



"""simple docstring"""







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

import os
from pathlib import Path

def       _lowerCamelCase				(							UpperCAmelCase_ :       Any,	UpperCAmelCase_ :       Any,	UpperCAmelCase_ :       List[Any],	UpperCAmelCase_ :       List[str]     )	-> int:






	"""simple docstring"""





	A__											=					{
	    "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]
	A__											=					{
	    "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],
	}
	A__											=					F"""{src_lang}-{tgt_lang}"""

	A__											=					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_     )
	A__											=					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
UpperCamelCase     =							Path(__file__).resolve().parent.parent.parent
UpperCamelCase     =							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"]:
			UpperCamelCase     =							model_cards_dir / """allenai""" / model_name
			write_model_card(model_card_dir, src_lang="""en""", tgt_lang="""de""", model_name=model_name)





'''simple docstring'''


import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel






class     A ( pl.LightningModule ):





							def __init__(   self					:				Dict  ,		__a					:				List[str]						)				->						Tuple:
								super().__init__()
								__UpperCAmelCase      					=      model
								__UpperCAmelCase      					=      2
								__UpperCAmelCase      					=      nn.Linear(self.model.config.hidden_size  ,		self.num_labels						)






							def    snake_case__    (   self					:				int						)				->						int:
								pass





def    lowerCAmelCase       (		UpperCamelCase__					: str     ,       UpperCamelCase__					: str     ,       UpperCamelCase__					: str							):


	"""simple docstring"""
	# load longformer model from model identifier
	__UpperCAmelCase      					=      LongformerModel.from_pretrained(UpperCamelCase__							)
	__UpperCAmelCase      					=      LightningModel(UpperCamelCase__							)

	__UpperCAmelCase      					=      torch.load(UpperCamelCase__     ,       map_location=torch.device('''cpu'''							)							)
	lightning_model.load_state_dict(ckpt['''state_dict''']							)

	# init longformer question answering model
	__UpperCAmelCase      					=      LongformerForQuestionAnswering.from_pretrained(UpperCamelCase__							)

	# transfer weights
	longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()							)
	longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()							)
	longformer_for_qa.eval()

	# save model
	longformer_for_qa.save_pretrained(UpperCamelCase__							)

	print(f"""Conversion successful. Model saved under {pytorch_dump_folder_path}"""							)


if __name__ == "__main__":
					__lowerCAmelCase						:							Optional[Any]    							=						argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--longformer_model",
					    default=None,
					    type=str,
					    required=True,
					    help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
					)
					parser.add_argument(
					    "--longformer_question_answering_ckpt_path",
					    default=None,
					    type=str,
					    required=True,
					    help="Path the official PyTorch Lightning Checkpoint.",
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
					)
					__lowerCAmelCase						:							List[str]    							=						parser.parse_args()
					convert_longformer_qa_checkpoint_to_pytorch(
					    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
					)
