



import os
from pathlib import Path
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Dict											= {
        """en""": """Machine learning is great, isn't it?""",
        """ru""": """Машинное обучение - это здорово, не так ли?""",
        """de""": """Maschinelles Lernen ist großartig, oder?""",
    }

    # BLUE scores as follows:
    # "pair": [fairseq, transformers]
    lowerCAmelCase__			:  Optional[Any]											= {
        """ru-en""": ["""[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)""", """39.20"""],
        """en-ru""": ["""[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)""", """33.47"""],
        """en-de""": ["""[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)""", """42.83"""],
        """de-en""": ["""[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)""", """41.35"""],
    }
    lowerCAmelCase__			:  Any											= f"""{src_lang}-{tgt_lang}"""

    lowerCAmelCase__			:  str											= f"""
---
language: 
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.

For more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).

The abbreviation FSMT stands for FairSeqMachineTranslation

All four models are available:

* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)

## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = \"facebook/wmt19-{src_lang}-{tgt_lang}\"
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

- The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)

## Training data

Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).

## Eval results

pair   | fairseq | transformers
-------|---------|----------
{pair}  | {scores[pair][0]} | {scores[pair][1]}

The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:
- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
- re-ranking

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=15
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 $PAIR
PYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $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
```
note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.

## 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

```bibtex
@inproceedings{{...,
  year={{2020}},
  title={{Facebook FAIR's WMT19 News Translation Task Submission}},
  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},
  booktitle={{Proc. of WMT}},
}}
```


## TODO

- port model ensemble (fairseq uses 4 model checkpoints)

"""
    os.makedirs(UpperCamelCase							,   exist_ok=UpperCamelCase      )
    lowerCAmelCase__			:  List[Any]											= 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
_lowerCAmelCase										=     Path(__file__).resolve().parent.parent.parent
_lowerCAmelCase										=     repo_dir / '''model_cards'''

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
 _lowerCAmelCase						,      _lowerCAmelCase						,      _lowerCAmelCase										=     model_name.split('''-''')
 _lowerCAmelCase										=     model_cards_dir / '''facebook''' / model_name
 write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)







'''simple docstring'''





import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()






class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				Optional[Any]           =				VQModel
  __lowercase							:				Tuple           =				'''sample'''




  @property
  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase=(32, 32)				)   ->		List[Any]:
      lowerCAmelCase__			:  Dict											= 4
      lowerCAmelCase__			:  List[str]											= 3

      lowerCAmelCase__			:  Dict											= floats_tensor((batch_size, num_channels) + sizes				).to(__UpperCAmelCase				)

      return {"sample": image}




  @property
  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      return (3, 32, 32)




  @property
  def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
      return (3, 32, 32)




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      lowerCAmelCase__			:  Dict											= {
          """block_out_channels""": [32, 64],
          """in_channels""": 3,
          """out_channels""": 3,
          """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
          """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
          """latent_channels""": 3,
      }
      lowerCAmelCase__			:  Any											= self.dummy_input
      return init_dict, inputs_dict




  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      pass




  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      pass




  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      lowerCAmelCase__				,       lowerCAmelCase__			:  Union[str, Any]											= VQModel.from_pretrained("""fusing/vqgan-dummy"""  ,output_loading_info=__UpperCAmelCase				)
      self.assertIsNotNone(__UpperCAmelCase				)
      self.assertEqual(len(loading_info["""missing_keys"""]				)  ,0				)

      model.to(__UpperCAmelCase				)
      lowerCAmelCase__			:  List[Any]											= model(**self.dummy_input				)

      assert image is not None, "Make sure output is not None"






  def 							UpperCAmelCase_     (   self				)   ->		List[str]:
      lowerCAmelCase__			:  Tuple											= VQModel.from_pretrained("""fusing/vqgan-dummy"""				)
      model.to(__UpperCAmelCase				).eval()

      torch.manual_seed(0				)
      if torch.cuda.is_available():
          torch.cuda.manual_seed_all(0				)

      lowerCAmelCase__			:  List[str]											= torch.randn(1  ,model.config.in_channels  ,model.config.sample_size  ,model.config.sample_size				)
      lowerCAmelCase__			:  Optional[Any]											= image.to(__UpperCAmelCase				)
      with torch.no_grad():
          lowerCAmelCase__			:  Optional[Any]											= model(__UpperCAmelCase				).sample

      lowerCAmelCase__			:  Tuple											= output[0, -1, -3:, -3:].flatten().cpu()
      # fmt: off
      lowerCAmelCase__			:  Tuple											= torch.tensor([-0.0_1_5_3, -0.4_0_4_4, -0.1_8_8_0, -0.5_1_6_1, -0.2_4_1_8, -0.4_0_7_2, -0.1_6_1_2, -0.0_6_3_3, -0.0_1_4_3]				)
      # fmt: on
      self.assertTrue(torch.allclose(__UpperCAmelCase  ,__UpperCAmelCase  ,atol=1E-3				)				)


