



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

import os
from pathlib import Path




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  int:



    """simple docstring"""






    A			:	List[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]
    A			:	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"""],
    }
    A			:	Any										=       f'''{src_lang}-{tgt_lang}'''

    A			:	List[Any]										=       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		)
    A			:	Optional[int]										=       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
SCREAMING_SNAKE_CASE_:Dict					=						Path(__file__).resolve().parent.parent.parent
SCREAMING_SNAKE_CASE_:Union[str, Any]					=						repo_dir / """model_cards"""

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







import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE_:Dict					=						logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_:int					=						{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

SCREAMING_SNAKE_CASE_:Tuple					=						{
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

SCREAMING_SNAKE_CASE_:Optional[int]					=						{"""facebook/blenderbot_small-90M""": 512}




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Dict:



    """simple docstring"""






    A			:	Optional[int]										=       set()
    A			:	Any										=       word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)		)
        A			:	List[Any]										=       char

    A			:	Optional[int]										=       set(_lowerCAmelCase		)
    return pairs






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			Union[str, Any] =				VOCAB_FILES_NAMES
    __lowerCamelCase      :			Any =				PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase      :			Optional[Any] =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase      :			int =				["input_ids", "attention_mask"]






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__="__start__",		lowerCamelCase__="__end__",		lowerCamelCase__="__unk__",		lowerCamelCase__="__null__",		**lowerCamelCase__,		):
        super().__init__(unk_token=lowerCamelCase__,		bos_token=lowerCamelCase__,		eos_token=lowerCamelCase__,		pad_token=lowerCamelCase__,		**lowerCamelCase__		)

        with open(lowerCamelCase__,		encoding="""utf-8"""		) as vocab_handle:
            A			:	Tuple										=       json.load(lowerCamelCase__		)
        A			:	Optional[Any]										=       {v: k for k, v in self.encoder.items()}
        with open(lowerCamelCase__,		encoding="""utf-8"""		) as merges_handle:
            A			:	str										=       merges_handle.read().split("""\n"""		)[1:-1]
        A			:	List[str]										=       [tuple(merge.split()		) for merge in merges]
        A			:	int										=       dict(zip(lowerCamelCase__,		range(len(lowerCamelCase__		)		)		)		)
        A			:	Optional[int]										=       {}






    @property
    def       _lowerCAmelCase (	self		):
        return len(self.encoder		)






    def       _lowerCAmelCase (	self		):
        return dict(self.encoder,		**self.added_tokens_encoder		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        if token in self.cache:
            return self.cache[token]
        A			:	Optional[int]										=       re.sub("""([.,!?()])""",		R""" \1""",		lowerCamelCase__		)
        A			:	List[Any]										=       re.sub("""(')""",		R""" \1 """,		lowerCamelCase__		)
        A			:	int										=       re.sub(R"""\s{2,}""",		""" """,		lowerCamelCase__		)
        if "\n" in token:
            A			:	Dict										=       token.replace("""\n""",		""" __newln__"""		)

        A			:	Tuple										=       token.split(""" """		)
        A			:	Union[str, Any]										=       []
        for token in tokens:
            if not len(lowerCamelCase__		):
                continue

            A			:	Optional[int]										=       token.lower()
            A			:	Optional[Any]										=       tuple(lowerCamelCase__		)
            A			:	int										=       tuple(list(word[:-1]		) + [word[-1] + """</w>"""]		)
            A			:	Tuple										=       get_pairs(lowerCamelCase__		)

            if not pairs:
                words.append(lowerCamelCase__		)
                continue

            while True:
                A			:	Any										=       min(lowerCamelCase__,		key=lambda lowerCamelCase__		: self.bpe_ranks.get(lowerCamelCase__,		float("""inf"""		)		)		)
                if bigram not in self.bpe_ranks:
                    break
                A					,							A			:	Any										=       bigram
                A			:	Optional[Any]										=       []
                A			:	Any										=       0

                while i < len(lowerCamelCase__		):
                    try:
                        A			:	List[str]										=       word.index(lowerCamelCase__,		lowerCamelCase__		)
                        new_word.extend(word[i:j]		)
                        A			:	Dict										=       j
                    except ValueError:
                        new_word.extend(word[i:]		)
                        break

                    if word[i] == first and i < len(lowerCamelCase__		) - 1 and word[i + 1] == second:
                        new_word.append(first + second		)
                        i += 2
                    else:
                        new_word.append(word[i]		)
                        i += 1
                A			:	Tuple										=       tuple(lowerCamelCase__		)
                A			:	int										=       new_word
                if len(lowerCamelCase__		) == 1:
                    break
                else:
                    A			:	Dict										=       get_pairs(lowerCamelCase__		)
            A			:	Any										=       """@@ """.join(lowerCamelCase__		)
            A			:	Dict										=       word[:-4]

            A			:	Union[str, Any]										=       word
            words.append(lowerCamelCase__		)
        return " ".join(lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        A			:	int										=       []

        A			:	Optional[Any]										=       re.findall(R"""\S+\n?""",		lowerCamelCase__		)

        for token in words:
            split_tokens.extend(list(self.bpe(lowerCamelCase__		).split(""" """		)		)		)
        return split_tokens






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        A			:	List[Any]										=       token.lower()
        return self.encoder.get(lowerCamelCase__,		self.encoder.get(self.unk_token		)		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        return self.decoder.get(lowerCamelCase__,		self.unk_token		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        A			:	Dict										=       """ """.join(lowerCamelCase__		).replace("""@@ """,		""""""		).strip()
        return out_string






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = None		):
        if not os.path.isdir(lowerCamelCase__		):
            logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
            return
        A			:	str										=       os.path.join(
            lowerCamelCase__,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)
        A			:	int										=       os.path.join(
            lowerCamelCase__,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]		)

        with open(lowerCamelCase__,		"""w""",		encoding="""utf-8"""		) as f:
            f.write(json.dumps(self.encoder,		indent=2,		sort_keys=lowerCamelCase__,		ensure_ascii=lowerCamelCase__		) + """\n"""		)

        A			:	str										=       0
        with open(lowerCamelCase__,		"""w""",		encoding="""utf-8"""		) as writer:
            writer.write("""#version: 0.2\n"""		)
            for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),		key=lambda lowerCamelCase__		: kv[1]		):
                if index != token_index:
                    logger.warning(
                        f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                        """ Please check that the tokenizer is not corrupted!"""		)
                    A			:	List[Any]										=       token_index
                writer.write(""" """.join(lowerCamelCase__		) + """\n"""		)
                index += 1

        return vocab_file, merge_file


