






'''simple docstring'''
import os
from pathlib import Path




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Dict           =     {
       '''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_	:    List[Any]           =     F"""{src_lang}-{tgt_lang}"""

   lowercase_	:    Optional[int]           =     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(lowerCAmelCase_						,		exist_ok=lowerCAmelCase_							)
   lowercase_	:    int           =     os.path.join(lowerCAmelCase_						,		"README.md"							)
   print(F"""Generating {path}"""							)
   with open(lowerCAmelCase_						,		"w"						,		encoding="utf-8"							) as f:
      f.write(lowerCAmelCase_							)


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

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







'''simple docstring'''
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   def update_area_of_max_square(_UpperCamelCase						,		_UpperCamelCase							) -> int:
      # BASE CASE
      if row >= rows or col >= cols:
         return 0

      lowercase_	:    List[str]           =     update_area_of_max_square(_UpperCamelCase						,		col + 1							)
      lowercase_	:    List[Any]           =     update_area_of_max_square(row + 1						,		col + 1							)
      lowercase_	:    Tuple           =     update_area_of_max_square(row + 1						,		_UpperCamelCase							)

      if mat[row][col]:
         lowercase_	:    Optional[int]           =     1 + min([right, diagonal, down]							)
         lowercase_	:    Any           =     max(largest_square_area[0]						,		_UpperCamelCase							)
         return sub_problem_sol
      else:
         return 0

   lowercase_	:    int           =     [0]
   update_area_of_max_square(0						,		0							)
   return largest_square_area[0]




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   def update_area_of_max_square_using_dp_array(
       _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							) -> int:
      if row >= rows or col >= cols:
         return 0
      if dp_array[row][col] != -1:
         return dp_array[row][col]

      lowercase_	:    Dict           =     update_area_of_max_square_using_dp_array(_UpperCamelCase						,		col + 1						,		_UpperCamelCase							)
      lowercase_	:    str           =     update_area_of_max_square_using_dp_array(row + 1						,		col + 1						,		_UpperCamelCase							)
      lowercase_	:    Optional[Any]           =     update_area_of_max_square_using_dp_array(row + 1						,		_UpperCamelCase						,		_UpperCamelCase							)

      if mat[row][col]:
         lowercase_	:    Tuple           =     1 + min([right, diagonal, down]							)
         lowercase_	:    int           =     max(largest_square_area[0]						,		_UpperCamelCase							)
         lowercase_	:    Dict           =     sub_problem_sol
         return sub_problem_sol
      else:
         return 0

   lowercase_	:    Any           =     [0]
   lowercase_	:    Optional[int]           =     [[-1] * cols for _ in range(_UpperCamelCase							)]
   update_area_of_max_square_using_dp_array(0						,		0						,		_UpperCamelCase							)

   return largest_square_area[0]




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    str           =     [[0] * (cols + 1) for _ in range(rows + 1							)]
   lowercase_	:    List[Any]           =     0
   for row in range(rows - 1						,		-1						,		-1							):
      for col in range(cols - 1						,		-1						,		-1							):
         lowercase_	:    Tuple           =     dp_array[row][col + 1]
         lowercase_	:    List[str]           =     dp_array[row + 1][col + 1]
         lowercase_	:    List[Any]           =     dp_array[row + 1][col]

         if mat[row][col] == 1:
            lowercase_	:    Any           =     1 + min(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)
            lowercase_	:    Any           =     max(dp_array[row][col]						,		_UpperCamelCase							)
         else:
            lowercase_	:    int           =     0

   return largest_square_area




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Optional[int]           =     [0] * (cols + 1)
   lowercase_	:    Union[str, Any]           =     [0] * (cols + 1)
   lowercase_	:    int           =     0
   for row in range(rows - 1						,		-1						,		-1							):
      for col in range(cols - 1						,		-1						,		-1							):
         lowercase_	:    Dict           =     current_row[col + 1]
         lowercase_	:    List[Any]           =     next_row[col + 1]
         lowercase_	:    Tuple           =     next_row[col]

         if mat[row][col] == 1:
            lowercase_	:    Dict           =     1 + min(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)
            lowercase_	:    int           =     max(current_row[col]						,		_UpperCamelCase							)
         else:
            lowercase_	:    Tuple           =     0
      lowercase_	:    Optional[Any]           =     current_row

   return largest_square_area


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))




