'''simple docstring'''




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

import os
from pathlib import Path




def 							lowerCamelCase	(      UpperCAmelCase__       :					Optional[Any]      ,      UpperCAmelCase__       :					Dict      ,      UpperCAmelCase__       :					List[Any] )			->      Union[str, Any]:
							lowercase_					:			str										=       {
							    """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_					:			List[Any]										=       {
							    """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_					:			List[Any]										=       F'''{src_lang}-{tgt_lang}'''

							lowercase_					:			str										=       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_					:			Union[str, Any]										=       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
_lowercase						: List[str]						=						Path(__file__).resolve().parent.parent.parent
_lowercase						: Tuple						=						repo_dir / "model_cards"

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






'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_albert import AlbertTokenizer
else:
						_lowercase						: int						=						None

_lowercase						: Union[str, Any]						=						logging.get_logger(__name__)
_lowercase						: Tuple						=						{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_lowercase						: Dict						=						{
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json",
    },
}

_lowercase						: int						=						{
    "albert-base-v1": 512,
    "albert-large-v1": 512,
    "albert-xlarge-v1": 512,
    "albert-xxlarge-v1": 512,
    "albert-base-v2": 512,
    "albert-large-v2": 512,
    "albert-xlarge-v2": 512,
    "albert-xxlarge-v2": 512,
}

_lowercase						: Tuple						=						"▁"





class     __magic_name__						(    _UpperCAmelCase):
			UpperCamelCase__				=       VOCAB_FILES_NAMES
			UpperCamelCase__				=       PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__				=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__				=       AlbertTokenizer







			def __init__(	self				:  Optional[Any]				,			lowercase_				:  Union[str, Any]=None				,			lowercase_				:  str=None				,			lowercase_				:  Any=True				,			lowercase_				:  Optional[int]=True				,			lowercase_				:  List[str]=False				,			lowercase_				:  Optional[int]="[CLS]"				,			lowercase_				:  Any="[SEP]"				,			lowercase_				:  int="<unk>"				,			lowercase_				:  Any="[SEP]"				,			lowercase_				:  int="<pad>"				,			lowercase_				:  Tuple="[CLS]"				,			lowercase_				:  Dict="[MASK]"				,			**lowercase_				:  Optional[int]				,			):
										# Mask token behave like a normal word, i.e. include the space before it and
										# is included in the raw text, there should be a match in a non-normalized sentence.
										lowercase_					:			Tuple										=       (
										    AddedToken(lowercase_				,			lstrip=lowercase_				,			rstrip=lowercase_				,			normalized=lowercase_    )
										    if isinstance(lowercase_				,			lowercase_    )
										    else mask_token
										)

										super().__init__(
										    lowercase_				,			tokenizer_file=lowercase_				,			do_lower_case=lowercase_				,			remove_space=lowercase_				,			keep_accents=lowercase_				,			bos_token=lowercase_				,			eos_token=lowercase_				,			unk_token=lowercase_				,			sep_token=lowercase_				,			pad_token=lowercase_				,			cls_token=lowercase_				,			mask_token=lowercase_				,			**lowercase_				,			)

										lowercase_					:			Optional[int]										=       do_lower_case
										lowercase_					:			Any										=       remove_space
										lowercase_					:			Dict										=       keep_accents
										lowercase_					:			Optional[int]										=       vocab_file
										lowercase_					:			Any										=       False if not self.vocab_file else True







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple				,			lowercase_				:  List[int]				,			lowercase_				:  Optional[List[int]] = None    ):
										lowercase_					:			Tuple										=       [self.sep_token_id]
										lowercase_					:			str										=       [self.cls_token_id]
										if token_ids_a is None:
																	return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any				,			lowercase_				:  List[int]				,			lowercase_				:  Optional[List[int]] = None    ):
										lowercase_					:			Union[str, Any]										=       [self.sep_token_id]
										lowercase_					:			int										=       [self.cls_token_id]

										if token_ids_a is None:
																	return len(cls + token_ids_a + sep    ) * [0]
										return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]				,			lowercase_				:  str				,			lowercase_				:  Optional[str] = None    ):
										if not self.can_save_slow_tokenizer:
																	raise ValueError(
																	    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
																	    """tokenizer."""    )

										if not os.path.isdir(lowercase_    ):
																	logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
																	return
										lowercase_					:			Optional[Any]										=       os.path.join(
										    lowercase_				,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )

										if os.path.abspath(self.vocab_file    ) != os.path.abspath(lowercase_    ):
																	copyfile(self.vocab_file				,			lowercase_    )

										return (out_vocab_file,)





