"""simple docstring"""
# Usage:
# ./gen-card-facebook-wmt19.py

import os
from pathlib import Path





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Dict	,     _UpperCamelCase    :			List[str]	,     _UpperCamelCase    :			Optional[int]   )   ->			Optional[Any]:





						"""simple docstring"""






						snake_case        =     {
						    '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]
						snake_case        =     {
						    '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'],
						}
						snake_case        =     f"""{src_lang}-{tgt_lang}"""

						snake_case        =     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   )
						snake_case        =     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
SCREAMING_SNAKE_CASE__   =					Path(__file__).resolve().parent.parent.parent
SCREAMING_SNAKE_CASE__   =					repo_dir / "model_cards"

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



"""simple docstring"""
import os





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str = "matrix.txt"   )   ->			int:





						"""simple docstring"""






						with open(os.path.join(os.path.dirname(_UpperCamelCase   )	,     _UpperCamelCase   )   ) as in_file:
												snake_case        =     in_file.read()

						snake_case        =     [[int(_UpperCamelCase   ) for cell in row.split(','   )] for row in data.strip().splitlines()]
						snake_case        =     [[0 for cell in row] for row in grid]
						snake_case        =     len(grid[0]   )

						snake_case        =     [[0 for i in range(_UpperCamelCase   )] for j in range(_UpperCamelCase   )]
						snake_case        =     grid[0][0]
						for i in range(1	,     _UpperCamelCase   ):
												snake_case        =     grid[0][i] + dp[0][i - 1]
						for i in range(1	,     _UpperCamelCase   ):
												snake_case        =     grid[i][0] + dp[i - 1][0]

						for i in range(1	,     _UpperCamelCase   ):
												for j in range(1	,     _UpperCamelCase   ):
																		snake_case        =     grid[i][j] + min(dp[i - 1][j]	,     dp[i][j - 1]   )

						return dp[-1][-1]


if __name__ == "__main__":
					print(f"""{solution() = }""")


