'''simple docstring'''




from __future__ import annotations






class 							a__:


			'''simple docstring'''






			def __init__(  self			,       __lowerCAmelCase = 0):




					"""simple docstring"""


					lowerCAmelCase     =    key



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


					assert isinstance(A_			,       A_) and isinstance(A_			,       A_)

					lowerCAmelCase     =    key or self.__key or 1

					# make sure key is an appropriate size
					key %= 255

					return [chr(ord(A_) ^ key) for ch in content]



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


					assert isinstance(A_			,       A_) and isinstance(A_			,       A_)

					lowerCAmelCase     =    key or self.__key or 1

					# make sure key is an appropriate size
					key %= 255

					return [chr(ord(A_) ^ key) for ch in content]



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = 0):




					"""simple docstring"""


					assert isinstance(A_			,       A_) and isinstance(A_			,       A_)

					lowerCAmelCase     =    key or self.__key or 1

					# make sure key can be any size
					while key > 255:
							key -= 255

					# This will be returned
					lowerCAmelCase     =    ""

					for ch in content:
							ans += chr(ord(A_) ^ key)

					return ans



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = 0):




					"""simple docstring"""


					assert isinstance(A_			,       A_) and isinstance(A_			,       A_)

					lowerCAmelCase     =    key or self.__key or 1

					# make sure key can be any size
					while key > 255:
							key -= 255

					# This will be returned
					lowerCAmelCase     =    ""

					for ch in content:
							ans += chr(ord(A_) ^ key)

					return ans



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = 0):




					"""simple docstring"""


					assert isinstance(A_			,       A_) and isinstance(A_			,       A_)

					try:
							with open(A_) as fin, open("""encrypt.out"""			,       """w+""") as fout:
									# actual encrypt-process
									for line in fin:
											fout.write(self.encrypt_string(A_			,       A_))

					except OSError:
							return False

					return True







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


					assert isinstance(A_			,       A_) and isinstance(A_			,       A_)

					try:
							with open(A_) as fin, open("""decrypt.out"""			,       """w+""") as fout:
									# actual encrypt-process
									for line in fin:
											fout.write(self.decrypt_string(A_			,       A_))

					except OSError:
							return False

					return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")











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

import os
from pathlib import Path



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase	)		->					str:
 UpperCamelCase      :   Union[str, Any]			      =  {
     "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]
 UpperCamelCase      :   List[str]			      =  {
     "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"],
 }
 UpperCamelCase      :   str			      =  F"""{src_lang}-{tgt_lang}"""

 UpperCamelCase      :   Dict			      =  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	)
 UpperCamelCase      :   Union[str, Any]			      =  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
__lowerCamelCase			:	str					    =	Path(__file__).resolve().parent.parent.parent
__lowerCamelCase			:	Optional[Any]					    =	repo_dir / """model_cards"""

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