"""simple docstring"""
# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path





def    lowerCAmelCase__    (			_UpperCamelCase       :				List[Any] ,			_UpperCamelCase       :				Any ,			_UpperCamelCase       :				Union[str, Any] ,			_UpperCamelCase       :				Dict					)					->				Tuple:





						"""simple docstring"""






						snake_case								=			{
						    'en': 'Machine learning is great, isn\'t it?',
						    'ru': 'Машинное обучение - это здорово, не так ли?',
						    'de': 'Maschinelles Lernen ist großartig, nicht wahr?',
						}

						# BLUE scores as follows:
						# "pair": [fairseq, transformers]
						snake_case								=			{
						    'wmt16-en-de-dist-12-1': [28.3, 27.52],
						    'wmt16-en-de-dist-6-1': [27.4, 27.11],
						    'wmt16-en-de-12-1': [26.9, 25.75],
						}
						snake_case								=			f"""{src_lang}-{tgt_lang}"""

						snake_case								=			f"""
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt16
- allenai
license: apache-2.0
datasets:
- wmt16
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.

For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).

All 3 models are available:

* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)


## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = \"allenai/{model_name}\"
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


## Training data

Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).

## Eval results

Here are the BLEU scores:

model   | fairseq | transformers
-------|---------|----------
{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}

The score is slightly below the score reported in the paper, as the researchers don't use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.

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=5
mkdir -p $DATA_DIR
sacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py allenai/{model_name} $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
```

## Data Sources

- [training, etc.](http://www.statmt.org/wmt16/)
- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)


### BibTeX entry and citation info

```
@misc{{kasai2020deep,
    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},
    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},
    year={{2020}},
    eprint={{2006.10369}},
    archivePrefix={{arXiv}},
    primaryClass={{cs.CL}}
}}
```

"""
						model_card_dir.mkdir(parents=_UpperCamelCase ,			exist_ok=_UpperCamelCase					)
						snake_case								=			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
SCREAMING_SNAKE_CASE__      						=  Path(__file__).resolve().parent.parent.parent
SCREAMING_SNAKE_CASE__      						=  repo_dir / "model_cards"

for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
					SCREAMING_SNAKE_CASE__      						=  model_cards_dir / "allenai" / model_name
					write_model_card(model_card_dir, src_lang="en", tgt_lang="de", model_name=model_name)



"""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    lowerCAmelCase__    (			_UpperCamelCase       :				Dict[str, torch.Tensor]					)					->				Dict[str, torch.Tensor]:





						"""simple docstring"""






						snake_case								=			[]
						snake_case								=			[]
						snake_case								=			[]

						for rt in rc.restypes:
												snake_case								=			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]					)
												snake_case								=			{name: i for i, name in enumerate(_UpperCamelCase					)}
												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] * 1_4					)
						restype_atomaa_to_atomaa_list.append([0] * 3_7					)
						restype_atomaa_mask_list.append([0.0] * 1_4					)

						snake_case								=			torch.tensor(
						    _UpperCamelCase ,			dtype=torch.intaa ,			device=protein['aatype'].device ,			)
						snake_case								=			torch.tensor(
						    _UpperCamelCase ,			dtype=torch.intaa ,			device=protein['aatype'].device ,			)
						snake_case								=			torch.tensor(
						    _UpperCamelCase ,			dtype=torch.floataa ,			device=protein['aatype'].device ,			)
						snake_case								=			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
						snake_case								=			restype_atomaa_to_atomaa[protein_aatype]
						snake_case								=			restype_atomaa_mask[protein_aatype]

						snake_case								=			residx_atomaa_mask
						snake_case								=			residx_atomaa_to_atomaa.long()

						# create the gather indices for mapping back
						snake_case								=			restype_atomaa_to_atomaa[protein_aatype]
						snake_case								=			residx_atomaa_to_atomaa.long()

						# create the corresponding mask
						snake_case								=			torch.zeros([2_1, 3_7] ,			dtype=torch.floataa ,			device=protein['aatype'].device					)
						for restype, restype_letter in enumerate(rc.restypes					):
												snake_case								=			rc.restype_atoa[restype_letter]
												snake_case								=			rc.residue_atoms[restype_name]
												for atom_name in atom_names:
																		snake_case								=			rc.atom_order[atom_name]
																		snake_case								=			1

						snake_case								=			restype_atomaa_mask[protein_aatype]
						snake_case								=			residx_atomaa_mask

						return protein





def    lowerCAmelCase__    (			_UpperCamelCase       :				Dict[str, torch.Tensor]					)					->				Dict[str, np.ndarray]:





						"""simple docstring"""






						snake_case								=			tree_map(lambda _UpperCamelCase					: torch.tensor(_UpperCamelCase ,			device=batch['aatype'].device					) ,			_UpperCamelCase ,			np.ndarray					)
						snake_case								=			tensor_tree_map(lambda _UpperCamelCase					: np.array(_UpperCamelCase					) ,			make_atomaa_masks(_UpperCamelCase					)					)
						return out


