

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		:   Tuple										=       16
_UpperCAmelCase		:   Tuple										=       32





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__ = 16      ):







							'''simple docstring'''






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

							def tokenize_function(UpperCamelCase__      ):
														# max_length=None => use the model max length (it's actually the default)
														snake_case_							=    tokenizer(examples['sentence1']   ,							examples['sentence2']   ,							truncation=_A   ,							max_length=_A      )
														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():
														snake_case_							=    datasets.map(
														    _A   ,							batched=_A   ,							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
							snake_case_							=    tokenized_datasets.rename_column('label'   ,							'labels'      )

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

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

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

							snake_case_							=    DataLoader(
							    tokenized_datasets['test']   ,							shuffle=_A   ,							collate_fn=_A   ,							batch_size=_A      )

							return train_dataloader, eval_dataloader, test_dataloader





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






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

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

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

							set_seed(_A      )

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

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

														# Instantiate optimizer
														snake_case_							=    AdamW(params=model.parameters()   ,							lr=_A      )

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

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

																					model.eval()
																					for step, batch in enumerate(_A      ):
																												# We could avoid this line since we set the accelerator with `device_placement=True`.
																												batch.to(accelerator.device      )
																												with torch.no_grad():
																																			snake_case_							=    model(**_A      )
																												snake_case_							=    outputs.logits.argmax(dim=-1      )
																												snake_case_							=    accelerator.gather_for_metrics((predictions, batch['labels'])      )
																												metric.add_batch(
																												    predictions=_A   ,							references=_A   ,							)

																					snake_case_							=    metric.compute()
																					# Use accelerator.print to print only on the main process.
																					accelerator.print(F'''epoch {epoch}:'''   ,							_A      )

														# New Code #
														# We also run predictions on the test set at the very end
														snake_case_							=    []
														for step, batch in enumerate(_A      ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device      )
																					with torch.no_grad():
																												snake_case_							=    model(**_A      )
																					snake_case_							=    outputs.logits
																					snake_case_							=    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(_A   ,							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:
							snake_case_							=    torch.cat(_A   ,							dim=0      )
							snake_case_							=    torch.stack(_A   ,							dim=0      ).sum(dim=0      ).div(int(args.num_folds      )      ).argmax(dim=-1      )
							snake_case_							=    metric.compute(predictions=_A   ,							references=_A      )
							accelerator.print('Average test metrics from all folds:'   ,							_A      )





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							snake_case_							=    argparse.ArgumentParser(description='Simple example of training script.'      )
							parser.add_argument(
							    '--mixed_precision'   ,							type=_A   ,							default=_A   ,							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=_A   ,							default=3   ,							help='The number of splits to perform across the dataset'      )
							snake_case_							=    parser.parse_args()
							snake_case_							=    {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
							training_function(_A   ,							_A      )


if __name__ == "__main__":
					main()








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A      :    Union[str, Any]		=					logging.get_logger(__name__)

A      :    Optional[Any]		=					{
    "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/config.json",
    "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/config.json",
    "xlm-roberta-large-finetuned-conll02-dutch": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll02-spanish": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-english": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-german": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"
    ),
}







class 			_lowercase (							lowercase__):


       """simple docstring"""




       A__											=			"xlm-roberta"





       def __init__(   self							:			Union[str, Any]					,      __lowerCamelCase							:			Optional[Any]=30522					,      __lowerCamelCase							:			List[Any]=768					,      __lowerCamelCase							:			Union[str, Any]=12					,      __lowerCamelCase							:			int=12					,      __lowerCamelCase							:			Dict=3072					,      __lowerCamelCase							:			Optional[int]="gelu"					,      __lowerCamelCase							:			Optional[Any]=0.1					,      __lowerCamelCase							:			Tuple=0.1					,      __lowerCamelCase							:			str=512					,      __lowerCamelCase							:			Optional[Any]=2					,      __lowerCamelCase							:			Dict=0.0_2					,      __lowerCamelCase							:			List[str]=1E-1_2					,      __lowerCamelCase							:			Optional[int]=1					,      __lowerCamelCase							:			Union[str, Any]=0					,      __lowerCamelCase							:			Optional[int]=2					,      __lowerCamelCase							:			Dict="absolute"					,      __lowerCamelCase							:			Optional[Any]=True					,      __lowerCamelCase							:			Tuple=None					,      **__lowerCamelCase							:			int					,      ):


             '''simple docstring'''



             super().__init__(pad_token_id=__lowerCamelCase					,      bos_token_id=__lowerCamelCase					,      eos_token_id=__lowerCamelCase					,      **__lowerCamelCase       )

             lowerCamelCase__		:				str    =  vocab_size
             lowerCamelCase__		:				Any    =  hidden_size
             lowerCamelCase__		:				Optional[Any]    =  num_hidden_layers
             lowerCamelCase__		:				Union[str, Any]    =  num_attention_heads
             lowerCamelCase__		:				Any    =  hidden_act
             lowerCamelCase__		:				str    =  intermediate_size
             lowerCamelCase__		:				Optional[int]    =  hidden_dropout_prob
             lowerCamelCase__		:				Any    =  attention_probs_dropout_prob
             lowerCamelCase__		:				Union[str, Any]    =  max_position_embeddings
             lowerCamelCase__		:				List[str]    =  type_vocab_size
             lowerCamelCase__		:				Tuple    =  initializer_range
             lowerCamelCase__		:				Tuple    =  layer_norm_eps
             lowerCamelCase__		:				Any    =  position_embedding_type
             lowerCamelCase__		:				Any    =  use_cache
             lowerCamelCase__		:				Any    =  classifier_dropout







class 			_lowercase (							lowercase__):


       """simple docstring"""




       @property
       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             if self.task == "multiple-choice":
                   lowerCamelCase__		:				List[str]    =  {0: "batch", 1: "choice", 2: "sequence"}
             else:
                   lowerCamelCase__		:				Any    =  {0: "batch", 1: "sequence"}
             return OrderedDict(
                 [
                     ("input_ids", dynamic_axis),
                     ("attention_mask", dynamic_axis),
                 ]       )


