


'''simple docstring'''
# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path




def 	_A  (       A							,A							,A							,A							)  ->				List[Any]:
	lowercase						:		Optional[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]
	lowercase						:		Optional[int]   					=		{
	    "wmt16-en-de-dist-12-1": [28.3, 27.52],
	    "wmt16-en-de-dist-6-1": [27.4, 27.11],
	    "wmt16-en-de-12-1": [26.9, 25.75],
	}
	lowercase						:		Dict   					=		F'''{src_lang}-{tgt_lang}'''

	lowercase						:		Union[str, Any]   					=		F'''
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt16
- allenai
license: apache-2.0
datasets:
- wmt16
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt16 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).

All 3 models are available:

* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)


## 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   | fairseq | transformers
-------|---------|----------
{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}

The score is slightly below the score reported in the paper, as the researchers don\'t use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.

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 wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt16 -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/wmt16/)
- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)


### 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=A							,exist_ok=A							)
	lowercase						:		str   					=		os.path.join(A							,"README.md"							)
	print(F'''Generating {path}'''							)
	with open(A							,"w"							,encoding="utf-8"							) as f:
		f.write(A							)


# make sure we are under the root of the project
lowerCAmelCase   : Union[str, Any]             =				Path(__file__).resolve().parent.parent.parent
lowerCAmelCase   : Union[str, Any]             =				repo_dir / """model_cards"""

for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
					lowerCAmelCase   : Optional[Any]             =				model_cards_dir / """allenai""" / model_name
					write_model_card(model_card_dir, src_lang="""en""", tgt_lang="""de""", model_name=model_name)






'''simple docstring'''
import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
					import jax.numpy as jnp

					from transformers.models.distilbert.modeling_flax_distilbert import (
					    FlaxDistilBertForMaskedLM,
					    FlaxDistilBertForMultipleChoice,
					    FlaxDistilBertForQuestionAnswering,
					    FlaxDistilBertForSequenceClassification,
					    FlaxDistilBertForTokenClassification,
					    FlaxDistilBertModel,
					)




class        _UpperCamelCase	(    unittest.TestCase):


				'''simple docstring'''


				def __init__(     self							,      a_							,      a_=1_3							,      a_=7							,      a_=True							,      a_=True							,      a_=True							,      a_=True							,      a_=9_9							,      a_=3_2							,      a_=5							,      a_=4							,      a_=3_7							,      a_="gelu"							,      a_=0.1							,      a_=0.1							,      a_=5_1_2							,      a_=1_6							,      a_=2							,      a_=0.02							,      a_=4							,      )    ->  Tuple:
					lowercase						:		Optional[Any]   					=		parent
					lowercase						:		int   					=		batch_size
					lowercase						:		int   					=		seq_length
					lowercase						:		List[str]   					=		is_training
					lowercase						:		str   					=		use_attention_mask
					lowercase						:		List[str]   					=		use_token_type_ids
					lowercase						:		Optional[Any]   					=		use_labels
					lowercase						:		Dict   					=		vocab_size
					lowercase						:		Union[str, Any]   					=		hidden_size
					lowercase						:		int   					=		num_hidden_layers
					lowercase						:		Optional[Any]   					=		num_attention_heads
					lowercase						:		Optional[Any]   					=		intermediate_size
					lowercase						:		int   					=		hidden_act
					lowercase						:		Dict   					=		hidden_dropout_prob
					lowercase						:		Optional[Any]   					=		attention_probs_dropout_prob
					lowercase						:		Dict   					=		max_position_embeddings
					lowercase						:		Tuple   					=		type_vocab_size
					lowercase						:		int   					=		type_sequence_label_size
					lowercase						:		Optional[Any]   					=		initializer_range
					lowercase						:		Union[str, Any]   					=		num_choices

				def 	a__		(     self							)    ->  Optional[int]:
					lowercase						:		List[Any]   					=		ids_tensor([self.batch_size, self.seq_length]							,      self.vocab_size							)

					lowercase						:		Any   					=		None
					if self.use_attention_mask:
						lowercase						:		str   					=		random_attention_mask([self.batch_size, self.seq_length]							)

					lowercase						:		Optional[Any]   					=		DistilBertConfig(
					    vocab_size=self.vocab_size							,      dim=self.hidden_size							,      n_layers=self.num_hidden_layers							,      n_heads=self.num_attention_heads							,      hidden_dim=self.intermediate_size							,      hidden_act=self.hidden_act							,      dropout=self.hidden_dropout_prob							,      attention_dropout=self.attention_probs_dropout_prob							,      max_position_embeddings=self.max_position_embeddings							,      initializer_range=self.initializer_range							,      tie_weights_=a_							,      )

					return config, input_ids, attention_mask


				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		Dict   					=		self.prepare_config_and_inputs()
					lowercase					,		lowercase					,		lowercase						:		Dict   					=		config_and_inputs
					lowercase						:		Any   					=		{"input_ids": input_ids, "attention_mask": attention_mask}
					return config, inputs_dict




@require_flax
class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE						,			unittest.TestCase):


				'''simple docstring'''


				_snake_case						       =				(
				    (
				        FlaxDistilBertModel,
				        FlaxDistilBertForMaskedLM,
				        FlaxDistilBertForMultipleChoice,
				        FlaxDistilBertForQuestionAnswering,
				        FlaxDistilBertForSequenceClassification,
				        FlaxDistilBertForTokenClassification,
				        FlaxDistilBertForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)

				def 	a__		(     self							)    ->  Dict:
					lowercase						:		Optional[int]   					=		FlaxDistilBertModelTester(self							)


				@slow
				def 	a__		(     self							)    ->  Union[str, Any]:
					for model_class_name in self.all_model_classes:
						lowercase						:		int   					=		model_class_name.from_pretrained("distilbert-base-uncased"							)
						lowercase						:		Tuple   					=		model(np.ones((1, 1)							)							)
						self.assertIsNotNone(a_							)




@require_flax
class        _UpperCamelCase	(    unittest.TestCase):


				'''simple docstring'''


				@slow
				def 	a__		(     self							)    ->  Union[str, Any]:
					lowercase						:		str   					=		FlaxDistilBertModel.from_pretrained("distilbert-base-uncased"							)
					lowercase						:		int   					=		np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]							)
					lowercase						:		int   					=		np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
					lowercase						:		List[str]   					=		model(a_							,      attention_mask=a_							)[0]
					lowercase						:		int   					=		(1, 1_1, 7_6_8)
					self.assertEqual(output.shape							,      a_							)
					lowercase						:		List[str]   					=		np.array([[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]]							)

					self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]							,      a_							,      atol=1e-4							)							)


