


'''simple docstring'''
import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__          = get_tests_dir('''fixtures/test_sentencepiece.model''')
UpperCamelCase__          = get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

UpperCamelCase__          = '''pt''' if is_torch_available() else '''tf'''




@require_sentencepiece
@require_tokenizers
class      lowerCamelCase_  (      __a	,			unittest.TestCase ):
      lowerCAmelCase__							=	CamembertTokenizer
      lowerCAmelCase__							=	CamembertTokenizerFast
      lowerCAmelCase__							=	True
      lowerCAmelCase__							=	True






      def lowercase_				(   self							:       str					):


          '''simple docstring'''


          super().setUp()

          # We have a SentencePiece fixture for testing
          UpperCAmelCase__     :						Optional[int]						=							CamembertTokenizer(_A					)
          tokenizer.save_pretrained(self.tmpdirname					)






      def lowercase_				(   self							:       List[Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Union[str, Any]						=							'''<pad>'''
          UpperCAmelCase__     :						Tuple						=							1

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A					)		,		_A					)
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A					)		,		_A					)






      def lowercase_				(   self							:       Tuple					):


          '''simple docstring'''


          UpperCAmelCase__     :						Optional[Any]						=							list(self.get_tokenizer().get_vocab().keys()					)

          self.assertEqual(vocab_keys[0]		,		'''<s>NOTUSED'''					)
          self.assertEqual(vocab_keys[1]		,		'''<pad>'''					)
          self.assertEqual(vocab_keys[-1]		,		'''<mask>'''					)
          self.assertEqual(len(_A					)		,		1_004					)






      def lowercase_				(   self							:       Union[str, Any]					):


          '''simple docstring'''


          self.assertEqual(self.get_tokenizer().vocab_size		,		1_005					)






      def lowercase_				(   self							:       Union[str, Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Dict						=							CamembertTokenizer(_A					)
          tokenizer.save_pretrained(self.tmpdirname					)
          UpperCAmelCase__     :						int						=							CamembertTokenizerFast.from_pretrained(self.tmpdirname					)

          UpperCAmelCase__     :						Any						=							'''I was born in 92000, and this is falsé.'''

          UpperCAmelCase__     :						Any						=							tokenizer.encode(_A					)
          UpperCAmelCase__     :						Tuple						=							rust_tokenizer.encode(_A					)
          self.assertListEqual(_A		,		_A					)

          UpperCAmelCase__     :						Dict						=							tokenizer.encode(_A		,		add_special_tokens=_A					)
          UpperCAmelCase__     :						Optional[Any]						=							rust_tokenizer.encode(_A		,		add_special_tokens=_A					)
          self.assertListEqual(_A		,		_A					)

          # <unk> tokens are not the same for `rust` than for `slow`.
          # Because spm gives back raw token instead of `unk` in EncodeAsPieces
          # tokens = tokenizer.tokenize(sequence)
          UpperCAmelCase__     :						Tuple						=							tokenizer.convert_ids_to_tokens(_A					)
          UpperCAmelCase__     :						int						=							rust_tokenizer.tokenize(_A					)
          self.assertListEqual(_A		,		_A					)






      def lowercase_				(   self							:       Optional[Any]					):


          '''simple docstring'''


          if not self.test_rust_tokenizer:
              return

          UpperCAmelCase__     :						str						=							self.get_tokenizer()
          UpperCAmelCase__     :						Tuple						=							self.get_rust_tokenizer()

          UpperCAmelCase__     :						List[Any]						=							'''I was born in 92000, and this is falsé.'''

          UpperCAmelCase__     :						List[str]						=							tokenizer.tokenize(_A					)
          UpperCAmelCase__     :						Any						=							rust_tokenizer.tokenize(_A					)
          self.assertListEqual(_A		,		_A					)

          UpperCAmelCase__     :						Any						=							tokenizer.encode(_A		,		add_special_tokens=_A					)
          UpperCAmelCase__     :						Optional[int]						=							rust_tokenizer.encode(_A		,		add_special_tokens=_A					)
          self.assertListEqual(_A		,		_A					)

          UpperCAmelCase__     :						str						=							self.get_rust_tokenizer()
          UpperCAmelCase__     :						Optional[int]						=							tokenizer.encode(_A					)
          UpperCAmelCase__     :						Optional[Any]						=							rust_tokenizer.encode(_A					)
          self.assertListEqual(_A		,		_A					)






      @slow
      def lowercase_				(   self							:       Any					):


          '''simple docstring'''


          UpperCAmelCase__     :						int						=							{'''input_ids''': [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 8, 930, 9, 6]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
          # fmt: on

          # camembert is a french model. So we also use french texts.
          UpperCAmelCase__     :						Tuple						=							[
              '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
              '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
              '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
              '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
              '''telles que la traduction et la synthèse de texte.''',
          ]

          self.tokenizer_integration_test_util(
              expected_encoding=_A		,		model_name='''camembert-base'''		,		revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'''		,		sequences=_A		,		)







# 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)
