
"""simple docstring"""

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

import os
from pathlib import Path
def 		UpperCAmelCase					(		a_,						a_,						a_,						a_			):





		'''simple docstring'''


		lowerCamelCase     :			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]
		lowerCamelCase     :			Any		=    {
		    'wmt19-de-en-6-6-base': [0, 3_8.3_7],
		    'wmt19-de-en-6-6-big': [0, 3_9.9_0],
		}
		lowerCamelCase     :			int		=    F"""{src_lang}-{tgt_lang}"""

		lowerCamelCase     :			Optional[int]		=    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=a_,						exist_ok=a_			)
		lowerCamelCase     :			Union[str, Any]		=    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
_A            = Path(__file__).resolve().parent.parent.parent
_A            = repo_dir / 'model_cards'

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













from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_        =			logging.get_logger(__name__)

lowerCAmelCase_        =			{
    """google/mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"""
}



class 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''
  a_							:				List[Any]					    =MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
  a_							:				Union[str, Any]					    ="""mobilebert"""







  def __init__(					self					:    int					, UpperCamelCase					:    int=3_05_22					, UpperCamelCase					:    Any=5_12					, UpperCamelCase					:    Any=24					, UpperCamelCase					:    List[str]=4					, UpperCamelCase					:    Any=5_12					, UpperCamelCase					:    str="relu"					, UpperCamelCase					:    Dict=0.0					, UpperCamelCase					:    int=0.1					, UpperCamelCase					:    Optional[Any]=5_12					, UpperCamelCase					:    str=2					, UpperCamelCase					:    Tuple=0.02					, UpperCamelCase					:    Union[str, Any]=1e-1_2					, UpperCamelCase					:    Optional[Any]=0					, UpperCamelCase					:    Union[str, Any]=1_28					, UpperCamelCase					:    Tuple=True					, UpperCamelCase					:    int=True					, UpperCamelCase					:    List[Any]=1_28					, UpperCamelCase					:    Dict=False					, UpperCamelCase					:    List[str]=True					, UpperCamelCase					:    Optional[Any]=4					, UpperCamelCase					:    int="no_norm"					, UpperCamelCase					:    Tuple=True					, UpperCamelCase					:    str=None					, **UpperCamelCase					:    str					, ):


    '''simple docstring'''





    super().__init__(pad_token_id=UpperCamelCase					, **UpperCamelCase							)

    _snake_case      : Union[str, Any]														=		vocab_size
    _snake_case      : Any														=		hidden_size
    _snake_case      : Tuple														=		num_hidden_layers
    _snake_case      : Any														=		num_attention_heads
    _snake_case      : Optional[Any]														=		hidden_act
    _snake_case      : List[Any]														=		intermediate_size
    _snake_case      : List[Any]														=		hidden_dropout_prob
    _snake_case      : Optional[int]														=		attention_probs_dropout_prob
    _snake_case      : int														=		max_position_embeddings
    _snake_case      : Optional[int]														=		type_vocab_size
    _snake_case      : Any														=		initializer_range
    _snake_case      : Optional[int]														=		layer_norm_eps
    _snake_case      : Tuple														=		embedding_size
    _snake_case      : int														=		trigram_input
    _snake_case      : Optional[int]														=		use_bottleneck
    _snake_case      : List[Any]														=		intra_bottleneck_size
    _snake_case      : Any														=		use_bottleneck_attention
    _snake_case      : str														=		key_query_shared_bottleneck
    _snake_case      : List[str]														=		num_feedforward_networks
    _snake_case      : int														=		normalization_type
    _snake_case      : str														=		classifier_activation

    if self.use_bottleneck:
      _snake_case      : Any														=		intra_bottleneck_size
    else:
      _snake_case      : Union[str, Any]														=		hidden_size

    _snake_case      : Dict														=		classifier_dropout




class 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''







  @property
  def UpperCamelCase_       (					self					:    List[Any]							):


    '''simple docstring'''





    if self.task == "multiple-choice":
      _snake_case      : str														=		{0: 'batch', 1: 'choice', 2: 'sequence'}
    else:
      _snake_case      : Dict														=		{0: 'batch', 1: 'sequence'}
    return OrderedDict(
        [
            ('input_ids', dynamic_axis),
            ('attention_mask', dynamic_axis),
            ('token_type_ids', dynamic_axis),
        ]							)




