





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

import os
from pathlib import Path


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     List[str]:
    lowerCamelCase__    :      Tuple					 =		{
        '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__    :      Tuple					 =		{
        '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__    :      str					 =		F"""{src_lang}-{tgt_lang}"""

    lowerCamelCase__    :      Any					 =		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__    :      Dict					 =		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
_UpperCAmelCase				:       Tuple	   =				Path(__file__).resolve().parent.parent.parent
_UpperCAmelCase				:       List[Any]	   =				repo_dir / """model_cards"""

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








"""simple docstring"""





import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


__UpperCamelCase  :      Optional[Any]							      =		'''scheduler_config.json'''







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       1
					lowercase__				 =       2
					lowercase__				 =       3
					lowercase__				 =       4
					lowercase__				 =       5







@dataclass
class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       42







class 				SCREAMING_SNAKE_CASE					:






					"""simple docstring"""



					lowercase__				 =       SCHEDULER_CONFIG_NAME
					lowercase__				 =       ["dtype"]
					lowercase__				 =       []
					lowercase__				 =       True







					@classmethod
					def        __lowerCAmelCase (       cls			:				List[Any]   ,lowercase_			:				Dict[str, Any] = None   ,lowercase_			:				Optional[str] = None   ,lowercase_			:				Optional[int]=False   ,**lowercase_			:				Any   ,):
						lowerCAmelCase__				,lowerCAmelCase__	:						Dict  				=	cls.load_config(
						    pretrained_model_name_or_path=lowercase_   ,subfolder=lowercase_   ,return_unused_kwargs=lowercase_   ,**lowercase_   ,)
						lowerCAmelCase__				,lowerCAmelCase__	:						Union[str, Any]  				=	cls.from_config(lowercase_   ,return_unused_kwargs=lowercase_   ,**lowercase_		)

						if hasattr(lowercase_   ,'''create_state'''		) and getattr(lowercase_   ,'''has_state'''   ,lowercase_		):
							lowerCAmelCase__	:						List[Any]  				=	scheduler.create_state()

						if return_unused_kwargs:
							return scheduler, state, unused_kwargs

						return scheduler, state







					def        __lowerCAmelCase (       self			:				Tuple   ,lowercase_			:				Union[str, os.PathLike]   ,lowercase_			:				bool = False   ,**lowercase_			:				str		):
						self.save_config(save_directory=lowercase_   ,push_to_hub=lowercase_   ,**lowercase_		)







					@property
					def        __lowerCAmelCase (       self			:				List[str]		):
						return self._get_compatibles()







					@classmethod
					def        __lowerCAmelCase (       cls			:				List[Any]		):
						lowerCAmelCase__	:						Tuple  				=	list(set([cls.__name__] + cls._compatibles		)		)
						lowerCAmelCase__	:						Tuple  				=	importlib.import_module(__name__.split('''.'''		)[0]		)
						lowerCAmelCase__	:						Union[str, Any]  				=	[
						    getattr(lowercase_   ,lowercase_		) for c in compatible_classes_str if hasattr(lowercase_   ,lowercase_		)
						]
						return compatible_classes

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	assert len(A_ ) >= x.ndim
	return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(A_ ) - x.ndim) )						,     A_ )

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=0.999						,     A_=jnp.floataa ):

	def alpha_bar(A_ ):
		return math.cos((time_step + 0.008) / 1.008 * math.pi / 2 ) ** 2

	lowerCAmelCase__	:						Optional[Any]  				=	[]
	for i in range(A_ ):
		lowerCAmelCase__	:						str  				=	i / num_diffusion_timesteps
		lowerCAmelCase__	:						Union[str, Any]  				=	(i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar(A_ ) / alpha_bar(A_ )						,     A_ ) )
	return jnp.array(A_						,     dtype=A_ )







@flax.struct.dataclass
class 				SCREAMING_SNAKE_CASE					:






					"""simple docstring"""



					lowercase__				 =       42
					lowercase__				 =       42
					lowercase__				 =       42







					@classmethod
					def        __lowerCAmelCase (       cls			:				Union[str, Any]   ,lowercase_			:				List[Any]		):
						lowerCAmelCase__	:						Optional[int]  				=	scheduler.config

						if config.trained_betas is not None:
							lowerCAmelCase__	:						Any  				=	jnp.asarray(config.trained_betas   ,dtype=scheduler.dtype		)
						elif config.beta_schedule == "linear":
							lowerCAmelCase__	:						Union[str, Any]  				=	jnp.linspace(config.beta_start   ,config.beta_end   ,config.num_train_timesteps   ,dtype=scheduler.dtype		)
						elif config.beta_schedule == "scaled_linear":
							# this schedule is very specific to the latent diffusion model.
							lowerCAmelCase__	:						int  				=	(
							    jnp.linspace(
							        config.beta_start**0.5   ,config.beta_end**0.5   ,config.num_train_timesteps   ,dtype=scheduler.dtype		)
							    ** 2
							)
						elif config.beta_schedule == "squaredcos_cap_v2":
							# Glide cosine schedule
							lowerCAmelCase__	:						List[Any]  				=	betas_for_alpha_bar(config.num_train_timesteps   ,dtype=scheduler.dtype		)
						else:
							raise NotImplementedError(
							    F'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'		)

						lowerCAmelCase__	:						str  				=	1.0 - betas

						lowerCAmelCase__	:						Union[str, Any]  				=	jnp.cumprod(lowercase_   ,axis=0		)

						return cls(
						    alphas=lowercase_   ,betas=lowercase_   ,alphas_cumprod=lowercase_   ,)

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						Any  				=	state.alphas_cumprod

	lowerCAmelCase__	:						Optional[Any]  				=	alphas_cumprod[timesteps] ** 0.5
	lowerCAmelCase__	:						Tuple  				=	sqrt_alpha_prod.flatten()
	lowerCAmelCase__	:						str  				=	broadcast_to_shape_from_left(A_						,     original_samples.shape )

	lowerCAmelCase__	:						Optional[Any]  				=	(1 - alphas_cumprod[timesteps]) ** 0.5
	lowerCAmelCase__	:						Optional[Any]  				=	sqrt_one_minus_alpha_prod.flatten()
	lowerCAmelCase__	:						Optional[int]  				=	broadcast_to_shape_from_left(A_						,     original_samples.shape )

	return sqrt_alpha_prod, sqrt_one_minus_alpha_prod

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_ ):
	lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	get_sqrt_alpha_prod(A_						,     A_						,     A_						,     A_ )
	lowerCAmelCase__	:						str  				=	sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
	return noisy_samples





def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_ ):
	lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	get_sqrt_alpha_prod(A_						,     A_						,     A_						,     A_ )
	lowerCAmelCase__	:						Union[str, Any]  				=	sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
	return velocity




