
"""simple docstring"""

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

import os
from pathlib import Path
def 		UpperCAmelCase					(		a_,						a_,						a_,						a_			):





		'''simple docstring'''


		lowerCamelCase     :			Optional[int]		=    {
		    '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     :			Any		=    {
		    'wmt19-de-en-6-6-base': [0, 3_8.3_7],
		    'wmt19-de-en-6-6-big': [0, 3_9.9_0],
		}
		lowerCamelCase     :			int		=    F"""{src_lang}-{tgt_lang}"""

		lowerCamelCase     :			Optional[int]		=    F"""
---

language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- allenai
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt19 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).

2 models are available:

* [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)
* [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)


## 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   |  transformers
-------|---------
{model_name}  |  {scores[model_name][1]}

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 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 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/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)


### 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=a_,						exist_ok=a_			)
		lowerCamelCase     :			Union[str, Any]		=    os.path.join(a_,						'README.md'			)
		print(F"""Generating {path}"""			)
		with open(a_,						'w',						encoding='utf-8'			) as f:
				f.write(a_			)


# 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 ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
					_A            = model_cards_dir / 'allenai' / model_name
					write_model_card(model_card_dir, src_lang='de', tgt_lang='en', model_name=model_name)








"""simple docstring"""

from transformers import (
    DPRContextEncoderTokenizer,
    DPRContextEncoderTokenizerFast,
    DPRQuestionEncoderTokenizer,
    DPRQuestionEncoderTokenizerFast,
    DPRReaderOutput,
    DPRReaderTokenizer,
    DPRReaderTokenizerFast,
)
from transformers.testing_utils import require_tokenizers, slow
from transformers.tokenization_utils_base import BatchEncoding

from ..bert.test_tokenization_bert import BertTokenizationTest






@require_tokenizers
class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	DPRContextEncoderTokenizer
	lowercase_        =	DPRContextEncoderTokenizerFast
	lowercase_        =	True






@require_tokenizers
class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	DPRQuestionEncoderTokenizer
	lowercase_        =	DPRQuestionEncoderTokenizerFast
	lowercase_        =	True






@require_tokenizers
class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	DPRReaderTokenizer
	lowercase_        =	DPRReaderTokenizerFast
	lowercase_        =	True




	@slow
	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase     :			str		=    self.tokenizer_class.from_pretrained('bert-base-uncased'					)

			lowerCamelCase     :			int		=    tokenizer.encode('question sequence'		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			List[Any]		=    tokenizer.encode('title sequence'		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			Optional[int]		=    tokenizer.encode('text sequence ' * 4		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			Union[str, Any]		=    [[101] + text_a + [102] + text_a + [102] + text_a]
			lowerCamelCase     :			List[str]		=    BatchEncoding({'input_ids': input_ids}					)

			lowerCamelCase     :			Optional[int]		=    [[0] * len(input_ids[0]					)]
			lowerCamelCase     :			str		=    [[0] * len(input_ids[0]					)]
			lowerCamelCase     :			List[Any]		=    [0]
			lowerCamelCase     :			Tuple		=    DPRReaderOutput(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)

			lowerCamelCase	,    lowerCamelCase     :			Optional[int]		=    8, 9
			lowerCamelCase     :			Optional[int]		=    10
			lowerCamelCase     :			int		=    10
			lowerCamelCase     :			List[Any]		=    tokenizer.decode_best_spans(UpperCAmelCase_		, UpperCAmelCase_					)
			self.assertEqual(predicted_spans[0].start_index		, UpperCAmelCase_					)
			self.assertEqual(predicted_spans[0].end_index		, UpperCAmelCase_					)
			self.assertEqual(predicted_spans[0].doc_id		, 0					)




	@slow
	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			lowerCamelCase     :			Union[str, Any]		=    self.tokenizer_class.from_pretrained('bert-base-uncased'					)

			lowerCamelCase     :			List[str]		=    tokenizer.encode('question sequence'		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			List[str]		=    tokenizer.encode('title sequence'		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			List[Any]		=    tokenizer.encode('text sequence'		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			List[Any]		=    [101] + text_a + [102] + text_a + [102] + text_a
			lowerCamelCase     :			Optional[Any]		=    tokenizer(questions=['question sequence']		, titles=['title sequence']		, texts=['text sequence']					)
			self.assertIn('input_ids'		, UpperCAmelCase_					)
			self.assertIn('attention_mask'		, UpperCAmelCase_					)
			self.assertListEqual(encoded_input['input_ids'][0]		, UpperCAmelCase_					)






