



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
#
########################################################################


a__      :				List[str]   	=			16
a__      :				Dict   	=			32

def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__						,  A__ = 16	):
      lowercase__     =  AutoTokenizer.from_pretrained('bert-base-cased'	)
      lowercase__     =  DatasetDict(
          {
              'train': dataset['train'].select(A__	),
              'validation': dataset['train'].select(A__	),
              'test': dataset['validation'],
          }	)

      def tokenize_function(A__	):
            # max_length=None => use the model max length (it's actually the default)
            lowercase__     =  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():
            lowercase__     =  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
      lowercase__     =  tokenized_datasets.rename_column('label'						,  'labels'	)

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

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

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

      lowercase__     =  DataLoader(
          tokenized_datasets['test']						,  shuffle=A__						,  collate_fn=A__						,  batch_size=A__	)

      return train_dataloader, eval_dataloader, test_dataloader

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

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

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

      set_seed(A__	)

      # New Code #
      # Create our folds:
      lowercase__     =  kfold.split(np.zeros(datasets['train'].num_rows	)						,  datasets['train']['label']	)
      lowercase__     =  []
      # Iterate over them
      for i, (train_idxs, valid_idxs) in enumerate(A__	):
            lowercase__,	lowercase__,	lowercase__     =  get_fold_dataloaders(
                A__						,  A__						,  A__						,  A__						,  )
            # Instantiate the model (we build the model here so that the seed also control new weights initialization)
            lowercase__     =  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).
            lowercase__     =  model.to(accelerator.device	)

            # Instantiate optimizer
            lowercase__     =  AdamW(params=model.parameters()						,  lr=A__	)

            # Instantiate scheduler
            lowercase__     =  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.
            lowercase__,	lowercase__,	lowercase__,	lowercase__,	lowercase__     =  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	)
                        lowercase__     =  model(**A__	)
                        lowercase__     =  outputs.loss
                        lowercase__     =  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():
                              lowercase__     =  model(**A__	)
                        lowercase__     =  outputs.logits.argmax(dim=-1	)
                        lowercase__,	lowercase__     =  accelerator.gather_for_metrics((predictions, batch['labels'])	)
                        metric.add_batch(
                            predictions=A__						,  references=A__						,  )

                  lowercase__     =  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
            lowercase__     =  []
            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():
                        lowercase__     =  model(**A__	)
                  lowercase__     =  outputs.logits
                  lowercase__,	lowercase__     =  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:
      lowercase__     =  torch.cat(A__						,  dim=0	)
      lowercase__     =  torch.stack(A__						,  dim=0	).sum(dim=0	).div(int(args.num_folds	)	).argmax(dim=-1	)
      lowercase__     =  metric.compute(predictions=A__						,  references=A__	)
      accelerator.print('Average test metrics from all folds:'						,  A__	)

def   _lowerCAmelCase   (						):
      lowercase__     =  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'	)
      lowercase__     =  parser.parse_args()
      lowercase__     =  {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
      training_function(A__						,  A__	)


if __name__ == "__main__":
       main()





from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


a__      :				int   	=			"\\n@misc{wu2016googles,\n      title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n              and Jeffrey Dean},\n      year={2016},\n      eprint={1609.08144},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n"

a__      :				Optional[Any]   	=			"\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe 'GLEU score'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore's range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n"

a__      :				Tuple   	=			"\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n    predictions (list of str): list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references (list of list of str): list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n    'google_bleu': google_bleu score\n\nExamples:\n    Example 1:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.44\n\n    Example 2:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.61\n\n    Example 3:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.53\n\n    Example 4:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.4\n"



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,							_KWARGS_DESCRIPTION	)
class       UpperCAmelCase__(							datasets.Metric	):

   '''simple docstring'''




   def 			UpperCAmelCase		(						self  :			List[Any])	->				MetricInfo:




         """simple docstring"""

         return datasets.MetricInfo(
             description=_DESCRIPTION			,				citation=_CITATION			,				inputs_description=_KWARGS_DESCRIPTION			,				features=datasets.Features(
                 {
                     'predictions': datasets.Sequence(datasets.Value('string'			,				id='token')			,				id='sequence'),
                     'references': datasets.Sequence(
                         datasets.Sequence(datasets.Value('string'			,				id='token')			,				id='sequence')			,				id='references'),
                 })			,				)



   def 			UpperCAmelCase		(						self  :			int			,				lowerCAmelCase  :			List[List[List[str]]]			,				lowerCAmelCase  :			List[List[str]]			,				lowerCAmelCase  :			int = 1			,				lowerCAmelCase  :			int = 4			,				)	->				Dict[str, float]:




         """simple docstring"""

         return {
             "google_bleu": gleu_score.corpus_gleu(
                 list_of_references=lowerCAmelCase			,				hypotheses=lowerCAmelCase			,				min_len=lowerCAmelCase			,				max_len=lowerCAmelCase)
         }
