




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

import os
from pathlib import Path



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Optional[int]						,							_lowerCAmelCase							:   Any						,							_lowerCAmelCase							:   List[Any]	):




 """simple docstring"""







 UpperCAmelCase__							      =						{
     "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]
 UpperCAmelCase__							      =						{
     "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"],
 }
 UpperCAmelCase__							      =						F'''{src_lang}-{tgt_lang}'''

 UpperCAmelCase__							      =						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(_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						:   Union[str, Any]		=   Path(__file__).resolve().parent.parent.parent
_lowerCAmelCase						:   Tuple		=   repo_dir / "model_cards"

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











import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import ASTForAudioClassification, ASTModel
  from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
      AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  )


if is_torchaudio_available():
  import torchaudio

  from transformers import ASTFeatureExtractor
class 			_UpperCamelCase      :
      def __init__(			self   :Any ,      lowerCamelCase   :List[str] ,      lowerCamelCase   :Optional[int]=13 ,      lowerCamelCase   :Optional[Any]=2 ,      lowerCamelCase   :Any=24 ,      lowerCamelCase   :Union[str, Any]=16 ,      lowerCamelCase   :Any=True ,      lowerCamelCase   :int=True ,      lowerCamelCase   :Optional[Any]=32 ,      lowerCamelCase   :Union[str, Any]=5 ,      lowerCamelCase   :Tuple=4 ,      lowerCamelCase   :Optional[Any]=37 ,      lowerCamelCase   :Optional[Any]="gelu" ,      lowerCamelCase   :int=0.1 ,      lowerCamelCase   :Tuple=0.1 ,      lowerCamelCase   :List[str]=10 ,      lowerCamelCase   :Optional[Any]=0.02 ,      lowerCamelCase   :Optional[int]=None ,      lowerCamelCase   :Optional[Any]=2 ,      lowerCamelCase   :List[Any]=2 ,      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						parent
       UpperCAmelCase__							 =						batch_size
       UpperCAmelCase__							 =						patch_size
       UpperCAmelCase__							 =						max_length
       UpperCAmelCase__							 =						num_mel_bins
       UpperCAmelCase__							 =						is_training
       UpperCAmelCase__							 =						use_labels
       UpperCAmelCase__							 =						hidden_size
       UpperCAmelCase__							 =						num_hidden_layers
       UpperCAmelCase__							 =						num_attention_heads
       UpperCAmelCase__							 =						intermediate_size
       UpperCAmelCase__							 =						hidden_act
       UpperCAmelCase__							 =						hidden_dropout_prob
       UpperCAmelCase__							 =						attention_probs_dropout_prob
       UpperCAmelCase__							 =						type_sequence_label_size
       UpperCAmelCase__							 =						initializer_range
       UpperCAmelCase__							 =						scope
       UpperCAmelCase__							 =						frequency_stride
       UpperCAmelCase__							 =						time_stride

       # in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
       UpperCAmelCase__							 =						(self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
       UpperCAmelCase__							 =						(self.max_length - self.patch_size) // self.time_stride + 1
       UpperCAmelCase__							 =						frequency_out_dimension * time_out_dimension
       UpperCAmelCase__							 =						num_patches + 2
      def 	UpperCAmelCase_						(			self   :int      )     ->			List[str]:
       UpperCAmelCase__							 =						floats_tensor([self.batch_size, self.max_length, self.num_mel_bins]      )

       UpperCAmelCase__							 =						None
       if self.use_labels:
        UpperCAmelCase__							 =						ids_tensor([self.batch_size] ,      self.type_sequence_label_size      )

       UpperCAmelCase__							 =						self.get_config()

       return config, input_values, labels
      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			Any:
       return ASTConfig(
           patch_size=self.patch_size ,      max_length=self.max_length ,      num_mel_bins=self.num_mel_bins ,      hidden_size=self.hidden_size ,      num_hidden_layers=self.num_hidden_layers ,      num_attention_heads=self.num_attention_heads ,      intermediate_size=self.intermediate_size ,      hidden_act=self.hidden_act ,      hidden_dropout_prob=self.hidden_dropout_prob ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob ,      is_decoder=lowerCamelCase ,      initializer_range=self.initializer_range ,      frequency_stride=self.frequency_stride ,      time_stride=self.time_stride ,      )
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :List[str] ,      lowerCamelCase   :List[str] ,      lowerCamelCase   :List[str]      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						ASTModel(config=lowerCamelCase      )
       model.to(lowerCamelCase      )
       model.eval()
       UpperCAmelCase__							 =						model(lowerCamelCase      )
       self.parent.assertEqual(result.last_hidden_state.shape ,      (self.batch_size, self.seq_length, self.hidden_size)      )







      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			str:
       UpperCAmelCase__							 =						self.prepare_config_and_inputs()
       (
           (
           UpperCAmelCase__
       ) , (
           UpperCAmelCase__
       ) , (
           UpperCAmelCase__
       ) , 
       )							 =						config_and_inputs
       UpperCAmelCase__							 =						{"input_values": input_values}
       return config, inputs_dict



@require_torch
class 			_UpperCamelCase      (				lowerCAmelCase	,       lowerCAmelCase	,       unittest.TestCase		):
      UpperCAmelCase_        =		(
          (
              ASTModel,
              ASTForAudioClassification,
          )
          if is_torch_available()
          else ()
      )
      UpperCAmelCase_        =		(
          {"""audio-classification""": ASTForAudioClassification, """feature-extraction""": ASTModel}
          if is_torch_available()
          else {}
      )
      UpperCAmelCase_        =		False
      UpperCAmelCase_        =		False
      UpperCAmelCase_        =		False
      UpperCAmelCase_        =		False
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :Optional[int] ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :str ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :int      )     ->			str:
       if pipeline_test_casse_name == "AudioClassificationPipelineTests":
        return True

       return False
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			int:
       UpperCAmelCase__							 =						ASTModelTester(self      )
       UpperCAmelCase__							 =						ConfigTester(self ,      config_class=lowerCamelCase ,      has_text_modality=lowerCamelCase ,      hidden_size=37      )
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			str:
       self.config_tester.run_common_tests()
      @unittest.skip(reason="AST does not use inputs_embeds"      )
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			Optional[Any]:
       pass
      def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			Any:
       UpperCAmelCase__ , UpperCAmelCase__							 =						self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        UpperCAmelCase__							 =						model_class(lowerCamelCase      )
        self.assertIsInstance(model.get_input_embeddings() ,      (nn.Module)      )
        UpperCAmelCase__							 =						model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(lowerCamelCase ,      nn.Linear      )      )
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			List[str]:
       UpperCAmelCase__ , UpperCAmelCase__							 =						self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        UpperCAmelCase__							 =						model_class(lowerCamelCase      )
        UpperCAmelCase__							 =						inspect.signature(model.forward      )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        UpperCAmelCase__							 =						[*signature.parameters.keys()]

        UpperCAmelCase__							 =						["input_values"]
        self.assertListEqual(arg_names[:1] ,      lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :int      )     ->			Any:
       UpperCAmelCase__							 =						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase      )







      @slow
      def 	UpperCAmelCase_						(			self   :int      )     ->			Optional[Any]:
       for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCAmelCase__							 =						ASTModel.from_pretrained(lowerCamelCase      )
        self.assertIsNotNone(lowerCamelCase      )



def 						lowerCAmelCase		(		):




 """simple docstring"""







 UpperCAmelCase__							 =						hf_hub_download(
     repo_id="nielsr/audio-spectogram-transformer-checkpoint"						,							filename="sample_audio.flac"						,							repo_type="dataset"	)

 UpperCAmelCase__ , UpperCAmelCase__							 =						torchaudio.load(_lowerCAmelCase	)

 return audio, sampling_rate


@require_torch
@require_torchaudio
class 			_UpperCamelCase      (				unittest.TestCase		):
      @cached_property
      def 	UpperCAmelCase_						(			self   :str      )     ->			Dict:
       return (
           ASTFeatureExtractor.from_pretrained("MIT/ast-finetuned-audioset-10-10-0.4593"      )
           if is_torchaudio_available()
           else None
       )







      @slow
      def 	UpperCAmelCase_						(			self   :str      )     ->			Optional[int]:
       UpperCAmelCase__							 =						self.default_feature_extractor
       UpperCAmelCase__							 =						ASTForAudioClassification.from_pretrained("MIT/ast-finetuned-audioset-10-10-0.4593"      ).to(lowerCamelCase      )

       UpperCAmelCase__							 =						self.default_feature_extractor
       UpperCAmelCase__ , UpperCAmelCase__							 =						prepare_audio()
       UpperCAmelCase__							 =						audio.squeeze().numpy()
       UpperCAmelCase__							 =						feature_extractor(lowerCamelCase ,      sampling_rate=lowerCamelCase ,      return_tensors="pt"      ).to(lowerCamelCase      )

       # forward pass
       with torch.no_grad():
        UpperCAmelCase__							 =						model(**lowerCamelCase      )

       # verify the logits
       UpperCAmelCase__							 =						torch.Size((1, 527)      )
       self.assertEqual(outputs.logits.shape ,      lowerCamelCase      )

       UpperCAmelCase__							 =						torch.tensor([-0.87_60, -7.00_42, -8.66_02]      ).to(lowerCamelCase      )

       self.assertTrue(torch.allclose(outputs.logits[0, :3] ,      lowerCamelCase ,      atol=1e-4      )      )





