


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      :    Tuple		=					16
A      :    Tuple		=					32

def       lowercase_		(		_A			:    Accelerator    ,					_A			:    DatasetDict    ,					_A			:    List[int]    ,					_A			:    List[int]    ,					_A			:    int = 16						):

      """simple docstring"""




      lowerCamelCase__		:				Dict    =  AutoTokenizer.from_pretrained("bert-base-cased"						)
      lowerCamelCase__		:				List[str]    =  DatasetDict(
          {
              "train": dataset["train"].select(_A						),
              "validation": dataset["train"].select(_A						),
              "test": dataset["validation"],
          }						)

      def tokenize_function(_A			:    Optional[int]						):
            # max_length=None => use the model max length (it's actually the default)
            lowerCamelCase__		:				str    =  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():
            lowerCamelCase__		:				Tuple    =  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
      lowerCamelCase__		:				Union[str, Any]    =  tokenized_datasets.rename_column("label"    ,					"labels"						)

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

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

      # Instantiate dataloaders.
      lowerCamelCase__		:				Union[str, Any]    =  DataLoader(
          tokenized_datasets["train"]    ,					shuffle=_A    ,					collate_fn=_A    ,					batch_size=_A						)
      lowerCamelCase__		:				Any    =  DataLoader(
          tokenized_datasets["validation"]    ,					shuffle=_A    ,					collate_fn=_A    ,					batch_size=_A						)

      lowerCamelCase__		:				Dict    =  DataLoader(
          tokenized_datasets["test"]    ,					shuffle=_A    ,					collate_fn=_A    ,					batch_size=_A						)

      return train_dataloader, eval_dataloader, test_dataloader

def       lowercase_		(		_A			:    str    ,					_A			:    Union[str, Any]						):

      """simple docstring"""




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

      lowerCamelCase__		:				Optional[int]    =  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__		:				Dict    =  batch_size // MAX_GPU_BATCH_SIZE
            lowerCamelCase__		:				List[Any]    =  MAX_GPU_BATCH_SIZE

      set_seed(_A						)

      # New Code #
      # Create our folds:
      lowerCamelCase__		:				str    =  kfold.split(np.zeros(datasets["train"].num_rows						)    ,					datasets["train"]["label"]						)
      lowerCamelCase__		:				Union[str, Any]    =  []
      # Iterate over them
      for i, (train_idxs, valid_idxs) in enumerate(_A						):
            lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				List[str]    =  get_fold_dataloaders(
                _A    ,					_A    ,					_A    ,					_A    ,					)
            # Instantiate the model (we build the model here so that the seed also control new weights initialization)
            lowerCamelCase__		:				Tuple    =  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).
            lowerCamelCase__		:				Optional[int]    =  model.to(accelerator.device						)

            # Instantiate optimizer
            lowerCamelCase__		:				Any    =  AdamW(params=model.parameters()    ,					lr=_A						)

            # Instantiate scheduler
            lowerCamelCase__		:				List[Any]    =  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.
            lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				List[str]    =  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						)
                        lowerCamelCase__		:				int    =  model(**_A						)
                        lowerCamelCase__		:				Union[str, Any]    =  outputs.loss
                        lowerCamelCase__		:				Any    =  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():
                              lowerCamelCase__		:				List[Any]    =  model(**_A						)
                        lowerCamelCase__		:				Tuple    =  outputs.logits.argmax(dim=-1						)
                        lowerCamelCase__    ,   lowerCamelCase__		:				Union[str, Any]    =  accelerator.gather_for_metrics((predictions, batch["labels"])						)
                        metric.add_batch(
                            predictions=_A    ,					references=_A    ,					)

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

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				List[Any]    =  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"						)
      lowerCamelCase__		:				Optional[Any]    =  parser.parse_args()
      lowerCamelCase__		:				Optional[int]    =  {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
      training_function(_A    ,					_A						)


if __name__ == "__main__":
    main()






from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


A      :    Optional[int]		=					[
    "Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"
    " final seconds on board Flight 9525. The Germanwings co-pilot says he had a \"previous episode of severe"
    " depression\" German airline confirms it knew of Andreas Lubitz's depression years before he took control.",
    "The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal"
    " accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC's"
    " founding Rome Statute in January. Israel and the United States opposed the Palestinians' efforts to join the"
    " body.",
    "Amnesty International releases its annual report on the death penalty. The report catalogs the use of"
    " state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the"
    " world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital"
    " punishment.",
]

A      :    List[Any]		=					[
    "Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports ."
    " Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz"
    " had informed his Lufthansa training school of an episode of severe depression, airline says .",
    "Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June ."
    " Israel and the United States opposed the move, which could open the door to war crimes investigations against"
    " Israelis .",
    "Amnesty's annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to"
    " death . Organization claims that governments around the world are using the threat of terrorism to advance"
    " executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death"
    " sentences up by 28% .",
]

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Dict    =  calculate_rouge(_A    ,					_A    ,					bootstrap_aggregation=_A    ,					rouge_keys=["rouge2", "rougeL"]						)
      assert isinstance(_A    ,					_A						)
      lowerCamelCase__		:				List[Any]    =  calculate_rouge(_A    ,					_A    ,					bootstrap_aggregation=_A    ,					rouge_keys=["rouge2"]						)
      assert (
          pd.DataFrame(no_aggregation["rouge2"]						).fmeasure.mean()
          == pd.DataFrame(no_aggregation_just_ra["rouge2"]						).fmeasure.mean()
      )

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Any    =  "rougeLsum"
      lowerCamelCase__		:				List[str]    =  calculate_rouge(_A    ,					_A    ,					newline_sep=_A    ,					rouge_keys=[k]						)[k]
      lowerCamelCase__		:				str    =  calculate_rouge(_A    ,					_A    ,					newline_sep=_A    ,					rouge_keys=[k]						)[k]
      assert score > score_no_sep

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				int    =  ["rouge1", "rouge2", "rougeL"]
      lowerCamelCase__		:				Union[str, Any]    =  calculate_rouge(_A    ,					_A    ,					newline_sep=_A    ,					rouge_keys=_A						)
      lowerCamelCase__		:				Any    =  calculate_rouge(_A    ,					_A    ,					newline_sep=_A    ,					rouge_keys=_A						)
      assert score_sep == score_no_sep

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Optional[Any]    =  [
          "Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.",
          "Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .",
      ]
      lowerCamelCase__		:				Tuple    =  [
          "Margot Frank, died in 1945, a month earlier than previously thought.",
          "Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"
          " the final seconds on board Flight 9525.",
      ]
      assert calculate_rouge(_A    ,					_A    ,					newline_sep=_A						) == calculate_rouge(_A    ,					_A    ,					newline_sep=_A						)

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				List[str]    =  [
          "\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" "
      ]
      lowerCamelCase__		:				str    =  [
          " Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."
      ]

      lowerCamelCase__		:				Union[str, Any]    =  calculate_rouge(_A    ,					_A    ,					rouge_keys=["rougeLsum"]    ,					newline_sep=_A						)["rougeLsum"]
      lowerCamelCase__		:				List[str]    =  calculate_rouge(_A    ,					_A    ,					rouge_keys=["rougeLsum"]						)["rougeLsum"]
      assert new_score > prev_score


def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Tuple    =  Path("examples/seq2seq/test_data/wmt_en_ro"						)
      lowerCamelCase__		:				Any    =  calculate_rouge_path(data_dir.joinpath("test.source"						)    ,					data_dir.joinpath("test.target"						)						)
      assert isinstance(_A    ,					_A						)
      lowerCamelCase__		:				str    =  calculate_rouge_path(
          data_dir.joinpath("test.source"						)    ,					data_dir.joinpath("test.target"						)    ,					bootstrap_aggregation=_A						)
      assert isinstance(_A    ,					_A						)


