




from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


__A								=       "\\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n"

__A								=       "\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n"

__A								=       "\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n    predictions: list of predictions to score. Depending on the SuperGlUE subset:\n        - for 'record': list of question-answer dictionaries with the following keys:\n            - 'idx': index of the question as specified by the dataset\n            - 'prediction_text': the predicted answer text\n        - for 'multirc': list of question-answer dictionaries with the following keys:\n            - 'idx': index of the question-answer pair as specified by the dataset\n            - 'prediction': the predicted answer label\n        - otherwise: list of predicted labels\n    references: list of reference labels. Depending on the SuperGLUE subset:\n        - for 'record': list of question-answers dictionaries with the following keys:\n            - 'idx': index of the question as specified by the dataset\n            - 'answers': list of possible answers\n        - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n    - for 'record':\n        - 'exact_match': Exact match between answer and gold answer\n        - 'f1': F1 score\n    - for 'multirc':\n        - 'exact_match': Exact match between answer and gold answer\n        - 'f1_m': Per-question macro-F1 score\n        - 'f1_a': Average F1 score over all answers\n    - for 'axb':\n        'matthews_correlation': Matthew Correlation\n    - for 'cb':\n        - 'accuracy': Accuracy\n        - 'f1': F1 score\n    - for all others:\n        - 'accuracy': Accuracy\nExamples:\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'copa')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'record')\n    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]\n    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 1.0, 'f1': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')\n    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"







def      lowerCamelCase_						(			UpperCamelCase__							: Optional[int]					,	UpperCamelCase__							: Dict )	->			Tuple:





				"""simple docstring"""


				return float((preds == labels).mean() )







def      lowerCamelCase_						(			UpperCamelCase__							: Optional[Any]					,	UpperCamelCase__							: Optional[int]					,	UpperCamelCase__							: Optional[int]="binary" )	->			str:





				"""simple docstring"""


				__lowerCamelCase									=       simple_accuracy(UpperCamelCase__					,	UpperCamelCase__ )
				__lowerCamelCase									=       float(fa_score(y_true=UpperCamelCase__					,	y_pred=UpperCamelCase__					,	average=UpperCamelCase__ ) )
				return {
				    "accuracy": acc,
				    "f1": fa,
				}







def      lowerCamelCase_						(			UpperCamelCase__							: Dict					,	UpperCamelCase__							: List[str] )	->			Tuple:





				"""simple docstring"""


				__lowerCamelCase									=       {}
				for id_pred, label in zip(UpperCamelCase__					,	UpperCamelCase__ ):
								__lowerCamelCase									=       F"""{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"""
								__lowerCamelCase									=       id_pred['prediction']
								if question_id in question_map:
												question_map[question_id].append((pred, label) )
								else:
												__lowerCamelCase									=       [(pred, label)]
				__lowerCamelCase			,		__lowerCamelCase									=       [], []
				for question, preds_labels in question_map.items():
								__lowerCamelCase			,		__lowerCamelCase									=       zip(*UpperCamelCase__ )
								__lowerCamelCase									=       fa_score(y_true=UpperCamelCase__					,	y_pred=UpperCamelCase__					,	average='macro' )
								fas.append(UpperCamelCase__ )
								__lowerCamelCase									=       int(sum(pred == label for pred, label in preds_labels ) == len(UpperCamelCase__ ) )
								ems.append(UpperCamelCase__ )
				__lowerCamelCase									=       float(sum(UpperCamelCase__ ) / len(UpperCamelCase__ ) )
				__lowerCamelCase									=       sum(UpperCamelCase__ ) / len(UpperCamelCase__ )
				__lowerCamelCase									=       float(fa_score(y_true=UpperCamelCase__					,	y_pred=[id_pred['prediction'] for id_pred in ids_preds] ) )
				return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,							_KWARGS_DESCRIPTION					)
class        __lowerCAmelCase					(			datasets.Metric					):

			"""simple docstring"""







			def  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							if self.config_name not in [
							    "boolq",
							    "cb",
							    "copa",
							    "multirc",
							    "record",
							    "rte",
							    "wic",
							    "wsc",
							    "wsc.fixed",
							    "axb",
							    "axg",
							]:
											raise KeyError(
											    'You should supply a configuration name selected in '
											    '["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]'				)
							return datasets.MetricInfo(
							    description=_DESCRIPTION     ,		citation=_CITATION     ,		inputs_description=_KWARGS_DESCRIPTION     ,		features=datasets.Features(self._get_feature_types()				)     ,		codebase_urls=[]     ,		reference_urls=[]     ,		format='numpy' if not self.config_name == 'record' and not self.config_name == 'multirc' else None     ,		)


			def  lowercase_       ( self				)       -> Optional[int]:



							'''simple docstring'''
							if self.config_name == "record":
											return {
											    "predictions": {
											        "idx": {
											            "passage": datasets.Value('int64'				),
											            "query": datasets.Value('int64'				),
											        },
											        "prediction_text": datasets.Value('string'				),
											    },
											    "references": {
											        "idx": {
											            "passage": datasets.Value('int64'				),
											            "query": datasets.Value('int64'				),
											        },
											        "answers": datasets.Sequence(datasets.Value('string'				)				),
											    },
											}
							elif self.config_name == "multirc":
											return {
											    "predictions": {
											        "idx": {
											            "answer": datasets.Value('int64'				),
											            "paragraph": datasets.Value('int64'				),
											            "question": datasets.Value('int64'				),
											        },
											        "prediction": datasets.Value('int64'				),
											    },
											    "references": datasets.Value('int64'				),
											}
							else:
											return {
											    "predictions": datasets.Value('int64'				),
											    "references": datasets.Value('int64'				),
											}






			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> int:



							'''simple docstring'''
							if self.config_name == "axb":
											return {"matthews_correlation": matthews_corrcoef(lowerCamelCase__     ,		lowerCamelCase__				)}
							elif self.config_name == "cb":
											return acc_and_fa(lowerCamelCase__     ,		lowerCamelCase__     ,		fa_avg='macro'				)
							elif self.config_name == "record":
											__lowerCamelCase									=       [
											    {
											        'qas': [
											            {'id': ref['idx']['query'], 'answers': [{'text': ans} for ans in ref['answers']]}
											            for ref in references
											        ]
											    }
											]
											__lowerCamelCase									=       {pred['idx']['query']: pred['prediction_text'] for pred in predictions}
											return evaluate_record(lowerCamelCase__     ,		lowerCamelCase__				)[0]
							elif self.config_name == "multirc":
											return evaluate_multirc(lowerCamelCase__     ,		lowerCamelCase__				)
							elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
											return {"accuracy": simple_accuracy(lowerCamelCase__     ,		lowerCamelCase__				)}
							else:
											raise KeyError(
											    'You should supply a configuration name selected in '
											    '["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]'				)








# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path







def      lowerCamelCase_						(			UpperCamelCase__							: int					,	UpperCamelCase__							: Optional[int]					,	UpperCamelCase__							: int					,	UpperCamelCase__							: str )	->			List[str]:





				"""simple docstring"""


				__lowerCamelCase									=       {
				    '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]
				__lowerCamelCase									=       {
				    '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],
				}
				__lowerCamelCase									=       F"""{src_lang}-{tgt_lang}"""

				__lowerCamelCase									=       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__ )
				__lowerCamelCase									=       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								=       Path(__file__).resolve().parent.parent.parent
__A								=       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"]:
					__A								=       model_cards_dir / "allenai" / model_name
					write_model_card(model_card_dir, src_lang="en", tgt_lang="de", model_name=model_name)


