


'''simple docstring'''


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

import os
from pathlib import Path







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       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]
				_SCREAMING_SNAKE_CASE :       Optional[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"""],
				}
				_SCREAMING_SNAKE_CASE :       List[str]     		=      f"""{src_lang}-{tgt_lang}"""

				_SCREAMING_SNAKE_CASE :       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(SCREAMING_SNAKE_CASE__							,      exist_ok=SCREAMING_SNAKE_CASE__     )
				_SCREAMING_SNAKE_CASE :       Any     		=      os.path.join(SCREAMING_SNAKE_CASE__							,      """README.md"""     )
				print(f"""Generating {path}"""     )
				with open(SCREAMING_SNAKE_CASE__							,      """w"""							,      encoding="""utf-8"""     ) as f:
								f.write(SCREAMING_SNAKE_CASE__     )


# make sure we are under the root of the project
UpperCAmelCase_		:   Union[str, Any]							   =		Path(__file__).resolve().parent.parent.parent
UpperCAmelCase_		:   str							   =		repo_dir / 'model_cards'

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








'''simple docstring'''


from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Tuple     		=      []
				_SCREAMING_SNAKE_CASE :       Dict     		=      []
				_SCREAMING_SNAKE_CASE :       str     		=      []

				for rt in rc.restypes:
								_SCREAMING_SNAKE_CASE :       Optional[int]     		=      rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
								restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]     )
								_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      {name: i for i, name in enumerate(SCREAMING_SNAKE_CASE__     )}
								restype_atomaa_to_atomaa_list.append(
								    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]     )

								restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]     )

				# Add dummy mapping for restype 'UNK'
				restype_atomaa_to_atomaa_list.append([0] * 14     )
				restype_atomaa_to_atomaa_list.append([0] * 37     )
				restype_atomaa_mask_list.append([0.0] * 14     )

				_SCREAMING_SNAKE_CASE :       Dict     		=      torch.tensor(
				    SCREAMING_SNAKE_CASE__							,      dtype=torch.intaa							,      device=protein["""aatype"""].device							,      )
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      torch.tensor(
				    SCREAMING_SNAKE_CASE__							,      dtype=torch.intaa							,      device=protein["""aatype"""].device							,      )
				_SCREAMING_SNAKE_CASE :       str     		=      torch.tensor(
				    SCREAMING_SNAKE_CASE__							,      dtype=torch.floataa							,      device=protein["""aatype"""].device							,      )
				_SCREAMING_SNAKE_CASE :       int     		=      protein["""aatype"""].to(torch.long     )

				# create the mapping for (residx, atom14) --> atom37, i.e. an array
				# with shape (num_res, 14) containing the atom37 indices for this protein
				_SCREAMING_SNAKE_CASE :       List[str]     		=      restype_atomaa_to_atomaa[protein_aatype]
				_SCREAMING_SNAKE_CASE :       str     		=      restype_atomaa_mask[protein_aatype]

				_SCREAMING_SNAKE_CASE :       List[Any]     		=      residx_atomaa_mask
				_SCREAMING_SNAKE_CASE :       List[str]     		=      residx_atomaa_to_atomaa.long()

				# create the gather indices for mapping back
				_SCREAMING_SNAKE_CASE :       int     		=      restype_atomaa_to_atomaa[protein_aatype]
				_SCREAMING_SNAKE_CASE :       Dict     		=      residx_atomaa_to_atomaa.long()

				# create the corresponding mask
				_SCREAMING_SNAKE_CASE :       str     		=      torch.zeros([21, 37]							,      dtype=torch.floataa							,      device=protein["""aatype"""].device     )
				for restype, restype_letter in enumerate(rc.restypes     ):
								_SCREAMING_SNAKE_CASE :       int     		=      rc.restype_atoa[restype_letter]
								_SCREAMING_SNAKE_CASE :       Dict     		=      rc.residue_atoms[restype_name]
								for atom_name in atom_names:
												_SCREAMING_SNAKE_CASE :       List[Any]     		=      rc.atom_order[atom_name]
												_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      1

				_SCREAMING_SNAKE_CASE :       int     		=      restype_atomaa_mask[protein_aatype]
				_SCREAMING_SNAKE_CASE :       int     		=      residx_atomaa_mask

				return protein







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      tree_map(lambda SCREAMING_SNAKE_CASE__     : torch.tensor(SCREAMING_SNAKE_CASE__							,      device=batch["""aatype"""].device     )							,      SCREAMING_SNAKE_CASE__							,      np.ndarray     )
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      tensor_tree_map(lambda SCREAMING_SNAKE_CASE__     : np.array(SCREAMING_SNAKE_CASE__     )							,      make_atomaa_masks(SCREAMING_SNAKE_CASE__     )     )
				return out




