






import os
from pathlib import Path

def        _lowerCamelCase		(  __A      :							str						,						__A      :							Any						,						__A      :							str						,						__A      :							Optional[Any]	) ->   str:
    _UpperCAmelCase			: 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]
    _UpperCAmelCase			: str     					=			{
        '''wmt19-de-en-6-6-base''': [0, 38.37],
        '''wmt19-de-en-6-6-big''': [0, 39.90],
    }
    _UpperCAmelCase			: Optional[Any]     					=			f'''{src_lang}-{tgt_lang}'''

    _UpperCAmelCase			: Optional[Any]     					=			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=snake_case__						,						exist_ok=snake_case__	)
    _UpperCAmelCase			: Optional[int]     					=			os.path.join(snake_case__						,						'''README.md'''	)
    print(f'''Generating {path}'''	)
    with open(snake_case__						,						'''w'''						,						encoding='''utf-8'''	) as f:
        f.write(snake_case__	)


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












from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class 							A_      (  __lowercase     ):






 '''simple docstring'''




 def __init__(		self							,					_A							,					_A)	->						Dict:

     """simple docstring"""



     super().__init__()
     self.register_modules(unet=_A							,					scheduler=_A)


 @torch.no_grad()
 def __call__(		self							,					_A = 1							,					_A = None							,					_A = 1000							,					_A = "pil"							,					_A = True							,					)	->						Union[ImagePipelineOutput, Tuple]:

     """simple docstring"""



     if isinstance(self.unet.config.sample_size							,					_A):
         _UpperCAmelCase			: List[str]     					=			(
             batch_size,
             self.unet.config.in_channels,
             self.unet.config.sample_size,
             self.unet.config.sample_size,
         )
     else:
         _UpperCAmelCase			: Union[str, Any]     					=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

     if self.device.type == "mps":
         # randn does not work reproducibly on mps
         _UpperCAmelCase			: int     					=			randn_tensor(_A							,					generator=_A)
         _UpperCAmelCase			: Tuple     					=			image.to(self.device)
     else:
         _UpperCAmelCase			: Any     					=			randn_tensor(_A							,					generator=_A							,					device=self.device)

     # set step values
     self.scheduler.set_timesteps(_A)

     for t in self.progress_bar(self.scheduler.timesteps):
         # 1. predict noise model_output
         _UpperCAmelCase			: Optional[Any]     					=			self.unet(_A							,					_A).sample

         # 2. compute previous image: x_t -> x_t-1
         _UpperCAmelCase			: Dict     					=			self.scheduler.step(_A							,					_A							,					_A							,					generator=_A).prev_sample

     _UpperCAmelCase			: Optional[int]     					=			(image / 2 + 0.5).clamp(0							,					1)
     _UpperCAmelCase			: int     					=			image.cpu().permute(0							,					2							,					3							,					1).numpy()
     if output_type == "pil":
         _UpperCAmelCase			: str     					=			self.numpy_to_pil(_A)

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=_A)




