



import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
							# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
							# setup instructions, if using on-demand hardware

							# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
							# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
							# Throw an error if user passes both BYO and on-demand cluster args
							# Otherwise, use default values
							lowercase_								=     argparse.ArgumentParser()
							parser.add_argument("""--user""", type=str, default="""ubuntu""")
							parser.add_argument("""--host""", type=str, default="""localhost""")
							parser.add_argument("""--key_path""", type=str, default=None)
							parser.add_argument("""--instance""", type=str, default="""V100:1""")
							parser.add_argument("""--provider""", type=str, default="""cheapest""")
							parser.add_argument("""--use_spot""", type=bool, default=False)
							parser.add_argument("""--example""", type=str, default="""pytorch/text-generation/run_generation.py""")
							lowercase_				,    lowercase_								=     parser.parse_known_args()
							if args.host != "localhost":
														if args.instance != "V100:1" or args.provider != "cheapest":
																					raise ValueError("""Cannot specify both BYO and on-demand cluster args""")
														lowercase_								=     rh.cluster(
														    name="""rh-cluster""", ips=[args.host], ssh_creds={"""ssh_user""": args.user, """ssh_private_key""": args.key_path}
														)
							else:
														lowercase_								=     rh.cluster(
														    name="""rh-cluster""", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
														)
							lowercase_								=     args.example.rsplit("""/""", 1)[0]

							# Set up remote environment
							cluster.install_packages(["""pip:./"""])  # Installs transformers from local source
							# Note transformers is copied into the home directory on the remote machine, so we can install from there
							cluster.run([F'pip install -r transformers/examples/{example_dir}/requirements.txt'])
							cluster.run(["""pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117"""])

							# Run example. You can bypass the CLI wrapper and paste your own code here.
							cluster.run([F'python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}'])

							# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
							# from my_script... import train
							# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
							# launch_train_gpu = rh.function(fn=train,
							#                                system=gpu,
							#                                reqs=reqs,
							#                                name='train_bert_glue')
							#
							# We can pass in arguments just like we would to a function:
							# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
							#                  stream_logs=True)







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

import os
from pathlib import Path



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				{
			    "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__					  =				{
			    "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__					  =				f'''{src_lang}-{tgt_lang}'''

			lowercase__					  =				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(SCREAMING_SNAKE_CASE_						,  exist_ok=SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				os.path.join(SCREAMING_SNAKE_CASE_						,  "README.md"  )
			print(f'''Generating {path}'''  )
			with open(SCREAMING_SNAKE_CASE_						,  "w"						,  encoding="utf-8"  ) as f:
						f.write(SCREAMING_SNAKE_CASE_  )


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

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




