

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

import os
from pathlib import Path

def       __snake_case						( __UpperCamelCase	: str				,__UpperCamelCase	: str				,__UpperCamelCase	: List[str]    ):



      """simple docstring"""

      A_						  =				{
          "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]
      A_						  =				{
          "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"],
      }
      A_						  =				f'''{src_lang}-{tgt_lang}'''

      A_						  =				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    )
      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
__a    :Optional[Any]      =			Path(__file__).resolve().parent.parent.parent
__a    :Optional[Any]      =			repo_dir / 'model_cards'

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

"""simple docstring"""
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		(DDIMParallelScheduler,)
							_snake_case						      =		(('''eta''', 0.0), ('''num_inference_steps''', 5_0))




							def    A__						( self    ,				**snake_case_				)       ->  Optional[Any]:
													__lowerCAmelCase     =  {
													    """num_train_timesteps""": 1_000,
													    """beta_start""": 0.0_001,
													    """beta_end""": 0.02,
													    """beta_schedule""": """linear""",
													    """clip_sample""": True,
													}

													config.update(**snake_case_				)
													return config




							def    A__						( self    ,				**snake_case_				)       ->  Tuple:
													__lowerCAmelCase     =  self.scheduler_classes[0]
													__lowerCAmelCase     =  self.get_scheduler_config(**snake_case_				)
													__lowerCAmelCase     =  scheduler_class(**snake_case_				)

													__lowerCAmelCase   ,     __lowerCAmelCase     =  10, 0.0

													__lowerCAmelCase     =  self.dummy_model()
													__lowerCAmelCase     =  self.dummy_sample_deter

													scheduler.set_timesteps(snake_case_				)

													for t in scheduler.timesteps:
																			__lowerCAmelCase     =  model(snake_case_    ,				snake_case_				)
																			__lowerCAmelCase     =  scheduler.step(snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_				).prev_sample

													return sample




							def    A__						( self				)       ->  List[str]:
													for timesteps in [100, 500, 1_000]:
																			self.check_over_configs(num_train_timesteps=snake_case_				)




							def    A__						( self				)       ->  Tuple:
													for steps_offset in [0, 1]:
																			self.check_over_configs(steps_offset=snake_case_				)

													__lowerCAmelCase     =  self.scheduler_classes[0]
													__lowerCAmelCase     =  self.get_scheduler_config(steps_offset=1				)
													__lowerCAmelCase     =  scheduler_class(**snake_case_				)
													scheduler.set_timesteps(5				)
													assert torch.equal(scheduler.timesteps    ,				torch.LongTensor([801, 601, 401, 201, 1]				)				)




							def    A__						( self				)       ->  int:
													for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]    ,				[0.002, 0.02, 0.2, 2]				):
																			self.check_over_configs(beta_start=snake_case_    ,				beta_end=snake_case_				)




							def    A__						( self				)       ->  str:
													for schedule in ["linear", "squaredcos_cap_v2"]:
																			self.check_over_configs(beta_schedule=snake_case_				)




							def    A__						( self				)       ->  Tuple:
													for prediction_type in ["epsilon", "v_prediction"]:
																			self.check_over_configs(prediction_type=snake_case_				)




							def    A__						( self				)       ->  Optional[int]:
													for clip_sample in [True, False]:
																			self.check_over_configs(clip_sample=snake_case_				)




							def    A__						( self				)       ->  Dict:
													for timestep_spacing in ["trailing", "leading"]:
																			self.check_over_configs(timestep_spacing=snake_case_				)




							def    A__						( self				)       ->  str:
													for rescale_betas_zero_snr in [True, False]:
																			self.check_over_configs(rescale_betas_zero_snr=snake_case_				)




							def    A__						( self				)       ->  Optional[int]:
													self.check_over_configs(thresholding=snake_case_				)
													for threshold in [0.5, 1.0, 2.0]:
																			for prediction_type in ["epsilon", "v_prediction"]:
																									self.check_over_configs(
																									    thresholding=snake_case_    ,				prediction_type=snake_case_    ,				sample_max_value=snake_case_    ,				)




							def    A__						( self				)       ->  Union[str, Any]:
													for t in [1, 10, 49]:
																			self.check_over_forward(time_step=snake_case_				)




							def    A__						( self				)       ->  Optional[int]:
													for t, num_inference_steps in zip([1, 10, 50]    ,				[10, 50, 500]				):
																			self.check_over_forward(time_step=snake_case_    ,				num_inference_steps=snake_case_				)




							def    A__						( self				)       ->  Optional[int]:
													for t, eta in zip([1, 10, 49]    ,				[0.0, 0.5, 1.0]				):
																			self.check_over_forward(time_step=snake_case_    ,				eta=snake_case_				)




							def    A__						( self				)       ->  Optional[int]:
													__lowerCAmelCase     =  self.scheduler_classes[0]
													__lowerCAmelCase     =  self.get_scheduler_config()
													__lowerCAmelCase     =  scheduler_class(**snake_case_				)

													assert torch.sum(torch.abs(scheduler._get_variance(0    ,				0				) - 0.0				)				) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(420    ,				400				) - 0.14_771				)				) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(980    ,				960				) - 0.32_460				)				) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(0    ,				0				) - 0.0				)				) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(487    ,				486				) - 0.00_979				)				) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(999    ,				998				) - 0.02				)				) < 1e-5




							def    A__						( self				)       ->  Optional[int]:
													__lowerCAmelCase     =  self.scheduler_classes[0]
													__lowerCAmelCase     =  self.get_scheduler_config()
													__lowerCAmelCase     =  scheduler_class(**snake_case_				)

													__lowerCAmelCase   ,     __lowerCAmelCase     =  10, 0.0
													scheduler.set_timesteps(snake_case_				)

													__lowerCAmelCase     =  self.dummy_model()
													__lowerCAmelCase     =  self.dummy_sample_deter
													__lowerCAmelCase     =  self.dummy_sample_deter + 0.1
													__lowerCAmelCase     =  self.dummy_sample_deter - 0.1

													__lowerCAmelCase     =  samplea.shape[0]
													__lowerCAmelCase     =  torch.stack([samplea, samplea, samplea]    ,				dim=0				)
													__lowerCAmelCase     =  torch.arange(snake_case_				)[0:3, None].repeat(1    ,				snake_case_				)

													__lowerCAmelCase     =  model(samples.flatten(0    ,				1				)    ,				timesteps.flatten(0    ,				1				)				)
													__lowerCAmelCase     =  scheduler.batch_step_no_noise(snake_case_    ,				timesteps.flatten(0    ,				1				)    ,				samples.flatten(0    ,				1				)    ,				snake_case_				)

													__lowerCAmelCase     =  torch.sum(torch.abs(snake_case_				)				)
													__lowerCAmelCase     =  torch.mean(torch.abs(snake_case_				)				)

													assert abs(result_sum.item() - 1_147.7_904				) < 1e-2
													assert abs(result_mean.item() - 0.4_982				) < 1e-3




							def    A__						( self				)       ->  Dict:
													__lowerCAmelCase     =  self.full_loop()

													__lowerCAmelCase     =  torch.sum(torch.abs(snake_case_				)				)
													__lowerCAmelCase     =  torch.mean(torch.abs(snake_case_				)				)

													assert abs(result_sum.item() - 172.0_067				) < 1e-2
													assert abs(result_mean.item() - 0.223_967				) < 1e-3




							def    A__						( self				)       ->  str:
													__lowerCAmelCase     =  self.full_loop(prediction_type="""v_prediction"""				)

													__lowerCAmelCase     =  torch.sum(torch.abs(snake_case_				)				)
													__lowerCAmelCase     =  torch.mean(torch.abs(snake_case_				)				)

													assert abs(result_sum.item() - 52.5_302				) < 1e-2
													assert abs(result_mean.item() - 0.0_684				) < 1e-3




							def    A__						( self				)       ->  Tuple:
													# We specify different beta, so that the first alpha is 0.99
													__lowerCAmelCase     =  self.full_loop(set_alpha_to_one=snake_case_    ,				beta_start=0.01				)
													__lowerCAmelCase     =  torch.sum(torch.abs(snake_case_				)				)
													__lowerCAmelCase     =  torch.mean(torch.abs(snake_case_				)				)

													assert abs(result_sum.item() - 149.8_295				) < 1e-2
													assert abs(result_mean.item() - 0.1_951				) < 1e-3




							def    A__						( self				)       ->  Union[str, Any]:
													# We specify different beta, so that the first alpha is 0.99
													__lowerCAmelCase     =  self.full_loop(set_alpha_to_one=snake_case_    ,				beta_start=0.01				)
													__lowerCAmelCase     =  torch.sum(torch.abs(snake_case_				)				)
													__lowerCAmelCase     =  torch.mean(torch.abs(snake_case_				)				)

													assert abs(result_sum.item() - 149.0_784				) < 1e-2
													assert abs(result_mean.item() - 0.1_941				) < 1e-3
