




"""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
#
########################################################################


UpperCamelCase__						=			1_6
UpperCamelCase__						=			3_2





def 							UpperCAmelCase		(		snake_case			:					Accelerator      ,       snake_case			:					DatasetDict      ,       snake_case			:					List[int]      ,       snake_case			:					List[int]      ,       snake_case			:					int = 16    ):
					_lowerCAmelCase:Dict									=  AutoTokenizer.from_pretrained('''bert-base-cased'''    )
					_lowerCAmelCase:Dict									=  DatasetDict(
					    {
					        '''train''': dataset['''train'''].select(snake_case    ),
					        '''validation''': dataset['''train'''].select(snake_case    ),
					        '''test''': dataset['''validation'''],
					    }    )

					def tokenize_function(snake_case			:					str    ):
										# max_length=None => use the model max length (it's actually the default)
										_lowerCAmelCase:List[str]									=  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
					# starting with the main process first:
					with accelerator.main_process_first():
										_lowerCAmelCase:str									=  datasets.map(
										    snake_case      ,       batched=snake_case      ,       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
					_lowerCAmelCase: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.
										_lowerCAmelCase:List[str]									=  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":
															_lowerCAmelCase:List[Any]									=  16
										elif accelerator.mixed_precision != "no":
															_lowerCAmelCase:Tuple									=  8
										else:
															_lowerCAmelCase:int									=  None

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

					# Instantiate dataloaders.
					_lowerCAmelCase:str									=  DataLoader(
					    tokenized_datasets['''train''']      ,       shuffle=snake_case      ,       collate_fn=snake_case      ,       batch_size=snake_case    )
					_lowerCAmelCase:Tuple									=  DataLoader(
					    tokenized_datasets['''validation''']      ,       shuffle=snake_case      ,       collate_fn=snake_case      ,       batch_size=snake_case    )

					_lowerCAmelCase:List[Any]									=  DataLoader(
					    tokenized_datasets['''test''']      ,       shuffle=snake_case      ,       collate_fn=snake_case      ,       batch_size=snake_case    )

					return train_dataloader, eval_dataloader, test_dataloader





def 							UpperCAmelCase		(		snake_case			:					Union[str, Any]      ,       snake_case			:					Optional[int]    ):
					# New Code #
					_lowerCAmelCase:Dict									=  []
					# Download the dataset
					_lowerCAmelCase:Dict									=  load_dataset('''glue'''      ,       '''mrpc'''    )
					# Create our splits
					_lowerCAmelCase:Optional[Any]									=  StratifiedKFold(n_splits=int(args.num_folds    )    )
					# Initialize accelerator
					_lowerCAmelCase:str									=  Accelerator(cpu=args.cpu      ,       mixed_precision=args.mixed_precision    )
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					_lowerCAmelCase:Optional[int]									=  config['''lr''']
					_lowerCAmelCase:List[str]									=  int(config['''num_epochs''']    )
					_lowerCAmelCase:Optional[int]									=  int(config['''seed''']    )
					_lowerCAmelCase:List[Any]									=  int(config['''batch_size''']    )

					_lowerCAmelCase:Dict									=  evaluate.load('''glue'''      ,       '''mrpc'''    )

					# If the batch size is too big we use gradient accumulation
					_lowerCAmelCase:Union[str, Any]									=  1
					if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
										_lowerCAmelCase:Union[str, Any]									=  batch_size // MAX_GPU_BATCH_SIZE
										_lowerCAmelCase:List[str]									=  MAX_GPU_BATCH_SIZE

					set_seed(snake_case    )

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

										# 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).
										_lowerCAmelCase:Optional[Any]									=  model.to(accelerator.device    )

										# Instantiate optimizer
										_lowerCAmelCase:Union[str, Any]									=  AdamW(params=model.parameters()      ,       lr=snake_case    )

										# Instantiate scheduler
										_lowerCAmelCase:List[str]									=  get_linear_schedule_with_warmup(
										    optimizer=snake_case      ,       num_warmup_steps=100      ,       num_training_steps=(len(snake_case    ) * 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.
										_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase:List[str]									=  accelerator.prepare(
										    snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case    )

										# Now we train the model
										for epoch in range(snake_case    ):
															model.train()
															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    )
																				_lowerCAmelCase:Any									=  model(**snake_case    )
																				_lowerCAmelCase:str									=  outputs.loss
																				_lowerCAmelCase:Tuple									=  loss / gradient_accumulation_steps
																				accelerator.backward(snake_case    )
																				if step % gradient_accumulation_steps == 0:
																									optimizer.step()
																									lr_scheduler.step()
																									optimizer.zero_grad()

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

															_lowerCAmelCase:Optional[Any]									=  metric.compute()
															# Use accelerator.print to print only on the main process.
															accelerator.print(F'epoch {epoch}:'      ,       snake_case    )

										# New Code #
										# We also run predictions on the test set at the very end
										_lowerCAmelCase:Any									=  []
										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():
																				_lowerCAmelCase:Any									=  model(**snake_case    )
															_lowerCAmelCase:Optional[Any]									=  outputs.logits
															_lowerCAmelCase							,   _lowerCAmelCase:Optional[Any]									=  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(snake_case      ,       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:
					_lowerCAmelCase:str									=  torch.cat(snake_case      ,       dim=0    )
					_lowerCAmelCase:Dict									=  torch.stack(snake_case      ,       dim=0    ).sum(dim=0    ).div(int(args.num_folds    )    ).argmax(dim=-1    )
					_lowerCAmelCase:Tuple									=  metric.compute(predictions=snake_case      ,       references=snake_case    )
					accelerator.print('''Average test metrics from all folds:'''      ,       snake_case    )





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:Tuple									=  argparse.ArgumentParser(description='''Simple example of training script.'''    )
					parser.add_argument(
					    '''--mixed_precision'''      ,       type=snake_case      ,       default=snake_case      ,       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=snake_case      ,       default=3      ,       help='''The number of splits to perform across the dataset'''    )
					_lowerCAmelCase:Optional[Any]									=  parser.parse_args()
					_lowerCAmelCase:Dict									=  {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(snake_case      ,       snake_case    )


if __name__ == "__main__":
				main()












"""simple docstring"""






# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class   a__			(       UpperCamelCase_  ):
						snake_case__             = 42
						snake_case__             = None





def 							UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					int=0.9_99      ,       snake_case			:					Union[str, Any]="cosine"      ,       ):
					if alpha_transform_type == "cosine":

										def alpha_bar_fn(snake_case			:					Dict    ):
															return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2    ) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(snake_case			:					Optional[Any]    ):
															return math.exp(t * -12.0    )

					else:
										raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'    )

					_lowerCAmelCase:List[str]									=  []
					for i in range(snake_case    ):
										_lowerCAmelCase:Union[str, Any]									=  i / num_diffusion_timesteps
										_lowerCAmelCase:Dict									=  (i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(snake_case    ) / alpha_bar_fn(snake_case    )      ,       snake_case    )    )
					return torch.tensor(snake_case      ,       dtype=torch.floataa    )





class   a__			(       UpperCamelCase_					,	UpperCamelCase_  ):
						snake_case__             = 1





						@register_to_config
						def __init__(							self      :	Optional[Any]     ,a__      :	int = 1000     ,a__      :	float = 0.0001     ,a__      :	float = 0.02     ,a__      :	str = "linear"     ,a__      :	Optional[Union[np.ndarray, List[float]]] = None     ,a__      :	bool = True     ,a__      :	bool = True     ,a__      :	int = 0     ,a__      :	str = "epsilon"     ,a__      :	float = 1.0     ,**a__      :	Any     ,)						->       str:




											"""simple docstring"""







											if kwargs.get('''set_alpha_to_one'''     ,a__) is not None:
																_lowerCAmelCase:Tuple									=  (
																    '''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
																)
																deprecate('''set_alpha_to_one'''     ,'''1.0.0'''     ,a__     ,standard_warn=a__)
																_lowerCAmelCase:Optional[Any]									=  kwargs['''set_alpha_to_one''']
											if trained_betas is not None:
																_lowerCAmelCase:Any									=  torch.tensor(a__     ,dtype=torch.floataa)
											elif beta_schedule == "linear":
																_lowerCAmelCase:str									=  torch.linspace(a__     ,a__     ,a__     ,dtype=torch.floataa)
											elif beta_schedule == "scaled_linear":
																# this schedule is very specific to the latent diffusion model.
																_lowerCAmelCase:int									=  (
																    torch.linspace(beta_start**0.5     ,beta_end**0.5     ,a__     ,dtype=torch.floataa) ** 2
																)
											elif beta_schedule == "squaredcos_cap_v2":
																# Glide cosine schedule
																_lowerCAmelCase:Optional[int]									=  betas_for_alpha_bar(a__)
											else:
																raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}')

											_lowerCAmelCase:Any									=  1.0 - self.betas
											_lowerCAmelCase:Optional[Any]									=  torch.cumprod(self.alphas     ,dim=0)

											# At every step in inverted ddim, we are looking into the next alphas_cumprod
											# For the final step, there is no next alphas_cumprod, and the index is out of bounds
											# `set_alpha_to_zero` decides whether we set this parameter simply to zero
											# in this case, self.step() just output the predicted noise
											# or whether we use the final alpha of the "non-previous" one.
											_lowerCAmelCase:Optional[Any]									=  torch.tensor(0.0) if set_alpha_to_zero else self.alphas_cumprod[-1]

											# standard deviation of the initial noise distribution
											_lowerCAmelCase:Any									=  1.0

											# setable values
											_lowerCAmelCase:Dict									=  None
											_lowerCAmelCase:List[Any]									=  torch.from_numpy(np.arange(0     ,a__).copy().astype(np.intaa))





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,a__      :	torch.FloatTensor     ,a__      :	Optional[int] = None)						->       torch.FloatTensor:




											"""simple docstring"""







											return sample





						def       __UpperCamelCase     (							self      :	Optional[int]     ,a__      :	int     ,a__      :	Union[str, torch.device] = None)						->       List[Any]:




											"""simple docstring"""







											if num_inference_steps > self.config.num_train_timesteps:
																raise ValueError(
																    F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
																    F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
																    F' maximal {self.config.num_train_timesteps} timesteps.')

											_lowerCAmelCase:Union[str, Any]									=  num_inference_steps
											_lowerCAmelCase:Union[str, Any]									=  self.config.num_train_timesteps // self.num_inference_steps
											# creates integer timesteps by multiplying by ratio
											# casting to int to avoid issues when num_inference_step is power of 3
											_lowerCAmelCase:str									=  (np.arange(0     ,a__) * step_ratio).round().copy().astype(np.intaa)
											_lowerCAmelCase:str									=  torch.from_numpy(a__).to(a__)
											self.timesteps += self.config.steps_offset





						def       __UpperCamelCase     (							self      :	int     ,a__      :	torch.FloatTensor     ,a__      :	int     ,a__      :	torch.FloatTensor     ,a__      :	float = 0.0     ,a__      :	bool = False     ,a__      :	Optional[torch.FloatTensor] = None     ,a__      :	bool = True     ,)						->       Union[DDIMSchedulerOutput, Tuple]:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  timestep + self.config.num_train_timesteps // self.num_inference_steps

											# 2. compute alphas, betas
											# change original implementation to exactly match noise levels for analogous forward process
											_lowerCAmelCase:Optional[Any]									=  self.alphas_cumprod[timestep]
											_lowerCAmelCase:List[Any]									=  (
											    self.alphas_cumprod[prev_timestep]
											    if prev_timestep < self.config.num_train_timesteps
											    else self.final_alpha_cumprod
											)

											_lowerCAmelCase:str									=  1 - alpha_prod_t

											# 3. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											if self.config.prediction_type == "epsilon":
																_lowerCAmelCase:int									=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																_lowerCAmelCase:str									=  model_output
											elif self.config.prediction_type == "sample":
																_lowerCAmelCase:List[str]									=  model_output
																_lowerCAmelCase:Optional[int]									=  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
											elif self.config.prediction_type == "v_prediction":
																_lowerCAmelCase:str									=  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																_lowerCAmelCase:Dict									=  (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
											else:
																raise ValueError(
																    F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
																    ''' `v_prediction`''')

											# 4. Clip or threshold "predicted x_0"
											if self.config.clip_sample:
																_lowerCAmelCase:List[Any]									=  pred_original_sample.clamp(
																    -self.config.clip_sample_range     ,self.config.clip_sample_range)

											# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											_lowerCAmelCase:str									=  (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

											# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											_lowerCAmelCase:Optional[Any]									=  alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

											if not return_dict:
																return (prev_sample, pred_original_sample)
											return DDIMSchedulerOutput(prev_sample=a__     ,pred_original_sample=a__)







						def __len__(							self      :	Dict)						->       Union[str, Any]:




											"""simple docstring"""







											return self.config.num_train_timesteps






