
"""simple docstring"""




import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class       __magic_name__							(							SCREAMING_SNAKE_CASE__ ,       unittest.TestCase			):
       UpperCamelCase_ =		LongformerTokenizer
       UpperCamelCase_ =		True
       UpperCamelCase_ =		LongformerTokenizerFast
       UpperCamelCase_ =		True
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         _lowercase:      Any   			=  [
             '''l''',
             '''o''',
             '''w''',
             '''e''',
             '''r''',
             '''s''',
             '''t''',
             '''i''',
             '''d''',
             '''n''',
             '''\u0120''',
             '''\u0120l''',
             '''\u0120n''',
             '''\u0120lo''',
             '''\u0120low''',
             '''er''',
             '''\u0120lowest''',
             '''\u0120newer''',
             '''\u0120wider''',
             '''<unk>''',
         ]
         _lowercase:      Optional[Any]   			=  dict(zip(A_			,					range(len(A_							)							)							)							)
         _lowercase:      int   			=  ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
         _lowercase:      Union[str, Any]   			=  {'''unk_token''': '''<unk>'''}

         _lowercase:      List[str]   			=  os.path.join(self.tmpdirname			,					VOCAB_FILES_NAMES['''vocab_file''']							)
         _lowercase:      Union[str, Any]   			=  os.path.join(self.tmpdirname			,					VOCAB_FILES_NAMES['''merges_file''']							)
         with open(self.vocab_file			,					'''w'''			,					encoding='''utf-8'''							) as fp:
           fp.write(json.dumps(A_							) + '''\n'''							)
         with open(self.merges_file			,					'''w'''			,					encoding='''utf-8'''							) as fp:
           fp.write('''\n'''.join(A_							)							)
       def 				lowercase_				(  self			,					**A_							)  ->						Tuple:





         """simple docstring"""




         kwargs.update(self.special_tokens_map							)
         return self.tokenizer_class.from_pretrained(self.tmpdirname			,					**A_							)
       def 				lowercase_				(  self			,					**A_							)  ->						List[Any]:





         """simple docstring"""




         kwargs.update(self.special_tokens_map							)
         return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,					**A_							)
       def 				lowercase_				(  self			,					A_							)  ->						Dict:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  '''lower newer'''
         _lowercase:      Union[str, Any]   			=  '''lower newer'''
         return input_text, output_text
       def 				lowercase_				(  self							)  ->						Any:





         """simple docstring"""




         _lowercase:      Tuple   			=  self.tokenizer_class(self.vocab_file			,					self.merges_file			,					**self.special_tokens_map							)
         _lowercase:      List[Any]   			=  '''lower newer'''
         _lowercase:      Tuple   			=  ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
         _lowercase:      Union[str, Any]   			=  tokenizer.tokenize(A_							)  # , add_prefix_space=True)
         self.assertListEqual(A_			,					A_							)

         _lowercase:      int   			=  tokens + [tokenizer.unk_token]
         _lowercase:      str   			=  [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(A_							)			,					A_							)
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      Optional[int]   			=  self.get_tokenizer()

         self.assertListEqual(tokenizer.encode('''Hello world!'''			,					add_special_tokens=A_							)			,					[0, 3_1414, 232, 328, 2]							)
         self.assertListEqual(
             tokenizer.encode('''Hello world! cécé herlolip 418'''			,					add_special_tokens=A_							)			,					[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]			,					)
       @slow
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         _lowercase:      Dict   			=  self.tokenizer_class.from_pretrained('''allenai/longformer-base-4096'''							)

         _lowercase:      List[Any]   			=  tokenizer.encode('''sequence builders'''			,					add_special_tokens=A_							)
         _lowercase:      Optional[Any]   			=  tokenizer.encode('''multi-sequence build'''			,					add_special_tokens=A_							)

         _lowercase:      Any   			=  tokenizer.encode(
             '''sequence builders'''			,					add_special_tokens=A_			,					add_prefix_space=A_							)
         _lowercase:      List[Any]   			=  tokenizer.encode(
             '''sequence builders'''			,					'''multi-sequence build'''			,					add_special_tokens=A_			,					add_prefix_space=A_							)

         _lowercase:      Optional[int]   			=  tokenizer.build_inputs_with_special_tokens(A_							)
         _lowercase:      str   			=  tokenizer.build_inputs_with_special_tokens(A_			,					A_							)

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      Tuple   			=  self.get_tokenizer()

         _lowercase:      str   			=  '''Encode this sequence.'''
         _lowercase:      List[str]   			=  tokenizer.byte_encoder[''' '''.encode('''utf-8'''							)[0]]

         # Testing encoder arguments
         _lowercase:      Tuple   			=  tokenizer.encode(A_			,					add_special_tokens=A_			,					add_prefix_space=A_							)
         _lowercase:      Any   			=  tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
         self.assertNotEqual(A_			,					A_							)

         _lowercase:      Dict   			=  tokenizer.encode(A_			,					add_special_tokens=A_			,					add_prefix_space=A_							)
         _lowercase:      List[str]   			=  tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
         self.assertEqual(A_			,					A_							)

         tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}							)
         _lowercase:      Optional[Any]   			=  tokenizer.encode(A_			,					add_special_tokens=A_							)
         _lowercase:      Union[str, Any]   			=  tokenizer.convert_ids_to_tokens(encoded[1]							)[0]
         self.assertNotEqual(A_			,					A_							)

         # Testing spaces after special tokens
         _lowercase:      Optional[Any]   			=  '''<mask>'''
         tokenizer.add_special_tokens(
             {'''mask_token''': AddedToken(A_			,					lstrip=A_			,					rstrip=A_							)}							)  # mask token has a left space
         _lowercase:      Union[str, Any]   			=  tokenizer.convert_tokens_to_ids(A_							)

         _lowercase:      str   			=  '''Encode <mask> sequence'''
         _lowercase:      Any   			=  '''Encode <mask>sequence'''

         _lowercase:      Optional[Any]   			=  tokenizer.encode(A_							)
         _lowercase:      List[str]   			=  encoded.index(A_							)
         _lowercase:      str   			=  tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
         self.assertEqual(A_			,					A_							)

         _lowercase:      Tuple   			=  tokenizer.encode(A_							)
         _lowercase:      str   			=  encoded.index(A_							)
         _lowercase:      int   			=  tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
         self.assertNotEqual(A_			,					A_							)
       def 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         pass
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
             _lowercase:      str   			=  self.rust_tokenizer_class.from_pretrained(A_			,					**A_							)
             _lowercase:      List[str]   			=  self.tokenizer_class.from_pretrained(A_			,					**A_							)
             _lowercase:      Dict   			=  '''A, <mask> AllenNLP sentence.'''
             _lowercase:      Union[str, Any]   			=  tokenizer_r.encode_plus(A_			,					add_special_tokens=A_			,					return_token_type_ids=A_							)
             _lowercase:      Optional[Any]   			=  tokenizer_p.encode_plus(A_			,					add_special_tokens=A_			,					return_token_type_ids=A_							)

             # token_type_ids should put 0 everywhere
             self.assertEqual(sum(tokens_r['''token_type_ids''']							)			,					sum(tokens_p['''token_type_ids''']							)							)

             # attention_mask should put 1 everywhere, so sum over length should be 1
             self.assertEqual(
                 sum(tokens_r['''attention_mask''']							) / len(tokens_r['''attention_mask''']							)			,					sum(tokens_p['''attention_mask''']							) / len(tokens_p['''attention_mask''']							)			,					)

             _lowercase:      Optional[int]   			=  tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']							)
             _lowercase:      Optional[int]   			=  tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']							)

             # Rust correctly handles the space before the mask while python doesnt
             self.assertSequenceEqual(tokens_p['''input_ids''']			,					[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]							)
             self.assertSequenceEqual(tokens_r['''input_ids''']			,					[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]							)

             self.assertSequenceEqual(
                 A_			,					['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']							)
             self.assertSequenceEqual(
                 A_			,					['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']							)
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         for trim_offsets, add_prefix_space in itertools.product([True, False]			,					repeat=2							):
           _lowercase:      Dict   			=  self.rust_tokenizer_class.from_pretrained(
               self.tmpdirname			,					use_fast=A_			,					add_prefix_space=A_			,					trim_offsets=A_							)

           _lowercase:      str   			=  json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()							)
           _lowercase:      List[Any]   			=  json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()							)

           self.assertEqual(pre_tokenizer_state['''add_prefix_space''']			,					A_							)

           self.assertEqual(post_processor_state['''add_prefix_space''']			,					A_							)
           self.assertEqual(post_processor_state['''trim_offsets''']			,					A_							)



       def 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
             _lowercase:      Tuple   			=  '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
             _lowercase:      Dict   			=  f'''{text_of_1_token} {text_of_1_token}'''

             _lowercase:      Optional[int]   			=  self.rust_tokenizer_class.from_pretrained(
                 A_			,					use_fast=A_			,					add_prefix_space=A_			,					trim_offsets=A_							)
             _lowercase:      Union[str, Any]   			=  tokenizer_r(A_			,					return_offsets_mapping=A_			,					add_special_tokens=A_							)
             self.assertEqual(encoding.offset_mapping[0]			,					(0, len(A_							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]			,					(len(A_							) + 1, len(A_							) + 1 + len(A_							))			,					)

             _lowercase:      Dict   			=  self.rust_tokenizer_class.from_pretrained(
                 A_			,					use_fast=A_			,					add_prefix_space=A_			,					trim_offsets=A_							)
             _lowercase:      str   			=  tokenizer_r(A_			,					return_offsets_mapping=A_			,					add_special_tokens=A_							)
             self.assertEqual(encoding.offset_mapping[0]			,					(0, len(A_							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]			,					(len(A_							) + 1, len(A_							) + 1 + len(A_							))			,					)

             _lowercase:      Tuple   			=  self.rust_tokenizer_class.from_pretrained(
                 A_			,					use_fast=A_			,					add_prefix_space=A_			,					trim_offsets=A_							)
             _lowercase:      Optional[Any]   			=  tokenizer_r(A_			,					return_offsets_mapping=A_			,					add_special_tokens=A_							)
             self.assertEqual(encoding.offset_mapping[0]			,					(0, len(A_							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]			,					(len(A_							), len(A_							) + 1 + len(A_							))			,					)

             _lowercase:      Tuple   			=  self.rust_tokenizer_class.from_pretrained(
                 A_			,					use_fast=A_			,					add_prefix_space=A_			,					trim_offsets=A_							)
             _lowercase:      Optional[int]   			=  tokenizer_r(A_			,					return_offsets_mapping=A_			,					add_special_tokens=A_							)
             self.assertEqual(encoding.offset_mapping[0]			,					(0, len(A_							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]			,					(len(A_							), len(A_							) + 1 + len(A_							))			,					)

             _lowercase:      str   			=  f''' {text}'''

             # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
             #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
             # )
             # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
             # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
             # self.assertEqual(
             #     encoding.offset_mapping[1],
             #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
             # )

             _lowercase:      Optional[int]   			=  self.rust_tokenizer_class.from_pretrained(
                 A_			,					use_fast=A_			,					add_prefix_space=A_			,					trim_offsets=A_							)
             _lowercase:      Optional[Any]   			=  tokenizer_r(A_			,					return_offsets_mapping=A_			,					add_special_tokens=A_							)
             self.assertEqual(encoding.offset_mapping[0]			,					(1, 1 + len(A_							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]			,					(1 + len(A_							) + 1, 1 + len(A_							) + 1 + len(A_							))			,					)

             _lowercase:      str   			=  self.rust_tokenizer_class.from_pretrained(
                 A_			,					use_fast=A_			,					add_prefix_space=A_			,					trim_offsets=A_							)
             _lowercase:      Any   			=  tokenizer_r(A_			,					return_offsets_mapping=A_			,					add_special_tokens=A_							)
             self.assertEqual(encoding.offset_mapping[0]			,					(0, 1 + len(A_							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]			,					(1 + len(A_							), 1 + len(A_							) + 1 + len(A_							))			,					)

             _lowercase:      Optional[Any]   			=  self.rust_tokenizer_class.from_pretrained(
                 A_			,					use_fast=A_			,					add_prefix_space=A_			,					trim_offsets=A_							)
             _lowercase:      Union[str, Any]   			=  tokenizer_r(A_			,					return_offsets_mapping=A_			,					add_special_tokens=A_							)
             self.assertEqual(encoding.offset_mapping[0]			,					(0, 1 + len(A_							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]			,					(1 + len(A_							), 1 + len(A_							) + 1 + len(A_							))			,					)


"""simple docstring"""




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

import os
from pathlib import Path




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      int   			=  {
      '''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]
  _lowercase:      Dict   			=  {
      '''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'''],
  }
  _lowercase:      Optional[Any]   			=  f'''{src_lang}-{tgt_lang}'''

  _lowercase:      Tuple   			=  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(_UpperCamelCase			,					exist_ok=_UpperCamelCase    )
  _lowercase:      List[str]   			=  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__ :       List[str]				= Path(__file__).resolve().parent.parent.parent
A__ :       Optional[int]				= repo_dir / 'model_cards'

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