






"""simple docstring"""




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

import os
from pathlib import Path

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__       ):



  '''simple docstring'''
  _a						: List[Any]    =			{
      """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]
  _a						: Union[str, Any]    =			{
      """wmt19-de-en-6-6-base""": [0, 38.37],
      """wmt19-de-en-6-6-big""": [0, 39.90],
  }
  _a						: Any    =			F"""{src_lang}-{tgt_lang}"""

  _a						: int    =			F"""
---

language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- allenai
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt19 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.

For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).

2 models are available:

* [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)
* [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)


## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = \"allenai/{model_name}\"
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


## Training data

Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).

## Eval results

Here are the BLEU scores:

model   |  transformers
-------|---------
{model_name}  |  {scores[model_name][1]}

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=5
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 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
```

## 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

```
@misc{{kasai2020deep,
    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},
    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},
    year={{2020}},
    eprint={{2006.10369}},
    archivePrefix={{arXiv}},
    primaryClass={{cs.CL}}
}}
```

"""
  model_card_dir.mkdir(parents=UpperCamelCase__  ,     exist_ok=UpperCamelCase__       )
  _a						: Any    =			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
_snake_case					   =     Path(__file__).resolve().parent.parent.parent
_snake_case					   =     repo_dir / 'model_cards'

for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
 _snake_case					   =     model_cards_dir / 'allenai' / model_name
 write_model_card(model_card_dir, src_lang='de', tgt_lang='en', model_name=model_name)









"""simple docstring"""




from __future__ import annotations

import math
from itertools import permutations

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''

  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5  ,     int(math.sqrt(UpperCamelCase__       ) + 1       )  ,     6       ):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True

def     lowerCAmelCase__  (  UpperCamelCase__ = 7       ):



  '''simple docstring'''
  _a						: Tuple    =			"""""".join(str(UpperCamelCase__       ) for i in range(1  ,     n + 1       )       )
  _a						: str    =			[int("""""".join(UpperCamelCase__       )       ) for i in permutations(UpperCamelCase__  ,     UpperCamelCase__       )]
  _a						: Union[str, Any]    =			[num for num in perm_list if is_prime(UpperCamelCase__       )]
  return max(UpperCamelCase__       ) if pandigitals else 0


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

