





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

import os
from pathlib import Path







def    UpperCAmelCase				(  _lowerCamelCase   :	Any      , _lowerCamelCase   :	Dict      , _lowerCamelCase   :	int      , _lowerCamelCase   :	int  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					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]
						SCREAMING_SNAKE_CASE__  :					List[str] =       {
						    '''wmt19-de-en-6-6-base''': [0, 3_8.3_7],
						    '''wmt19-de-en-6-6-big''': [0, 3_9.9_0],
						}
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       f"""{src_lang}-{tgt_lang}"""

						SCREAMING_SNAKE_CASE__  :					Tuple =       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=_UpperCamelCase      , exist_ok=_UpperCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Union[str, 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
__lowercase							:int							=	Path(__file__).resolve().parent.parent.parent
__lowercase							:Tuple							=	repo_dir / 'model_cards'

for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
				__lowercase							:List[Any]							=	model_cards_dir / 'allenai' / model_name
				write_model_card(model_card_dir, src_lang="de", tgt_lang="en", model_name=model_name)




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)





@flax.struct.dataclass
class 							lowerCAmelCase_		:
		SCREAMING_SNAKE_CASE_       :			CommonSchedulerState

		# setable values
		SCREAMING_SNAKE_CASE_       :			jnp.ndarray
		SCREAMING_SNAKE_CASE_       :			jnp.ndarray
		SCREAMING_SNAKE_CASE_       :			jnp.ndarray
		SCREAMING_SNAKE_CASE_       :			Optional[int]					       =	None

		# running values
		SCREAMING_SNAKE_CASE_       :			Optional[jnp.ndarray]					       =	None







		@classmethod
		def 					a_  (    cls  :   List[Any]       ,		UpperCAmelCase_  :   CommonSchedulerState       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   jnp.ndarray					)       ->		Optional[Any]:


						'''simple docstring'''







						return cls(common=UpperCAmelCase_       ,		init_noise_sigma=UpperCAmelCase_       ,		timesteps=UpperCAmelCase_       ,		sigmas=UpperCAmelCase_					)





@dataclass
class 							lowerCAmelCase_		(		lowercase_     ):
		SCREAMING_SNAKE_CASE_       :			LMSDiscreteSchedulerState





class 							lowerCAmelCase_		(		lowercase_							,		lowercase_     ):
		SCREAMING_SNAKE_CASE_       :			Union[str, Any]					       =	[e.name for e in FlaxKarrasDiffusionSchedulers]

		SCREAMING_SNAKE_CASE_       :			jnp.dtype







		@property
		def 					a_  (    self  :   Union[str, Any]					)       ->		List[Any]:


						'''simple docstring'''







						return True







		@register_to_config
		def __init__(    self  :   Optional[Any]       ,		UpperCAmelCase_  :   int = 1000       ,		UpperCAmelCase_  :   float = 0.0_001       ,		UpperCAmelCase_  :   float = 0.02       ,		UpperCAmelCase_  :   str = "linear"       ,		UpperCAmelCase_  :   Optional[jnp.ndarray] = None       ,		UpperCAmelCase_  :   str = "epsilon"       ,		UpperCAmelCase_  :   jnp.dtype = jnp.floataa       ,		)       ->		List[str]:


						'''simple docstring'''







						_UpperCAmelCase						:  str     					=	dtype







		def 					a_  (    self  :   Tuple       ,		UpperCAmelCase_  :   Optional[CommonSchedulerState] = None					)       ->		LMSDiscreteSchedulerState:


						'''simple docstring'''







						if common is None:
										_UpperCAmelCase						:  int     					=	CommonSchedulerState.create(self					)

						_UpperCAmelCase						:  Any     					=	jnp.arange(0       ,		self.config.num_train_timesteps					).round()[::-1]
						_UpperCAmelCase						:  Any     					=	((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

						# standard deviation of the initial noise distribution
						_UpperCAmelCase						:  Dict     					=	sigmas.max()

						return LMSDiscreteSchedulerState.create(
						    common=UpperCAmelCase_       ,		init_noise_sigma=UpperCAmelCase_       ,		timesteps=UpperCAmelCase_       ,		sigmas=UpperCAmelCase_       ,		)







		def 					a_  (    self  :   Optional[Any]       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   int					)       ->		jnp.ndarray:


						'''simple docstring'''







						((_UpperCAmelCase)					, )						:  Optional[Any]     					=	jnp.where(state.timesteps == timestep       ,		size=1					)
						_UpperCAmelCase						:  List[str]     					=	step_index[0]

						_UpperCAmelCase						:  List[Any]     					=	state.sigmas[step_index]
						_UpperCAmelCase						:  Union[str, Any]     					=	sample / ((sigma**2 + 1) ** 0.5)
						return sample







		def 					a_  (    self  :   List[Any]       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   int       ,		UpperCAmelCase_  :   str       ,		UpperCAmelCase_  :   Union[str, Any]					)       ->		Union[str, Any]:


						'''simple docstring'''







						def lms_derivative(UpperCAmelCase_  :   Optional[int]					):
										_UpperCAmelCase						:  Optional[Any]     					=	1.0
										for k in range(UpperCAmelCase_					):
														if current_order == k:
																		continue
														prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
										return prod

						_UpperCAmelCase						:  Optional[Any]     					=	integrate.quad(UpperCAmelCase_       ,		state.sigmas[t]       ,		state.sigmas[t + 1]       ,		epsrel=1E-4					)[0]

						return integrated_coeff







		def 					a_  (    self  :   Dict       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   int       ,		UpperCAmelCase_  :   Tuple = ()					)       ->		LMSDiscreteSchedulerState:


						'''simple docstring'''







						_UpperCAmelCase						:  List[str]     					=	jnp.linspace(self.config.num_train_timesteps - 1       ,		0       ,		UpperCAmelCase_       ,		dtype=self.dtype					)

						_UpperCAmelCase						:  Any     					=	jnp.floor(UpperCAmelCase_					).astype(jnp.intaa					)
						_UpperCAmelCase						:  Optional[Any]     					=	jnp.ceil(UpperCAmelCase_					).astype(jnp.intaa					)

						_UpperCAmelCase						:  str     					=	jnp.mod(UpperCAmelCase_       ,		1.0					)

						_UpperCAmelCase						:  Optional[Any]     					=	((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
						_UpperCAmelCase						:  Any     					=	(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
						_UpperCAmelCase						:  Tuple     					=	jnp.concatenate([sigmas, jnp.array([0.0]       ,		dtype=self.dtype					)]					)

						_UpperCAmelCase						:  int     					=	timesteps.astype(jnp.intaa					)

						# initial running values
						_UpperCAmelCase						:  List[str]     					=	jnp.zeros((0,) + shape       ,		dtype=self.dtype					)

						return state.replace(
						    timesteps=UpperCAmelCase_       ,		sigmas=UpperCAmelCase_       ,		num_inference_steps=UpperCAmelCase_       ,		derivatives=UpperCAmelCase_       ,		)







		def 					a_  (    self  :   Tuple       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   int       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   int = 4       ,		UpperCAmelCase_  :   bool = True       ,		)       ->		Union[FlaxLMSSchedulerOutput, Tuple]:


						'''simple docstring'''







						if state.num_inference_steps is None:
										raise ValueError(
										    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''					)

						_UpperCAmelCase						:  List[Any]     					=	state.sigmas[timestep]

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
										_UpperCAmelCase						:  Optional[int]     					=	sample - sigma * model_output
						elif self.config.prediction_type == "v_prediction":
										# * c_out + input * c_skip
										_UpperCAmelCase						:  Tuple     					=	model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
						else:
										raise ValueError(
										    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''					)

						# 2. Convert to an ODE derivative
						_UpperCAmelCase						:  Tuple     					=	(sample - pred_original_sample) / sigma
						_UpperCAmelCase						:  Tuple     					=	state.replace(derivatives=jnp.append(state.derivatives       ,		UpperCAmelCase_					)					)
						if len(state.derivatives					) > order:
										_UpperCAmelCase						:  Union[str, Any]     					=	state.replace(derivatives=jnp.delete(state.derivatives       ,		0					)					)

						# 3. Compute linear multistep coefficients
						_UpperCAmelCase						:  int     					=	min(timestep + 1       ,		UpperCAmelCase_					)
						_UpperCAmelCase						:  Optional[int]     					=	[self.get_lms_coefficient(UpperCAmelCase_       ,		UpperCAmelCase_       ,		UpperCAmelCase_       ,		UpperCAmelCase_					) for curr_order in range(UpperCAmelCase_					)]

						# 4. Compute previous sample based on the derivatives path
						_UpperCAmelCase						:  int     					=	sample + sum(
						    coeff * derivative for coeff, derivative in zip(UpperCAmelCase_       ,		reversed(state.derivatives					)					)					)

						if not return_dict:
										return (prev_sample, state)

						return FlaxLMSSchedulerOutput(prev_sample=UpperCAmelCase_       ,		state=UpperCAmelCase_					)







		def 					a_  (    self  :   Union[str, Any]       ,		UpperCAmelCase_  :   LMSDiscreteSchedulerState       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   jnp.ndarray       ,		UpperCAmelCase_  :   jnp.ndarray       ,		)       ->		jnp.ndarray:


						'''simple docstring'''







						_UpperCAmelCase						:  Union[str, Any]     					=	state.sigmas[timesteps].flatten()
						_UpperCAmelCase						:  Union[str, Any]     					=	broadcast_to_shape_from_left(UpperCAmelCase_       ,		noise.shape					)

						_UpperCAmelCase						:  Any     					=	original_samples + noise * sigma

						return noisy_samples







		def __len__(    self  :   Dict					)       ->		Tuple:


						'''simple docstring'''







						return self.config.num_train_timesteps
