


'''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
#
########################################################################


__lowercase     :						List[Any]	=     16
__lowercase     :						Tuple	=     32






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Accelerator						,				_SCREAMING_SNAKE_CASE				:       DatasetDict						,				_SCREAMING_SNAKE_CASE				:       List[int]						,				_SCREAMING_SNAKE_CASE				:       List[int]						,				_SCREAMING_SNAKE_CASE				:       int = 16       ):
					__a      :	Optional[Any]    =  AutoTokenizer.from_pretrained('bert-base-cased'       )
					__a      :	Union[str, Any]    =  DatasetDict(
					    {
					        'train': dataset['train'].select(UpperCamelCase__       ),
					        'validation': dataset['train'].select(UpperCamelCase__       ),
					        'test': dataset['validation'],
					    }       )

					def tokenize_function(_SCREAMING_SNAKE_CASE				:       Union[str, Any]       ):
										# max_length=None => use the model max length (it's actually the default)
										__a      :	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
					# starting with the main process first:
					with accelerator.main_process_first():
										__a      :	str    =  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      :	int    =  tokenized_datasets.rename_column('label'						,				'labels'       )

					def collate_fn(_SCREAMING_SNAKE_CASE				:       Tuple       ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										__a      :	Any    =  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      :	int    =  16
										elif accelerator.mixed_precision != "no":
															__a      :	List[str]    =  8
										else:
															__a      :	Optional[int]    =  None

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

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

					__a      :	Union[str, Any]    =  DataLoader(
					    tokenized_datasets['test']						,				shuffle=UpperCamelCase__						,				collate_fn=UpperCamelCase__						,				batch_size=UpperCamelCase__       )

					return train_dataloader, eval_dataloader, test_dataloader






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

					__a      :	List[str]    =  evaluate.load('glue'						,				'mrpc'       )

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

					set_seed(UpperCamelCase__       )

					# New Code #
					# Create our folds:
					__a      :	Optional[Any]    =  kfold.split(np.zeros(datasets['train'].num_rows       )						,				datasets['train']['label']       )
					__a      :	str    =  []
					# Iterate over them
					for i, (train_idxs, valid_idxs) in enumerate(UpperCamelCase__       ):
										__a      :	int    =  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      :	Optional[Any]    =  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      :	List[Any]    =  model.to(accelerator.device       )

										# Instantiate optimizer
										__a      :	int    =  AdamW(params=model.parameters()						,				lr=UpperCamelCase__       )

										# Instantiate scheduler
										__a      :	Tuple    =  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      :	Dict    =  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      :	Optional[Any]    =  model(**UpperCamelCase__       )
																				__a      :	List[Any]    =  outputs.loss
																				__a      :	Dict    =  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      :	int    =  model(**UpperCamelCase__       )
																				__a      :	Optional[int]    =  outputs.logits.argmax(dim=-1       )
																				__a      :	Optional[Any]    =  accelerator.gather_for_metrics((predictions, batch['labels'])       )
																				metric.add_batch(
																				    predictions=UpperCamelCase__						,				references=UpperCamelCase__						,				)

															__a      :	Any    =  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      :	int    =  []
										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      :	List[Any]    =  model(**UpperCamelCase__       )
															__a      :	Dict    =  outputs.logits
															__a      :	Optional[int]    =  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      :	Tuple    =  torch.cat(UpperCamelCase__						,				dim=0       )
					__a      :	List[str]    =  torch.stack(UpperCamelCase__						,				dim=0       ).sum(dim=0       ).div(int(args.num_folds       )       ).argmax(dim=-1       )
					__a      :	List[Any]    =  metric.compute(predictions=UpperCamelCase__						,				references=UpperCamelCase__       )
					accelerator.print('Average test metrics from all folds:'						,				UpperCamelCase__       )






def   lowerCamelCase							():
					__a      :	List[str]    =  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      :	Tuple    =  parser.parse_args()
					__a      :	Optional[int]    =  {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(UpperCamelCase__						,				UpperCamelCase__       )


if __name__ == "__main__":
	main()







'''simple docstring'''



from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput





@dataclass
class 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   None
	A_           =   None
	A_           =   None
	A_           =   None





class 							__UpperCamelCase    (		lowerCAmelCase_ ):




	def __init__(       self						,    __a=1						,    __a=0						,    __a=2						,    __a=512						,    __a="cls"						,    __a=False						,    __a=True						,    **__a						,    ):

						'''simple docstring'''





						super().__init__(pad_token_id=__a						,    bos_token_id=__a						,    eos_token_id=__a						,    **__a							)
						__a      :	Any    =  project_dim
						__a      :	Optional[Any]    =  pooler_fn
						__a      :	int    =  learn_encoder
						__a      :	str    =  use_attention_mask





class 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   [r"pooler", r"logit_scale"]
	A_           =   [r"position_ids", r"predictions.decoder.bias"]
	A_           =   "roberta"
	A_           =   RobertaSeriesConfig




	def __init__(       self						,    __a							):

						'''simple docstring'''





						super().__init__(__a							)
						__a      :	Optional[Any]    =  XLMRobertaModel(__a							)
						__a      :	str    =  nn.Linear(config.hidden_size						,    config.project_dim							)
						__a      :	Optional[int]    =  getattr(__a						,    'has_pre_transformation'						,    __a							)
						if self.has_pre_transformation:
											__a      :	int    =  nn.Linear(config.hidden_size						,    config.project_dim							)
											__a      :	List[str]    =  nn.LayerNorm(config.hidden_size						,    eps=config.layer_norm_eps							)
						self.post_init()





	def 				__UpperCAmelCase							(       self						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    ):

						'''simple docstring'''





						__a      :	Optional[Any]    =  return_dict if return_dict is not None else self.config.use_return_dict

						__a      :	Tuple    =  self.base_model(
						    input_ids=__a						,    attention_mask=__a						,    token_type_ids=__a						,    position_ids=__a						,    head_mask=__a						,    inputs_embeds=__a						,    encoder_hidden_states=__a						,    encoder_attention_mask=__a						,    output_attentions=__a						,    output_hidden_states=True if self.has_pre_transformation else output_hidden_states						,    return_dict=__a						,    )

						if self.has_pre_transformation:
											__a      :	Optional[Any]    =  outputs['hidden_states'][-2]
											__a      :	Optional[int]    =  self.pre_LN(__a							)
											__a      :	Union[str, Any]    =  self.transformation_pre(__a							)

											return TransformationModelOutput(
											    projection_state=__a						,    last_hidden_state=outputs.last_hidden_state						,    hidden_states=outputs.hidden_states						,    attentions=outputs.attentions						,    )
						else:
											__a      :	Optional[Any]    =  self.transformation(outputs.last_hidden_state							)
											return TransformationModelOutput(
											    projection_state=__a						,    last_hidden_state=outputs.last_hidden_state						,    hidden_states=outputs.hidden_states						,    attentions=outputs.attentions						,    )



