

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

import os
from pathlib import Path


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	str						)		->				List[Any]:
 '''simple docstring'''


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

 SCREAMING_SNAKE_CASE		    =      f"""
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt16
- allenai
license: apache-2.0
datasets:
- wmt16
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt16 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).

All 3 models are available:

* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)


## 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   | fairseq | transformers
-------|---------|----------
{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}

The 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.

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 wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt16 -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/wmt16/)
- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)


### 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						)
 SCREAMING_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
a_     :    Optional[int]     		=   Path(__file__).resolve().parent.parent.parent
a_     :    Union[str, Any]     		=   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"]:
      a_     :    List[Any]     		=   model_cards_dir / "allenai" / model_name
      write_model_card(model_card_dir, src_lang="en", tgt_lang="de", model_name=model_name)




import math


def 							__lowerCAmelCase     (							_UpperCamelCase		:	int						)		->				list[int]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      []
 SCREAMING_SNAKE_CASE		    =      2
 SCREAMING_SNAKE_CASE		    =      int(math.sqrt(_UpperCamelCase						)						)  # Size of every segment
 SCREAMING_SNAKE_CASE		    =      [True] * (end + 1)
 SCREAMING_SNAKE_CASE		    =      []

 while start <= end:
  if temp[start] is True:
   in_prime.append(_UpperCamelCase						)
   for i in range(start * start		,				end + 1		,				_UpperCamelCase						):
    SCREAMING_SNAKE_CASE		    =      False
  start += 1
 prime += in_prime

 SCREAMING_SNAKE_CASE		    =      end + 1
 SCREAMING_SNAKE_CASE		    =      min(2 * end		,				_UpperCamelCase						)

 while low <= n:
  SCREAMING_SNAKE_CASE		    =      [True] * (high - low + 1)
  for each in in_prime:
   SCREAMING_SNAKE_CASE		    =      math.floor(low / each						) * each
   if t < low:
    t += each

   for j in range(_UpperCamelCase		,				high + 1		,				_UpperCamelCase						):
    SCREAMING_SNAKE_CASE		    =      False

  for j in range(len(_UpperCamelCase						)						):
   if temp[j] is True:
    prime.append(j + low						)

  SCREAMING_SNAKE_CASE		    =      high + 1
  SCREAMING_SNAKE_CASE		    =      min(high + end		,				_UpperCamelCase						)

 return prime




print(sieve(10**6))

