# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path







def  __UpperCamelCase    (						A ,							A ,							A ,							A							):
     UpperCamelCase__										=      {
         '''en''': '''Machine learning is great, isn\'t it?''',
         '''ru''': '''Машинное обучение - это здорово, не так ли?''',
         '''de''': '''Maschinelles Lernen ist großartig, nicht wahr?''',
     }

     # BLUE scores as follows:
     # "pair": [fairseq, transformers]
     UpperCamelCase__										=      {
         '''wmt16-en-de-dist-12-1''': [28.3, 27.52],
         '''wmt16-en-de-dist-6-1''': [27.4, 27.11],
         '''wmt16-en-de-12-1''': [26.9, 25.75],
     }
     UpperCamelCase__										=      f"{src_lang}-{tgt_lang}"

     UpperCamelCase__										=      f"\n---\nlanguage:\n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt16\n- allenai\nlicense: apache-2.0\ndatasets:\n- wmt16\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.\n\nFor more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).\n\nAll 3 models are available:\n\n* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)\n* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)\n* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)\n\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"allenai/{model_name}\"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = \"{texts[src_lang]}\"\ninput_ids = tokenizer.encode(input, return_tensors=\"pt\")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n\n## Training data\n\nPretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).\n\n## Eval results\n\nHere are the BLEU scores:\n\nmodel   | fairseq | transformers\n-------|---------|----------\n{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}\n\nThe score is slightly below the score reported in the paper, as the researchers don't use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=5\nmkdir -p $DATA_DIR\nsacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py allenai/{model_name} $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\n```\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt16/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)\n\n\n### BibTeX entry and citation info\n\n```\n@misc{{kasai2020deep,\n    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},\n    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},\n    year={{2020}},\n    eprint={{2006.10369}},\n    archivePrefix={{arXiv}},\n    primaryClass={{cs.CL}}\n}}\n```\n\n"
     model_card_dir.mkdir(parents=A ,							exist_ok=A							)
     UpperCamelCase__										=      os.path.join(A ,							'''README.md'''							)
     print(f"Generating {path}"							)
     with open(A ,							'''w''' ,							encoding='''utf-8'''							) as f:
          f.write(A							)


# make sure we are under the root of the project
__magic_name__								=Path(__file__).resolve().parent.parent.parent
__magic_name__								=repo_dir / '''model_cards'''

for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
     __magic_name__								=model_cards_dir / '''allenai''' / model_name
     write_model_card(model_card_dir, src_lang='''en''', tgt_lang='''de''', model_name=model_name)




import argparse
import os

import evaluate
import torch
from datasets import load_dataset
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 properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


__magic_name__								=16
__magic_name__								=32







def  __UpperCamelCase    (						A ,							A = 16							):
     UpperCamelCase__										=      AutoTokenizer.from_pretrained('''bert-base-cased'''							)
     UpperCamelCase__										=      load_dataset('''glue''' ,							'''mrpc'''							)

     def tokenize_function(A							):
          # max_length=None => use the model max length (it's actually the default)
          UpperCamelCase__										=      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():
          UpperCamelCase__										=      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
     UpperCamelCase__										=      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.
          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(
              A ,							padding='''longest''' ,							max_length=A ,							pad_to_multiple_of=A ,							return_tensors='''pt''' ,							)

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

     return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
     from accelerate.test_utils.training import mocked_dataloaders

     __magic_name__								=mocked_dataloaders  # noqa: F811







def  __UpperCamelCase    (						A ,							A							):
     # For testing only
     if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,							A							) == "1":
          UpperCamelCase__										=      2
     # 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(A							)
     UpperCamelCase__						,       UpperCamelCase__										=      get_dataloaders(A ,							A							)
     # 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=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).
     UpperCamelCase__										=      model.to(accelerator.device							)

     # Instantiate optimizer
     UpperCamelCase__										=      AdamW(params=model.parameters() ,							lr=A							)

     # Instantiate scheduler
     UpperCamelCase__										=      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.
     UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__										=      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							)
               UpperCamelCase__										=      model(**A							)
               UpperCamelCase__										=      outputs.loss
               UpperCamelCase__										=      loss / gradient_accumulation_steps
               accelerator.backward(A							)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

          model.eval()
          UpperCamelCase__										=      0
          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():
                    UpperCamelCase__										=      model(**A							)
               UpperCamelCase__										=      outputs.logits.argmax(dim=-1							)
               UpperCamelCase__						,       UpperCamelCase__										=      accelerator.gather((predictions, batch['''labels'''])							)
               # New Code #
               # First we check if it's a distributed system
               if accelerator.use_distributed:
                    # Then see if we're on the last batch of our eval dataloader
                    if step == len(A							) - 1:
                         # Last batch needs to be truncated on distributed systems as it contains additional samples
                         UpperCamelCase__										=      predictions[: len(eval_dataloader.dataset							) - samples_seen]
                         UpperCamelCase__										=      references[: len(eval_dataloader.dataset							) - samples_seen]
                    else:
                         # Otherwise we add the number of samples seen
                         samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
               metric.add_batch(
                   predictions=A ,							references=A ,							)

          UpperCamelCase__										=      metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f"epoch {epoch}:" ,							A							)







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      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.'''							)
     UpperCamelCase__										=      parser.parse_args()
     UpperCamelCase__										=      {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(A ,							A							)


if __name__ == "__main__":
     main()



