




"""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
#
########################################################################


SCREAMING_SNAKE_CASE_	     =       16
SCREAMING_SNAKE_CASE_	     =       32





def      __snake_case (				_lowercase			,_lowercase			,_lowercase			,_lowercase			,_lowercase = 16    ):


    """simple docstring"""







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

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

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

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

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

    UpperCamelCase              =	DataLoader(
        tokenized_datasets['''test''']			,shuffle=_lowercase			,collate_fn=_lowercase			,batch_size=_lowercase    )

    return train_dataloader, eval_dataloader, test_dataloader





def      __snake_case (				_lowercase			,_lowercase    ):


    """simple docstring"""







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

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

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

    set_seed(_lowercase    )

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

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

        # Instantiate optimizer
        UpperCamelCase              =	AdamW(params=model.parameters()			,lr=_lowercase    )

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

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

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

            UpperCamelCase              =	metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f'epoch {epoch}:'			,_lowercase    )

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





def      __snake_case (				):


    """simple docstring"""







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


if __name__ == "__main__":
  main()

"""simple docstring"""




# Usage:
# ./gen-card-facebook-wmt19.py

import os
from pathlib import Path




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      int   			=  {
      '''en''': '''Machine learning is great, isn\'t it?''',
      '''ru''': '''Машинное обучение - это здорово, не так ли?''',
      '''de''': '''Maschinelles Lernen ist großartig, oder?''',
  }

  # BLUE scores as follows:
  # "pair": [fairseq, transformers]
  _lowercase:      Dict   			=  {
      '''ru-en''': ['''[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)''', '''39.20'''],
      '''en-ru''': ['''[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)''', '''33.47'''],
      '''en-de''': ['''[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)''', '''42.83'''],
      '''de-en''': ['''[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)''', '''41.35'''],
  }
  _lowercase:      Optional[Any]   			=  f'''{src_lang}-{tgt_lang}'''

  _lowercase:      Tuple   			=  f'''
---
language: 
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.

For more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).

The abbreviation FSMT stands for FairSeqMachineTranslation

All four models are available:

* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)

## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "facebook/wmt19-{src_lang}-{tgt_lang}"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)

input = "{texts[src_lang]}"
input_ids = tokenizer.encode(input, return_tensors="pt")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}

```

#### Limitations and bias

- The original (and this ported model) doesn\'t seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)

## Training data

Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).

## Eval results

pair   | fairseq | transformers
-------|---------|----------
{pair}  | {scores[pair][0]} | {scores[pair][1]}

The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:
- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
- re-ranking

The score was calculated using this code:

```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=15
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```
note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.

## Data Sources

- [training, etc.](http://www.statmt.org/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)


### BibTeX entry and citation info

```bibtex
@inproceedings{{...,
  year={{2020}},
  title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},
  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},
  booktitle={{Proc. of WMT}},
}}
```


## TODO

- port model ensemble (fairseq uses 4 model checkpoints)

'''
  os.makedirs(_UpperCamelCase			,					exist_ok=_UpperCamelCase    )
  _lowercase:      List[str]   			=  os.path.join(_UpperCamelCase			,					'''README.md'''    )
  print(f'''Generating {path}'''    )
  with open(_UpperCamelCase			,					'''w'''			,					encoding='''utf-8'''    ) as f:
    f.write(_UpperCamelCase    )


# make sure we are under the root of the project
A__ :       List[str]				= Path(__file__).resolve().parent.parent.parent
A__ :       Optional[int]				= repo_dir / 'model_cards'

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
  A__				,			A__				,			A__ :       int				= model_name.split('-')
  A__ :       Optional[int]				= model_cards_dir / 'facebook' / model_name
  write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)
