
"""simple docstring"""



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

import os
from pathlib import Path






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any     ,  SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					Dict     ,  SCREAMING_SNAKE_CASE:					Tuple						):






							"""simple docstring"""



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

							_lowerCAmelCase  	=			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=SCREAMING_SNAKE_CASE     ,  exist_ok=SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			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
_snake_case          =						Path(__file__).resolve().parent.parent.parent
_snake_case          =						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"]:
	_snake_case          =						model_cards_dir / '''allenai''' / model_name
	write_model_card(model_card_dir, src_lang='''en''', tgt_lang='''de''', model_name=model_name)








"""simple docstring"""



def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			0

							# if input_string is "aba" than new_input_string become "a|b|a"
							_lowerCAmelCase  	=			''
							_lowerCAmelCase  	=			''

							# append each character + "|" in new_string for range(0, length-1)
							for i in input_string[: len(SCREAMING_SNAKE_CASE						) - 1]:
														new_input_string += i + "|"
							# append last character
							new_input_string += input_string[-1]

							# we will store the starting and ending of previous furthest ending palindromic
							# substring
							_lowerCAmelCase	,		_lowerCAmelCase  	=			0, 0

							# length[i] shows the length of palindromic substring with center i
							_lowerCAmelCase  	=			[1 for i in range(len(SCREAMING_SNAKE_CASE						)						)]

							# for each character in new_string find corresponding palindromic string
							_lowerCAmelCase  	=			0
							for j in range(len(SCREAMING_SNAKE_CASE						)						):
														_lowerCAmelCase  	=			1 if j > r else min(length[l + r - j] // 2     ,  r - j + 1						)
														while (
														    j - k >= 0
														    and j + k < len(SCREAMING_SNAKE_CASE						)
														    and new_input_string[k + j] == new_input_string[j - k]
														):
																					k += 1

														_lowerCAmelCase  	=			2 * k - 1

														# does this string is ending after the previously explored end (that is r) ?
														# if yes the update the new r to the last index of this
														if j + k - 1 > r:
																					_lowerCAmelCase  	=			j - k + 1  # noqa: E741
																					_lowerCAmelCase  	=			j + k - 1

														# update max_length and start position
														if max_length < length[j]:
																					_lowerCAmelCase  	=			length[j]
																					_lowerCAmelCase  	=			j

    # create that string
							_lowerCAmelCase  	=			new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
							for i in s:
														if i != "|":
																					output_string += i

							return output_string


if __name__ == "__main__":
	import doctest

	doctest.testmod()






