self.check_over_configs(num_train_timesteps=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				List[Any]:


														"""simple docstring"""


														for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]				,     [0.0_0_2, 0.0_2, 0.2, 2]   ):
																					self.check_over_configs(beta_start=lowerCAmelCase_				,     beta_end=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				List[Any]:


														"""simple docstring"""


														for schedule in ["linear", "squaredcos_cap_v2"]:
																					self.check_over_configs(beta_schedule=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    int   )     ->				Optional[Any]:


														"""simple docstring"""


														for variance in ["fixed_small", "fixed_large", "other"]:
																					self.check_over_configs(variance_type=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Any   )     ->				List[Any]:


														"""simple docstring"""


														for clip_sample in [True, False]:
																					self.check_over_configs(clip_sample=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				Optional[Any]:


														"""simple docstring"""


														self.check_over_configs(thresholding=lowerCAmelCase_   )
														for threshold in [0.5, 1.0, 2.0]:
																					for prediction_type in ["epsilon", "sample", "v_prediction"]:
																												self.check_over_configs(
																												    thresholding=lowerCAmelCase_				,     prediction_type=lowerCAmelCase_				,     sample_max_value=lowerCAmelCase_				,     )







							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				str:


														"""simple docstring"""


														for prediction_type in ["epsilon", "sample", "v_prediction"]:
																					self.check_over_configs(prediction_type=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    str   )     ->				List[str]:


														"""simple docstring"""


														for t in [0, 5_00, 9_99]:
																					self.check_over_forward(time_step=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    str   )     ->				Optional[int]:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 0.0   )   ) < 1e-5
														assert torch.sum(torch.abs(scheduler._get_variance(4_87   ) - 0.0_0_9_7_9   )   ) < 1e-5
														assert torch.sum(torch.abs(scheduler._get_variance(9_99   ) - 0.0_2   )   ) < 1e-5







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				str:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							len(lowerCAmelCase_   )

														_a      	=							self.dummy_model()
														_a      	=							self.dummy_sample_deter
														_a      	=							self.dummy_sample_deter + 0.1
														_a      	=							self.dummy_sample_deter - 0.1

														_a      	=							samplea.shape[0]
														_a      	=							torch.stack([samplea, samplea, samplea]				,     dim=0   )
														_a      	=							torch.arange(lowerCAmelCase_   )[0:3, None].repeat(1				,     lowerCAmelCase_   )

														_a      	=							model(samples.flatten(0				,     1   )				,     timesteps.flatten(0				,     1   )   )
														_a      	=							scheduler.batch_step_no_noise(lowerCAmelCase_				,     timesteps.flatten(0				,     1   )				,     samples.flatten(0				,     1   )   )

														_a      	=							torch.sum(torch.abs(lowerCAmelCase_   )   )
														_a      	=							torch.mean(torch.abs(lowerCAmelCase_   )   )

														assert abs(result_sum.item() - 1_1_5_3.1_8_3_3   ) < 1e-2
														assert abs(result_mean.item() - 0.5_0_0_5   ) < 1e-3







							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				Dict:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							len(lowerCAmelCase_   )

														_a      	=							self.dummy_model()
														_a      	=							self.dummy_sample_deter
														_a      	=							torch.manual_seed(0   )

														for t in reversed(range(lowerCAmelCase_   )   ):
																					# 1. predict noise residual
																					_a      	=							model(lowerCAmelCase_				,     lowerCAmelCase_   )

																					# 2. predict previous mean of sample x_t-1
																					_a      	=							scheduler.step(lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_				,     generator=lowerCAmelCase_   ).prev_sample

																					_a      	=							pred_prev_sample

														_a      	=							torch.sum(torch.abs(lowerCAmelCase_   )   )
														_a      	=							torch.mean(torch.abs(lowerCAmelCase_   )   )

														assert abs(result_sum.item() - 2_5_8.9_6_0_6   ) < 1e-2
														assert abs(result_mean.item() - 0.3_3_7_2   ) < 1e-3







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config(prediction_type='''v_prediction'''   )
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							len(lowerCAmelCase_   )

														_a      	=							self.dummy_model()
														_a      	=							self.dummy_sample_deter
														_a      	=							torch.manual_seed(0   )

														for t in reversed(range(lowerCAmelCase_   )   ):
																					# 1. predict noise residual
																					_a      	=							model(lowerCAmelCase_				,     lowerCAmelCase_   )

																					# 2. predict previous mean of sample x_t-1
																					_a      	=							scheduler.step(lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_				,     generator=lowerCAmelCase_   ).prev_sample

																					_a      	=							pred_prev_sample

														_a      	=							torch.sum(torch.abs(lowerCAmelCase_   )   )
														_a      	=							torch.mean(torch.abs(lowerCAmelCase_   )   )

														assert abs(result_sum.item() - 2_0_2.0_2_9_6   ) < 1e-2
														assert abs(result_mean.item() - 0.2_6_3_1   ) < 1e-3







							def   __lowerCAmelCase     (    self			:    int   )     ->				Dict:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							[1_00, 87, 50, 1, 0]

														scheduler.set_timesteps(timesteps=lowerCAmelCase_   )

														_a      	=							scheduler.timesteps

														for i, timestep in enumerate(lowerCAmelCase_   ):
																					if i == len(lowerCAmelCase_   ) - 1:
																												_a      	=							-1
																					else:
																												_a      	=							timesteps[i + 1]

																					_a      	=							scheduler.previous_timestep(lowerCAmelCase_   )
																					_a      	=							prev_t.item()

																					self.assertEqual(lowerCAmelCase_				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							[1_00, 87, 50, 51, 0]

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







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							[1_00, 87, 50, 1, 0]
														_a      	=							len(lowerCAmelCase_   )

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







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Any:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							[scheduler.config.num_train_timesteps]

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

