





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_		:				Union[str, Any]				=						16
lowercase_		:				Dict				=						32

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase = 16 ):
				_snake_case :			List[Any]  						=   AutoTokenizer.from_pretrained('bert-base-cased' )
				_snake_case :			str  						=   DatasetDict(
				    {
				        'train': dataset['train'].select(__lowerCAmelCase ),
				        'validation': dataset['train'].select(__lowerCAmelCase ),
				        'test': dataset['validation'],
				    } )

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

				def collate_fn(__lowerCAmelCase ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								_snake_case :			str  						=   1_28 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 :			Optional[Any]  						=   16
								elif accelerator.mixed_precision != "no":
												_snake_case :			List[str]  						=   8
								else:
												_snake_case :			List[Any]  						=   None

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

				# Instantiate dataloaders.
				_snake_case :			int  						=   DataLoader(
				    tokenized_datasets['train']	,			shuffle=__lowerCAmelCase	,			collate_fn=__lowerCAmelCase	,			batch_size=__lowerCAmelCase )
				_snake_case :			Dict  						=   DataLoader(
				    tokenized_datasets['validation']	,			shuffle=__lowerCAmelCase	,			collate_fn=__lowerCAmelCase	,			batch_size=__lowerCAmelCase )

				_snake_case :			Optional[int]  						=   DataLoader(
				    tokenized_datasets['test']	,			shuffle=__lowerCAmelCase	,			collate_fn=__lowerCAmelCase	,			batch_size=__lowerCAmelCase )

				return train_dataloader, eval_dataloader, test_dataloader

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				# New Code #
				_snake_case :			Union[str, Any]  						=   []
				# Download the dataset
				_snake_case :			List[str]  						=   load_dataset('glue'	,			'mrpc' )
				# Create our splits
				_snake_case :			str  						=   StratifiedKFold(n_splits=int(args.num_folds ) )
				# Initialize accelerator
				_snake_case :			Optional[int]  						=   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 :			Any  						=   config['lr']
				_snake_case :			List[str]  						=   int(config['num_epochs'] )
				_snake_case :			Union[str, Any]  						=   int(config['seed'] )
				_snake_case :			List[str]  						=   int(config['batch_size'] )

				_snake_case :			int  						=   evaluate.load('glue'	,			'mrpc' )

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

				set_seed(__lowerCAmelCase )

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

								# 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 :			int  						=   model.to(accelerator.device )

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

								# Instantiate scheduler
								_snake_case :			Dict  						=   get_linear_schedule_with_warmup(
								    optimizer=__lowerCAmelCase	,			num_warmup_steps=1_00	,			num_training_steps=(len(__lowerCAmelCase ) * 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						,			_snake_case						,			_snake_case						,			_snake_case						,			_snake_case :			List[str]  						=   accelerator.prepare(
								    __lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase )

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

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

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

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

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


if __name__ == "__main__":
		main()













import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


lowercase_		:				Tuple				=						logging.getLogger(__name__)


class 			lowercase				(							a_     ):



						"""simple docstring"""

						def __init__(    self			:		Dict ,				lowerCamelCase_			:		Dict=-1					):





										'''simple docstring'''







										_snake_case :			str  						=   label_idx







						def 		__UpperCAmelCase       (    self			:		Any ,				lowerCamelCase_			:		Optional[int] ,				lowerCamelCase_			:		Union[Split, str]					):





										'''simple docstring'''







										if isinstance(lowerCamelCase_ ,				lowerCamelCase_					):
														_snake_case :			Union[str, Any]  						=   mode.value
										_snake_case :			Optional[int]  						=   os.path.join(lowerCamelCase_ ,				f'''{mode}.txt'''					)
										_snake_case :			Dict  						=   1
										_snake_case :			List[str]  						=   []
										with open(lowerCamelCase_ ,				encoding='utf-8'					) as f:
														_snake_case :			List[Any]  						=   []
														_snake_case :			int  						=   []
														for line in f:
																		if line.startswith('-DOCSTART-'					) or line == "" or line == "\n":
																						if words:
																										examples.append(InputExample(guid=f'''{mode}-{guid_index}''' ,				words=lowerCamelCase_ ,				labels=lowerCamelCase_					)					)
																										guid_index += 1
																										_snake_case :			Optional[Any]  						=   []
																										_snake_case :			Union[str, Any]  						=   []
																		else:
																						_snake_case :			Tuple  						=   line.split(' '					)
																						words.append(splits[0]					)
																						if len(lowerCamelCase_					) > 1:
																										labels.append(splits[self.label_idx].replace('\n' ,				''					)					)
																						else:
																										# Examples could have no label for mode = "test"
																										labels.append('O'					)
														if words:
																		examples.append(InputExample(guid=f'''{mode}-{guid_index}''' ,				words=lowerCamelCase_ ,				labels=lowerCamelCase_					)					)
										return examples







						def 		__UpperCAmelCase       (    self			:		Union[str, Any] ,				lowerCamelCase_			:		TextIO ,				lowerCamelCase_			:		TextIO ,				lowerCamelCase_			:		List					):





										'''simple docstring'''







										_snake_case :			str  						=   0
										for line in test_input_reader:
														if line.startswith('-DOCSTART-'					) or line == "" or line == "\n":
																		writer.write(lowerCamelCase_					)
																		if not preds_list[example_id]:
																						example_id += 1
														elif preds_list[example_id]:
																		_snake_case :			List[str]  						=   line.split()[0] + ' ' + preds_list[example_id].pop(0					) + '\n'
																		writer.write(lowerCamelCase_					)
														else:
																		logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.' ,				line.split()[0]					)







						def 		__UpperCAmelCase       (    self			:		List[str] ,				lowerCamelCase_			:		str					):





										'''simple docstring'''







										if path:
														with open(lowerCamelCase_ ,				'r'					) as f:
																		_snake_case :			Optional[int]  						=   f.read().splitlines()
														if "O" not in labels:
																		_snake_case :			Optional[int]  						=   ['O'] + labels
														return labels
										else:
														return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]



class 			lowercase				(							a_     ):



						"""simple docstring"""

						def __init__(    self			:		Optional[int]					):





										'''simple docstring'''







										super().__init__(label_idx=-2					)







						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		str					):





										'''simple docstring'''







										if path:
														with open(lowerCamelCase_ ,				'r'					) as f:
																		_snake_case :			str  						=   f.read().splitlines()
														if "O" not in labels:
																		_snake_case :			Union[str, Any]  						=   ['O'] + labels
														return labels
										else:
														return [
														    "O",
														    "B-ADVP",
														    "B-INTJ",
														    "B-LST",
														    "B-PRT",
														    "B-NP",
														    "B-SBAR",
														    "B-VP",
														    "B-ADJP",
														    "B-CONJP",
														    "B-PP",
														    "I-ADVP",
														    "I-INTJ",
														    "I-LST",
														    "I-PRT",
														    "I-NP",
														    "I-SBAR",
														    "I-VP",
														    "I-ADJP",
														    "I-CONJP",
														    "I-PP",
														]



class 			lowercase				(							a_     ):



						"""simple docstring"""

						def 		__UpperCAmelCase       (    self			:		Optional[Any] ,				lowerCamelCase_			:		Union[str, Any] ,				lowerCamelCase_			:		Union[Split, str]					):





										'''simple docstring'''







										if isinstance(lowerCamelCase_ ,				lowerCamelCase_					):
														_snake_case :			str  						=   mode.value
										_snake_case :			List[str]  						=   os.path.join(lowerCamelCase_ ,				f'''{mode}.txt'''					)
										_snake_case :			Tuple  						=   1
										_snake_case :			List[str]  						=   []

										with open(lowerCamelCase_ ,				encoding='utf-8'					) as f:
														for sentence in parse_incr(lowerCamelCase_					):
																		_snake_case :			List[str]  						=   []
																		_snake_case :			str  						=   []
																		for token in sentence:
																						words.append(token['form']					)
																						labels.append(token['upos']					)
																		assert len(lowerCamelCase_					) == len(lowerCamelCase_					)
																		if words:
																						examples.append(InputExample(guid=f'''{mode}-{guid_index}''' ,				words=lowerCamelCase_ ,				labels=lowerCamelCase_					)					)
																						guid_index += 1
										return examples







						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		TextIO ,				lowerCamelCase_			:		TextIO ,				lowerCamelCase_			:		List					):





										'''simple docstring'''







										_snake_case :			Dict  						=   0
										for sentence in parse_incr(lowerCamelCase_					):
														_snake_case :			Optional[int]  						=   preds_list[example_id]
														_snake_case :			List[Any]  						=   ''
														for token in sentence:
																		out += f'''{token['form']} ({token['upos']}|{s_p.pop(0					)}) '''
														out += "\n"
														writer.write(lowerCamelCase_					)
														example_id += 1







						def 		__UpperCAmelCase       (    self			:		Optional[Any] ,				lowerCamelCase_			:		str					):





										'''simple docstring'''







										if path:
														with open(lowerCamelCase_ ,				'r'					) as f:
																		return f.read().splitlines()
										else:
														return [
														    "ADJ",
														    "ADP",
														    "ADV",
														    "AUX",
														    "CCONJ",
														    "DET",
														    "INTJ",
														    "NOUN",
														    "NUM",
														    "PART",
														    "PRON",
														    "PROPN",
														    "PUNCT",
														    "SCONJ",
														    "SYM",
														    "VERB",
														    "X",
														]






