






"""simple docstring"""







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

import os
from pathlib import Path






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       UpperCAmelCase					=	{
           """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]
       UpperCAmelCase					=	{
           """wmt19-de-en-6-6-base""": [0, 38.37],
           """wmt19-de-en-6-6-big""": [0, 39.90],
       }
       UpperCAmelCase					=	F'''{src_lang}-{tgt_lang}'''

       UpperCAmelCase					=	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=lowerCAmelCase		,					exist_ok=lowerCAmelCase						)
       UpperCAmelCase					=	os.path.join(lowerCAmelCase		,					"""README.md"""						)
       print(F'''Generating {path}'''						)
       with open(lowerCAmelCase		,					"""w"""		,					encoding="""utf-8"""						) as f:
              f.write(lowerCAmelCase						)


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

for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
 lowerCAmelCase_		:			Union[str, Any]				   =					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 collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available






def 					_lowerCAmelCase				(					lowerCAmelCase						):







       '''simple docstring'''
       if is_torch_available():
              import torch

              if isinstance(lowerCAmelCase		,					torch.Tensor						):
                     return True

       return isinstance(lowerCAmelCase		,					np.ndarray						)





class      UpperCamelCase_       (				a_     ):



  def 						UpperCamelCase_ (					self )	->	Dict:



         """simple docstring"""





         UpperCAmelCase					=	fields(self )

         # Safety and consistency checks
         if not len(snake_case__ ):
                raise ValueError(f'''{self.__class__.__name__} has no fields.''' )

         UpperCAmelCase					=	getattr(self       ,						class_fields[0].name )
         UpperCAmelCase					=	all(getattr(self       ,						field.name ) is None for field in class_fields[1:] )

         if other_fields_are_none and isinstance(snake_case__       ,						snake_case__ ):
                for key, value in first_field.items():
                       UpperCAmelCase					=	value
         else:
                for field in class_fields:
                       UpperCAmelCase					=	getattr(self       ,						field.name )
                       if v is not None:
                              UpperCAmelCase					=	v



  def __delitem__(					self       ,						*snake_case__       ,						**snake_case__ )	->	Optional[int]:



         """simple docstring"""





         raise Exception(f'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.''' )



  def 						UpperCamelCase_ (					self       ,						*snake_case__       ,						**snake_case__ )	->	List[str]:



         """simple docstring"""





         raise Exception(f'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.''' )



  def 						UpperCamelCase_ (					self       ,						*snake_case__       ,						**snake_case__ )	->	Optional[int]:



         """simple docstring"""





         raise Exception(f'''You cannot use ``pop`` on a {self.__class__.__name__} instance.''' )



  def 						UpperCamelCase_ (					self       ,						*snake_case__       ,						**snake_case__ )	->	Optional[int]:



         """simple docstring"""





         raise Exception(f'''You cannot use ``update`` on a {self.__class__.__name__} instance.''' )



  def __getitem__(					self       ,						snake_case__ )	->	List[Any]:



         """simple docstring"""





         if isinstance(snake_case__       ,						snake_case__ ):
                UpperCAmelCase					=	dict(self.items() )
                return inner_dict[k]
         else:
                return self.to_tuple()[k]



  def __setattr__(					self       ,						snake_case__       ,						snake_case__ )	->	Union[str, Any]:



         """simple docstring"""





         if name in self.keys() and value is not None:
                # Don't call self.__setitem__ to avoid recursion errors
                super().__setitem__(snake_case__       ,						snake_case__ )
         super().__setattr__(snake_case__       ,						snake_case__ )



  def __setitem__(					self       ,						snake_case__       ,						snake_case__ )	->	Tuple:



         """simple docstring"""





         super().__setitem__(snake_case__       ,						snake_case__ )
         # Don't call self.__setattr__ to avoid recursion errors
         super().__setattr__(snake_case__       ,						snake_case__ )



  def 						UpperCamelCase_ (					self )	->	Tuple[Any]:



         """simple docstring"""





         return tuple(self[k] for k in self.keys() )
